From 9b76d2332517b8087ceae9bec68505ba12e66fca Mon Sep 17 00:00:00 2001
From: Maziyar Panahi <maziyar.panahi@iscpif.fr>
Date: Fri, 26 May 2023 08:13:04 +0200
Subject: [PATCH] Models hub (#13830)

* Add model 2023-04-13-CyberbullyingDetection_ClassifierDL_tfhub_en (#13757)

Co-authored-by: Naveen-004 <chinna.nk4@gmail.com>

* 2023-04-20-distilbert_base_uncased_mnli_en (#13761)

* Add model 2023-04-20-distilbert_base_uncased_mnli_en

* Add model 2023-04-20-distilbert_base_turkish_cased_allnli_tr

* Add model 2023-04-20-distilbert_base_turkish_cased_snli_tr

* Add model 2023-04-20-distilbert_base_turkish_cased_multinli_tr

* Update and rename 2023-04-20-distilbert_base_turkish_cased_allnli_tr.md to 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_allnli_tr.md

* Update and rename 2023-04-20-distilbert_base_turkish_cased_multinli_tr.md to 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr.md

* Update and rename 2023-04-20-distilbert_base_turkish_cased_snli_tr.md to 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_snli_tr.md

* Update and rename 2023-04-20-distilbert_base_uncased_mnli_en.md to distilbert_base_zero_shot_classifier_turkish_cased_snli

* Rename distilbert_base_zero_shot_classifier_turkish_cased_snli to distilbert_base_zero_shot_classifier_turkish_cased_snli_en.md

* Update 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_snli_tr.md

* Update 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr.md

* Update 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_allnli_tr.md

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Co-authored-by: ahmedlone127 <ahmedlone127@gmail.com>

* 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr (#13763)

* Add model 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr

* Add model 2023-04-20-distilbert_base_zero_shot_classifier_uncased_mnli_en

* Add model 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_snli_tr

* Add model 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_allnli_tr

* Update 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr.md

* Update 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_snli_tr.md

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Co-authored-by: ahmedlone127 <ahmedlone127@gmail.com>

* 2023-05-04-roberta_base_zero_shot_classifier_nli_en (#13781)

* Add model 2023-05-04-roberta_base_zero_shot_classifier_nli_en

* Fix Spark version to 3.0

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Co-authored-by: ahmedlone127 <ahmedlone127@gmail.com>
Co-authored-by: Maziyar Panahi <maziyar.panahi@iscpif.fr>

* 2023-05-09-distilbart_xsum_6_6_en (#13788)

* Add model 2023-05-09-distilbart_xsum_6_6_en

* Add model 2023-05-09-distilbart_xsum_12_6_en

* Add model 2023-05-09-distilbart_cnn_12_6_en

* Add model 2023-05-09-distilbart_cnn_6_6_en

* Add model 2023-05-09-bart_large_cnn_en

* Update 2023-05-09-bart_large_cnn_en.md

* Update 2023-05-09-distilbart_cnn_12_6_en.md

* Update 2023-05-09-distilbart_cnn_6_6_en.md

* Update 2023-05-09-distilbart_xsum_12_6_en.md

* Update 2023-05-09-distilbart_xsum_6_6_en.md

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Co-authored-by: prabod <prabod@rathnayaka.me>
Co-authored-by: Maziyar Panahi <maziyar.panahi@iscpif.fr>

* 2023-05-11-distilbart_cnn_12_6_en (#13795)

* Add model 2023-05-11-distilbart_cnn_12_6_en

* Add model 2023-05-11-distilbart_cnn_6_6_en

* Add model 2023-05-11-distilbart_xsum_12_6_en

* Add model 2023-05-11-distilbart_xsum_6_6_en

* Add model 2023-05-11-bart_large_cnn_en

* Update 2023-05-11-bart_large_cnn_en.md

* Update 2023-05-11-distilbart_cnn_12_6_en.md

* Update 2023-05-11-distilbart_cnn_6_6_en.md

* Update 2023-05-11-distilbart_xsum_12_6_en.md

* Update 2023-05-11-distilbart_xsum_6_6_en.md

---------

Co-authored-by: prabod <prabod@rathnayaka.me>
Co-authored-by: Maziyar Panahi <maziyar.panahi@iscpif.fr>

* 2023-05-19-match_pattern_en (#13805)

* Add model 2023-05-19-match_pattern_en

* Add model 2023-05-19-dependency_parse_en

* Add model 2023-05-20-explain_document_md_fr

* Add model 2023-05-20-dependency_parse_en

* Add model 2023-05-20-explain_document_md_it

* Add model 2023-05-20-entity_recognizer_lg_fr

* Add model 2023-05-20-entity_recognizer_md_fr

* Add model 2023-05-20-entity_recognizer_lg_it

* Add model 2023-05-20-entity_recognizer_md_it

* Add model 2023-05-20-check_spelling_en

* Add model 2023-05-20-match_datetime_en

* Add model 2023-05-20-match_pattern_en

* Add model 2023-05-20-clean_pattern_en

* Add model 2023-05-20-clean_stop_en

* Add model 2023-05-20-movies_sentiment_analysis_en

* Add model 2023-05-20-explain_document_ml_en

* Add model 2023-05-20-analyze_sentiment_en

* Add model 2023-05-20-explain_document_dl_en

* Add model 2023-05-20-recognize_entities_dl_en

* Add model 2023-05-20-recognize_entities_bert_en

* Add model 2023-05-20-explain_document_md_de

* Add model 2023-05-21-entity_recognizer_lg_de

* Add model 2023-05-21-entity_recognizer_md_de

* Add model 2023-05-21-onto_recognize_entities_sm_en

* Add model 2023-05-21-onto_recognize_entities_lg_en

* Add model 2023-05-21-match_chunks_en

* Add model 2023-05-21-explain_document_lg_es

* Add model 2023-05-21-explain_document_md_es

* Add model 2023-05-21-explain_document_sm_es

* Add model 2023-05-21-entity_recognizer_lg_es

* Add model 2023-05-21-entity_recognizer_md_es

* Add model 2023-05-21-entity_recognizer_sm_es

* Add model 2023-05-21-explain_document_lg_ru

* Add model 2023-05-21-explain_document_md_ru

* Add model 2023-05-21-explain_document_sm_ru

* Add model 2023-05-21-entity_recognizer_lg_ru

* Add model 2023-05-21-entity_recognizer_md_ru

* Add model 2023-05-21-entity_recognizer_sm_ru

* Add model 2023-05-21-text_cleaning_en

* Add model 2023-05-21-explain_document_lg_pt

* Add model 2023-05-21-explain_document_md_pt

* Add model 2023-05-21-explain_document_sm_pt

* Add model 2023-05-21-entity_recognizer_lg_pt

* Add model 2023-05-21-entity_recognizer_md_pt

* Add model 2023-05-21-entity_recognizer_sm_pt

* Add model 2023-05-21-explain_document_lg_pl

* Add model 2023-05-21-explain_document_md_pl

* Add model 2023-05-21-explain_document_sm_pl

* Add model 2023-05-21-entity_recognizer_lg_pl

* Add model 2023-05-21-entity_recognizer_md_pl

* Add model 2023-05-21-entity_recognizer_sm_pl

* Add model 2023-05-21-explain_document_lg_nl

* Add model 2023-05-21-explain_document_md_nl

* Add model 2023-05-21-explain_document_sm_nl

* Add model 2023-05-21-entity_recognizer_lg_nl

* Add model 2023-05-21-entity_recognizer_md_nl

* Add model 2023-05-21-entity_recognizer_sm_nl

* Add model 2023-05-21-analyze_sentimentdl_glove_imdb_en

* Add model 2023-05-21-explain_document_lg_no

* Add model 2023-05-21-explain_document_md_no

* Add model 2023-05-21-explain_document_sm_no

* Add model 2023-05-21-entity_recognizer_lg_no

* Add model 2023-05-21-entity_recognizer_md_no

* Add model 2023-05-21-entity_recognizer_sm_no

* Add model 2023-05-21-explain_document_lg_sv

* Add model 2023-05-21-explain_document_md_sv

* Add model 2023-05-21-explain_document_sm_sv

* Add model 2023-05-21-entity_recognizer_lg_sv

* Add model 2023-05-21-entity_recognizer_md_sv

* Add model 2023-05-21-entity_recognizer_sm_sv

* Add model 2023-05-21-explain_document_lg_da

* Add model 2023-05-21-explain_document_md_da

* Add model 2023-05-21-explain_document_sm_da

* Add model 2023-05-21-entity_recognizer_lg_da

* Add model 2023-05-21-entity_recognizer_md_da

* Add model 2023-05-21-entity_recognizer_sm_da

* Add model 2023-05-21-explain_document_lg_fi

* Add model 2023-05-21-explain_document_md_fi

* Add model 2023-05-21-explain_document_sm_fi

* Add model 2023-05-21-entity_recognizer_lg_fi

* Add model 2023-05-21-entity_recognizer_md_fi

* Add model 2023-05-21-entity_recognizer_sm_fi

* Add model 2023-05-21-onto_recognize_entities_bert_base_en

* Add model 2023-05-21-onto_recognize_entities_bert_large_en

* Add model 2023-05-21-onto_recognize_entities_bert_medium_en

* Add model 2023-05-21-onto_recognize_entities_bert_mini_en

* Add model 2023-05-21-onto_recognize_entities_bert_small_en

* Add model 2023-05-21-onto_recognize_entities_bert_tiny_en

* Add model 2023-05-21-onto_recognize_entities_electra_base_en

* Add model 2023-05-21-onto_recognize_entities_electra_small_en

* Add model 2023-05-21-onto_recognize_entities_electra_large_en

* Add model 2023-05-21-recognize_entities_dl_fa

* Add model 2023-05-21-nerdl_fewnerd_subentity_100d_pipeline_en

* Add model 2023-05-21-nerdl_fewnerd_100d_pipeline_en

* Add model 2023-05-21-pos_ud_bokmaal_nb

* Add model 2023-05-21-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx

* Add model 2023-05-21-bert_token_classifier_scandi_ner_pipeline_xx

* Add model 2023-05-21-bert_sequence_classifier_trec_coarse_pipeline_en

* Add model 2023-05-21-bert_sequence_classifier_age_news_pipeline_en

* Add model 2023-05-21-distilbert_token_classifier_typo_detector_pipeline_is

* Add model 2023-05-21-distilbert_base_token_classifier_masakhaner_pipeline_xx

* Add model 2023-05-21-nerdl_restaurant_100d_pipeline_en

* Add model 2023-05-21-roberta_token_classifier_timex_semeval_pipeline_en

* Add model 2023-05-21-bert_token_classifier_hi_en_ner_pipeline_hi

* Add model 2023-05-21-xlm_roberta_large_token_classifier_hrl_pipeline_xx

* Add model 2023-05-21-spellcheck_dl_pipeline_en

* Add model 2023-05-21-bert_token_classifier_dutch_udlassy_ner_pipeline_nl

* Add model 2023-05-21-xlm_roberta_large_token_classifier_conll03_pipeline_de

* Add model 2023-05-21-roberta_token_classifier_bne_capitel_ner_pipeline_es

* Add model 2023-05-21-roberta_token_classifier_icelandic_ner_pipeline_is

* Add model 2023-05-21-longformer_base_token_classifier_conll03_pipeline_en

* Add model 2023-05-21-longformer_large_token_classifier_conll03_pipeline_en

* Add model 2023-05-21-xlnet_base_token_classifier_conll03_pipeline_en

* Add model 2023-05-21-xlm_roberta_base_token_classifier_ontonotes_pipeline_en

* Add model 2023-05-21-xlm_roberta_base_token_classifier_conll03_pipeline_en

* Add model 2023-05-21-xlnet_large_token_classifier_conll03_pipeline_en

* Add model 2023-05-21-albert_base_token_classifier_conll03_pipeline_en

* Add model 2023-05-21-albert_large_token_classifier_conll03_pipeline_en

* Add model 2023-05-21-albert_xlarge_token_classifier_conll03_pipeline_en

* Add model 2023-05-21-distilroberta_base_token_classifier_ontonotes_pipeline_en

* Add model 2023-05-21-roberta_base_token_classifier_ontonotes_pipeline_en

* Add model 2023-05-21-roberta_large_token_classifier_conll03_pipeline_en

* Add model 2023-05-21-distilbert_token_classifier_typo_detector_pipeline_en

---------

Co-authored-by: ahmedlone127 <ahmedlone127@gmail.com>

* 2023-05-22-explain_document_md_fr (#13811)

* Add model 2023-05-22-explain_document_md_fr

* Add model 2023-05-22-dependency_parse_en

* Add model 2023-05-22-explain_document_md_it

* Add model 2023-05-22-entity_recognizer_lg_fr

* Add model 2023-05-22-entity_recognizer_md_fr

* Add model 2023-05-22-entity_recognizer_lg_it

* Add model 2023-05-22-entity_recognizer_md_it

* Add model 2023-05-22-check_spelling_en

* Add model 2023-05-22-match_datetime_en

* Add model 2023-05-22-match_pattern_en

* Add model 2023-05-22-clean_pattern_en

* Add model 2023-05-22-clean_stop_en

* Add model 2023-05-22-movies_sentiment_analysis_en

* Add model 2023-05-22-explain_document_ml_en

* Add model 2023-05-22-analyze_sentiment_en

* Add model 2023-05-22-explain_document_dl_en

* Add model 2023-05-22-recognize_entities_dl_en

* Add model 2023-05-22-recognize_entities_bert_en

* Add model 2023-05-22-explain_document_md_de

* Add model 2023-05-22-entity_recognizer_lg_de

* Add model 2023-05-22-entity_recognizer_md_de

* Add model 2023-05-22-onto_recognize_entities_sm_en

* Add model 2023-05-22-onto_recognize_entities_lg_en

* Add model 2023-05-22-match_chunks_en

* Add model 2023-05-22-explain_document_lg_es

* Add model 2023-05-22-explain_document_md_es

* Add model 2023-05-22-explain_document_sm_es

* Add model 2023-05-22-entity_recognizer_lg_es

* Add model 2023-05-22-entity_recognizer_md_es

* Add model 2023-05-22-entity_recognizer_sm_es

* Add model 2023-05-22-explain_document_lg_ru

* Add model 2023-05-22-explain_document_md_ru

* Add model 2023-05-22-explain_document_sm_ru

* Add model 2023-05-22-entity_recognizer_lg_ru

* Add model 2023-05-22-entity_recognizer_md_ru

* Add model 2023-05-22-entity_recognizer_sm_ru

* Add model 2023-05-22-text_cleaning_en

* Add model 2023-05-22-explain_document_lg_pt

* Add model 2023-05-22-explain_document_md_pt

* Add model 2023-05-22-explain_document_sm_pt

* Add model 2023-05-22-entity_recognizer_lg_pt

* Add model 2023-05-22-entity_recognizer_md_pt

* Add model 2023-05-22-entity_recognizer_sm_pt

* Add model 2023-05-22-explain_document_lg_pl

* Add model 2023-05-22-explain_document_md_pl

* Add model 2023-05-22-explain_document_sm_pl

* Add model 2023-05-22-entity_recognizer_lg_pl

* Add model 2023-05-22-entity_recognizer_md_pl

* Add model 2023-05-22-entity_recognizer_sm_pl

* Add model 2023-05-22-explain_document_lg_nl

* Add model 2023-05-22-explain_document_md_nl

* Add model 2023-05-22-explain_document_sm_nl

* Add model 2023-05-22-entity_recognizer_lg_nl

* Add model 2023-05-22-entity_recognizer_md_nl

* Add model 2023-05-22-entity_recognizer_sm_nl

* Add model 2023-05-22-analyze_sentimentdl_glove_imdb_en

* Add model 2023-05-22-explain_document_lg_no

* Add model 2023-05-22-explain_document_md_no

* Add model 2023-05-22-explain_document_sm_no

* Add model 2023-05-22-entity_recognizer_md_no

* Add model 2023-05-22-entity_recognizer_sm_no

* Add model 2023-05-22-explain_document_lg_sv

* Add model 2023-05-22-explain_document_md_sv

* Add model 2023-05-22-explain_document_sm_sv

* Add model 2023-05-22-entity_recognizer_lg_sv

* Add model 2023-05-22-entity_recognizer_md_sv

* Add model 2023-05-22-entity_recognizer_sm_sv

* Add model 2023-05-22-explain_document_lg_da

* Add model 2023-05-22-explain_document_md_da

* Add model 2023-05-22-explain_document_sm_da

* Add model 2023-05-22-entity_recognizer_lg_da

* Add model 2023-05-22-entity_recognizer_md_da

* Add model 2023-05-22-entity_recognizer_sm_da

* Add model 2023-05-22-explain_document_lg_fi

* Add model 2023-05-22-explain_document_md_fi

* Add model 2023-05-22-explain_document_sm_fi

* Add model 2023-05-22-entity_recognizer_lg_fi

* Add model 2023-05-22-entity_recognizer_md_fi

* Add model 2023-05-22-entity_recognizer_sm_fi

* Add model 2023-05-22-onto_recognize_entities_bert_base_en

* Add model 2023-05-22-onto_recognize_entities_bert_large_en

* Add model 2023-05-22-onto_recognize_entities_bert_medium_en

* Add model 2023-05-22-onto_recognize_entities_bert_mini_en

* Add model 2023-05-22-onto_recognize_entities_bert_small_en

* Add model 2023-05-22-onto_recognize_entities_bert_tiny_en

* Add model 2023-05-22-onto_recognize_entities_electra_base_en

* Add model 2023-05-22-onto_recognize_entities_electra_small_en

* Add model 2023-05-22-onto_recognize_entities_electra_large_en

* Add model 2023-05-22-recognize_entities_dl_fa

* Add model 2023-05-22-nerdl_fewnerd_subentity_100d_pipeline_en

* Add model 2023-05-22-nerdl_fewnerd_100d_pipeline_en

* Add model 2023-05-22-pos_ud_bokmaal_nb

* Add model 2023-05-22-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx

* Add model 2023-05-22-bert_token_classifier_scandi_ner_pipeline_xx

* Add model 2023-05-22-bert_sequence_classifier_trec_coarse_pipeline_en

* Add model 2023-05-22-bert_sequence_classifier_age_news_pipeline_en

* Add model 2023-05-22-distilbert_token_classifier_typo_detector_pipeline_is

* Add model 2023-05-22-distilbert_base_token_classifier_masakhaner_pipeline_xx

* Add model 2023-05-22-nerdl_restaurant_100d_pipeline_en

* Add model 2023-05-22-roberta_token_classifier_timex_semeval_pipeline_en

* Add model 2023-05-22-bert_token_classifier_hi_en_ner_pipeline_hi

* Add model 2023-05-22-xlm_roberta_large_token_classifier_hrl_pipeline_xx

* Add model 2023-05-22-spellcheck_dl_pipeline_en

* Add model 2023-05-22-bert_token_classifier_dutch_udlassy_ner_pipeline_nl

* Add model 2023-05-22-xlm_roberta_large_token_classifier_conll03_pipeline_de

* Add model 2023-05-22-roberta_token_classifier_bne_capitel_ner_pipeline_es

* Add model 2023-05-22-roberta_token_classifier_icelandic_ner_pipeline_is

* Add model 2023-05-22-longformer_base_token_classifier_conll03_pipeline_en

* Add model 2023-05-22-longformer_large_token_classifier_conll03_pipeline_en

* Add model 2023-05-22-xlnet_base_token_classifier_conll03_pipeline_en

* Add model 2023-05-22-xlm_roberta_base_token_classifier_ontonotes_pipeline_en

* Add model 2023-05-22-xlm_roberta_base_token_classifier_conll03_pipeline_en

* Add model 2023-05-22-xlnet_large_token_classifier_conll03_pipeline_en

* Add model 2023-05-22-albert_base_token_classifier_conll03_pipeline_en

* Add model 2023-05-22-albert_large_token_classifier_conll03_pipeline_en

* Add model 2023-05-22-albert_xlarge_token_classifier_conll03_pipeline_en

* Add model 2023-05-22-distilroberta_base_token_classifier_ontonotes_pipeline_en

* Add model 2023-05-22-roberta_base_token_classifier_ontonotes_pipeline_en

* Add model 2023-05-22-roberta_large_token_classifier_conll03_pipeline_en

* Add model 2023-05-22-distilbert_token_classifier_typo_detector_pipeline_en

---------

Co-authored-by: ahmedlone127 <ahmedlone127@gmail.com>

* 2023-05-24-explain_document_md_fr (#13821)

* Add model 2023-05-24-explain_document_md_fr

* Add model 2023-05-24-dependency_parse_en

* Add model 2023-05-24-explain_document_md_it

* Add model 2023-05-24-entity_recognizer_lg_fr

* Add model 2023-05-24-entity_recognizer_md_fr

* Add model 2023-05-24-entity_recognizer_lg_it

* Add model 2023-05-24-entity_recognizer_md_it

* Add model 2023-05-24-check_spelling_en

* Add model 2023-05-24-match_datetime_en

* Add model 2023-05-24-match_pattern_en

* Add model 2023-05-24-clean_pattern_en

* Add model 2023-05-24-clean_stop_en

* Add model 2023-05-24-movies_sentiment_analysis_en

* Add model 2023-05-24-explain_document_ml_en

* Add model 2023-05-24-analyze_sentiment_en

* Add model 2023-05-24-explain_document_dl_en

* Add model 2023-05-24-recognize_entities_dl_en

* Add model 2023-05-24-recognize_entities_bert_en

* Add model 2023-05-24-explain_document_md_de

* Add model 2023-05-24-entity_recognizer_lg_de

* Add model 2023-05-24-entity_recognizer_md_de

* Add model 2023-05-24-onto_recognize_entities_sm_en

* Add model 2023-05-24-onto_recognize_entities_lg_en

* Add model 2023-05-24-match_chunks_en

* Add model 2023-05-24-explain_document_lg_es

* Add model 2023-05-24-explain_document_md_es

* Add model 2023-05-24-explain_document_sm_es

* Add model 2023-05-24-entity_recognizer_lg_es

* Add model 2023-05-24-entity_recognizer_md_es

* Add model 2023-05-24-entity_recognizer_sm_es

* Add model 2023-05-24-explain_document_lg_ru

* Add model 2023-05-24-explain_document_md_ru

* Add model 2023-05-24-explain_document_sm_ru

* Add model 2023-05-24-entity_recognizer_lg_ru

* Add model 2023-05-24-entity_recognizer_md_ru

* Add model 2023-05-24-entity_recognizer_sm_ru

* Add model 2023-05-24-text_cleaning_en

* Add model 2023-05-24-explain_document_lg_pt

* Add model 2023-05-24-explain_document_md_pt

* Add model 2023-05-24-explain_document_sm_pt

* Add model 2023-05-24-entity_recognizer_lg_pt

* Add model 2023-05-24-entity_recognizer_md_pt

* Add model 2023-05-24-entity_recognizer_sm_pt

* Add model 2023-05-24-explain_document_lg_pl

* Add model 2023-05-24-explain_document_md_pl

* Add model 2023-05-24-explain_document_sm_pl

* Add model 2023-05-24-entity_recognizer_lg_pl

* Add model 2023-05-24-entity_recognizer_md_pl

* Add model 2023-05-24-entity_recognizer_sm_pl

* Add model 2023-05-24-explain_document_lg_nl

* Add model 2023-05-24-explain_document_md_nl

* Add model 2023-05-24-explain_document_sm_nl

* Add model 2023-05-24-entity_recognizer_lg_nl

* Add model 2023-05-24-entity_recognizer_md_nl

* Add model 2023-05-24-entity_recognizer_sm_nl

* Add model 2023-05-24-analyze_sentimentdl_glove_imdb_en

* Add model 2023-05-24-explain_document_lg_no

* Add model 2023-05-24-explain_document_md_no

* Add model 2023-05-24-explain_document_sm_no

* Add model 2023-05-24-entity_recognizer_lg_no

* Add model 2023-05-24-entity_recognizer_md_no

* Add model 2023-05-24-entity_recognizer_sm_no

* Add model 2023-05-24-explain_document_lg_sv

* Add model 2023-05-24-explain_document_md_sv

* Add model 2023-05-24-explain_document_sm_sv

* Add model 2023-05-24-entity_recognizer_lg_sv

* Add model 2023-05-24-entity_recognizer_md_sv

* Add model 2023-05-24-entity_recognizer_sm_sv

* Add model 2023-05-25-explain_document_lg_da

* Add model 2023-05-25-explain_document_md_da

* Add model 2023-05-25-explain_document_sm_da

* Add model 2023-05-25-entity_recognizer_lg_da

* Add model 2023-05-25-entity_recognizer_md_da

* Add model 2023-05-25-entity_recognizer_sm_da

* Add model 2023-05-25-explain_document_lg_fi

* Add model 2023-05-25-explain_document_md_fi

* Add model 2023-05-25-explain_document_sm_fi

* Add model 2023-05-25-entity_recognizer_lg_fi

* Add model 2023-05-25-entity_recognizer_md_fi

* Add model 2023-05-25-entity_recognizer_sm_fi

* Add model 2023-05-25-onto_recognize_entities_bert_base_en

* Add model 2023-05-25-onto_recognize_entities_bert_large_en

* Add model 2023-05-25-onto_recognize_entities_bert_medium_en

* Add model 2023-05-25-onto_recognize_entities_bert_mini_en

* Add model 2023-05-25-onto_recognize_entities_bert_small_en

* Add model 2023-05-25-onto_recognize_entities_bert_tiny_en

* Add model 2023-05-25-onto_recognize_entities_electra_base_en

* Add model 2023-05-25-onto_recognize_entities_electra_small_en

* Add model 2023-05-25-onto_recognize_entities_electra_large_en

* Add model 2023-05-25-recognize_entities_dl_fa

* Add model 2023-05-25-nerdl_fewnerd_subentity_100d_pipeline_en

* Add model 2023-05-25-nerdl_fewnerd_100d_pipeline_en

* Add model 2023-05-25-pos_ud_bokmaal_nb

* Add model 2023-05-25-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx

* Add model 2023-05-25-bert_token_classifier_scandi_ner_pipeline_xx

* Add model 2023-05-25-bert_sequence_classifier_trec_coarse_pipeline_en

* Add model 2023-05-25-bert_sequence_classifier_age_news_pipeline_en

* Add model 2023-05-25-distilbert_token_classifier_typo_detector_pipeline_is

* Add model 2023-05-25-distilbert_base_token_classifier_masakhaner_pipeline_xx

* Add model 2023-05-25-nerdl_restaurant_100d_pipeline_en

* Add model 2023-05-25-roberta_token_classifier_timex_semeval_pipeline_en

* Add model 2023-05-25-bert_token_classifier_hi_en_ner_pipeline_hi

* Add model 2023-05-25-xlm_roberta_large_token_classifier_hrl_pipeline_xx

* Add model 2023-05-25-spellcheck_dl_pipeline_en

* Add model 2023-05-25-bert_token_classifier_dutch_udlassy_ner_pipeline_nl

* Add model 2023-05-25-xlm_roberta_large_token_classifier_conll03_pipeline_de

* Add model 2023-05-25-roberta_token_classifier_bne_capitel_ner_pipeline_es

* Add model 2023-05-25-roberta_token_classifier_icelandic_ner_pipeline_is

* Add model 2023-05-25-longformer_base_token_classifier_conll03_pipeline_en

* Add model 2023-05-25-longformer_large_token_classifier_conll03_pipeline_en

* Add model 2023-05-25-xlnet_base_token_classifier_conll03_pipeline_en

* Add model 2023-05-25-xlm_roberta_base_token_classifier_ontonotes_pipeline_en

* Add model 2023-05-25-xlm_roberta_base_token_classifier_conll03_pipeline_en

* Add model 2023-05-25-xlnet_large_token_classifier_conll03_pipeline_en

* Add model 2023-05-25-albert_base_token_classifier_conll03_pipeline_en

* Add model 2023-05-25-albert_large_token_classifier_conll03_pipeline_en

* Add model 2023-05-25-albert_xlarge_token_classifier_conll03_pipeline_en

* Add model 2023-05-25-distilroberta_base_token_classifier_ontonotes_pipeline_en

* Add model 2023-05-25-roberta_base_token_classifier_ontonotes_pipeline_en

* Add model 2023-05-25-roberta_large_token_classifier_conll03_pipeline_en

* Add model 2023-05-25-distilbert_token_classifier_typo_detector_pipeline_en

---------

Co-authored-by: ahmedlone127 <ahmedlone127@gmail.com>

* Add model 2023-05-25-explain_document_md_fr (#13827)

Co-authored-by: ahmedlone127 <ahmedlone127@gmail.com>

* 2023-05-25-dependency_parse_en (#13828)

* Add model 2023-05-25-dependency_parse_en

* Add model 2023-05-25-explain_document_md_it

* Add model 2023-05-25-entity_recognizer_lg_fr

* Add model 2023-05-25-entity_recognizer_md_fr

* Add model 2023-05-25-entity_recognizer_lg_it

* Add model 2023-05-25-entity_recognizer_md_it

* Add model 2023-05-25-check_spelling_en

* Add model 2023-05-25-match_datetime_en

* Add model 2023-05-25-match_pattern_en

* Add model 2023-05-25-clean_pattern_en

* Add model 2023-05-25-clean_stop_en

* Add model 2023-05-25-movies_sentiment_analysis_en

* Add model 2023-05-25-explain_document_ml_en

* Add model 2023-05-25-analyze_sentiment_en

* Add model 2023-05-25-explain_document_dl_en

* Add model 2023-05-25-recognize_entities_dl_en

* Add model 2023-05-25-recognize_entities_bert_en

* Add model 2023-05-25-explain_document_md_de

* Add model 2023-05-25-entity_recognizer_lg_de

* Add model 2023-05-25-entity_recognizer_md_de

* Add model 2023-05-25-onto_recognize_entities_sm_en

* Add model 2023-05-25-onto_recognize_entities_lg_en

* Add model 2023-05-25-match_chunks_en

* Add model 2023-05-25-explain_document_lg_es

* Add model 2023-05-25-explain_document_md_es

* Add model 2023-05-25-explain_document_sm_es

* Add model 2023-05-25-entity_recognizer_lg_es

* Add model 2023-05-25-entity_recognizer_md_es

* Add model 2023-05-25-entity_recognizer_sm_es

* Add model 2023-05-25-explain_document_lg_ru

* Add model 2023-05-25-explain_document_md_ru

* Add model 2023-05-25-explain_document_sm_ru

* Add model 2023-05-25-entity_recognizer_lg_ru

* Add model 2023-05-25-entity_recognizer_md_ru

* Add model 2023-05-25-entity_recognizer_sm_ru

* Add model 2023-05-25-text_cleaning_en

* Add model 2023-05-25-explain_document_lg_pt

* Add model 2023-05-25-explain_document_md_pt

* Add model 2023-05-25-explain_document_sm_pt

* Add model 2023-05-25-entity_recognizer_lg_pt

* Add model 2023-05-25-entity_recognizer_md_pt

* Add model 2023-05-25-entity_recognizer_sm_pt

* Add model 2023-05-25-explain_document_lg_pl

* Add model 2023-05-25-explain_document_md_pl

* Add model 2023-05-25-explain_document_sm_pl

* Add model 2023-05-25-entity_recognizer_lg_pl

* Add model 2023-05-25-entity_recognizer_md_pl

* Add model 2023-05-25-entity_recognizer_sm_pl

* Add model 2023-05-25-explain_document_lg_nl

* Add model 2023-05-25-explain_document_md_nl

* Add model 2023-05-25-explain_document_sm_nl

* Add model 2023-05-25-entity_recognizer_lg_nl

* Add model 2023-05-25-entity_recognizer_md_nl

* Add model 2023-05-25-entity_recognizer_sm_nl

* Add model 2023-05-25-analyze_sentimentdl_glove_imdb_en

* Add model 2023-05-25-explain_document_lg_no

* Add model 2023-05-25-explain_document_md_no

* Add model 2023-05-25-explain_document_sm_no

* Add model 2023-05-25-entity_recognizer_lg_no

* Add model 2023-05-25-entity_recognizer_md_no

* Add model 2023-05-25-entity_recognizer_sm_no

* Add model 2023-05-25-explain_document_lg_sv

* Add model 2023-05-25-explain_document_md_sv

* Add model 2023-05-25-explain_document_sm_sv

* Add model 2023-05-25-entity_recognizer_lg_sv

* Add model 2023-05-25-entity_recognizer_md_sv

* Add model 2023-05-25-entity_recognizer_sm_sv

* Add model 2023-05-25-explain_document_lg_da

* Add model 2023-05-25-explain_document_md_da

* Add model 2023-05-25-explain_document_sm_da

* Add model 2023-05-25-entity_recognizer_lg_da

* Add model 2023-05-25-entity_recognizer_md_da

* Add model 2023-05-25-entity_recognizer_sm_da

* Add model 2023-05-25-explain_document_lg_fi

* Add model 2023-05-25-explain_document_md_fi

* Add model 2023-05-25-explain_document_sm_fi

* Add model 2023-05-25-entity_recognizer_lg_fi

* Add model 2023-05-25-entity_recognizer_md_fi

* Add model 2023-05-25-entity_recognizer_sm_fi

* Add model 2023-05-25-onto_recognize_entities_bert_base_en

* Add model 2023-05-25-onto_recognize_entities_bert_large_en

* Add model 2023-05-25-onto_recognize_entities_bert_medium_en

* Add model 2023-05-25-onto_recognize_entities_bert_mini_en

* Add model 2023-05-25-onto_recognize_entities_bert_small_en

* Add model 2023-05-25-onto_recognize_entities_bert_tiny_en

* Add model 2023-05-25-onto_recognize_entities_electra_base_en

* Add model 2023-05-25-onto_recognize_entities_electra_small_en

* Add model 2023-05-25-onto_recognize_entities_electra_large_en

* Add model 2023-05-26-recognize_entities_dl_fa

* Add model 2023-05-26-nerdl_fewnerd_subentity_100d_pipeline_en

* Add model 2023-05-26-nerdl_fewnerd_100d_pipeline_en

* Add model 2023-05-26-pos_ud_bokmaal_nb

* Add model 2023-05-26-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx

* Add model 2023-05-26-bert_token_classifier_scandi_ner_pipeline_xx

* Add model 2023-05-26-bert_sequence_classifier_trec_coarse_pipeline_en

* Add model 2023-05-26-bert_sequence_classifier_age_news_pipeline_en

* Add model 2023-05-26-distilbert_token_classifier_typo_detector_pipeline_is

* Add model 2023-05-26-distilbert_base_token_classifier_masakhaner_pipeline_xx

* Add model 2023-05-26-nerdl_restaurant_100d_pipeline_en

* Add model 2023-05-26-roberta_token_classifier_timex_semeval_pipeline_en

* Add model 2023-05-26-bert_token_classifier_hi_en_ner_pipeline_hi

* Add model 2023-05-26-xlm_roberta_large_token_classifier_hrl_pipeline_xx

* Add model 2023-05-26-spellcheck_dl_pipeline_en

* Add model 2023-05-26-bert_token_classifier_dutch_udlassy_ner_pipeline_nl

* Add model 2023-05-26-xlm_roberta_large_token_classifier_conll03_pipeline_de

* Add model 2023-05-26-roberta_token_classifier_bne_capitel_ner_pipeline_es

* Add model 2023-05-26-roberta_token_classifier_icelandic_ner_pipeline_is

* Add model 2023-05-26-longformer_base_token_classifier_conll03_pipeline_en

* Add model 2023-05-26-longformer_large_token_classifier_conll03_pipeline_en

* Add model 2023-05-26-xlnet_base_token_classifier_conll03_pipeline_en

* Add model 2023-05-26-xlm_roberta_base_token_classifier_ontonotes_pipeline_en

* Add model 2023-05-26-xlm_roberta_base_token_classifier_conll03_pipeline_en

* Add model 2023-05-26-xlnet_large_token_classifier_conll03_pipeline_en

* Add model 2023-05-26-albert_base_token_classifier_conll03_pipeline_en

* Add model 2023-05-26-albert_large_token_classifier_conll03_pipeline_en

* Add model 2023-05-26-albert_xlarge_token_classifier_conll03_pipeline_en

* Add model 2023-05-26-distilroberta_base_token_classifier_ontonotes_pipeline_en

* Add model 2023-05-26-roberta_base_token_classifier_ontonotes_pipeline_en

* Add model 2023-05-26-roberta_large_token_classifier_conll03_pipeline_en

* Add model 2023-05-26-distilbert_token_classifier_typo_detector_pipeline_en

---------

Co-authored-by: ahmedlone127 <ahmedlone127@gmail.com>

---------

Co-authored-by: jsl-models <74001263+jsl-models@users.noreply.github.com>
Co-authored-by: Naveen-004 <chinna.nk4@gmail.com>
Co-authored-by: ahmedlone127 <ahmedlone127@gmail.com>
Co-authored-by: prabod <prabod@rathnayaka.me>
---
 .../2023-05-25-analyze_sentiment_en.md        | 130 ++++++++++++++
 ...05-25-analyze_sentimentdl_glove_imdb_en.md | 122 +++++++++++++
 .../2023-05-25-check_spelling_en.md           | 120 +++++++++++++
 .../2023-05-25-clean_pattern_en.md            | 120 +++++++++++++
 .../ahmedlone127/2023-05-25-clean_stop_en.md  | 120 +++++++++++++
 .../2023-05-25-dependency_parse_en.md         | 119 ++++++++++++
 .../2023-05-25-entity_recognizer_lg_da.md     |   4 +-
 .../2023-05-25-entity_recognizer_lg_de.md     | 122 +++++++++++++
 .../2023-05-25-entity_recognizer_lg_es.md     |  94 ++++++++++
 .../2023-05-25-entity_recognizer_lg_fi.md     |   4 +-
 .../2023-05-25-entity_recognizer_lg_fr.md     | 122 +++++++++++++
 .../2023-05-25-entity_recognizer_lg_it.md     | 122 +++++++++++++
 .../2023-05-25-entity_recognizer_lg_nl.md     |  94 ++++++++++
 .../2023-05-25-entity_recognizer_lg_no.md     |  94 ++++++++++
 .../2023-05-25-entity_recognizer_lg_pl.md     |  94 ++++++++++
 .../2023-05-25-entity_recognizer_lg_pt.md     |  94 ++++++++++
 .../2023-05-25-entity_recognizer_lg_ru.md     |  94 ++++++++++
 .../2023-05-25-entity_recognizer_lg_sv.md     |  94 ++++++++++
 .../2023-05-25-entity_recognizer_md_da.md     |   4 +-
 .../2023-05-25-entity_recognizer_md_de.md     | 121 +++++++++++++
 .../2023-05-25-entity_recognizer_md_es.md     | 121 +++++++++++++
 .../2023-05-25-entity_recognizer_md_fi.md     |   4 +-
 .../2023-05-25-entity_recognizer_md_fr.md     | 121 +++++++++++++
 .../2023-05-25-entity_recognizer_md_it.md     | 121 +++++++++++++
 .../2023-05-25-entity_recognizer_md_nl.md     | 121 +++++++++++++
 .../2023-05-25-entity_recognizer_md_no.md     | 121 +++++++++++++
 .../2023-05-25-entity_recognizer_md_pl.md     | 121 +++++++++++++
 .../2023-05-25-entity_recognizer_md_pt.md     | 121 +++++++++++++
 .../2023-05-25-entity_recognizer_md_ru.md     | 121 +++++++++++++
 .../2023-05-25-entity_recognizer_md_sv.md     | 121 +++++++++++++
 .../2023-05-25-entity_recognizer_sm_da.md     |   4 +-
 .../2023-05-25-entity_recognizer_sm_es.md     | 121 +++++++++++++
 .../2023-05-25-entity_recognizer_sm_fi.md     |   4 +-
 .../2023-05-25-entity_recognizer_sm_nl.md     | 121 +++++++++++++
 .../2023-05-25-entity_recognizer_sm_no.md     | 121 +++++++++++++
 .../2023-05-25-entity_recognizer_sm_pl.md     | 121 +++++++++++++
 .../2023-05-25-entity_recognizer_sm_pt.md     | 121 +++++++++++++
 .../2023-05-25-entity_recognizer_sm_ru.md     | 121 +++++++++++++
 .../2023-05-25-entity_recognizer_sm_sv.md     | 121 +++++++++++++
 .../2023-05-25-explain_document_dl_en.md      | 128 +++++++++++++
 .../2023-05-25-explain_document_lg_da.md      |   4 +-
 .../2023-05-25-explain_document_lg_es.md      | 124 +++++++++++++
 .../2023-05-25-explain_document_lg_fi.md      |   4 +-
 .../2023-05-25-explain_document_lg_nl.md      | 124 +++++++++++++
 .../2023-05-25-explain_document_lg_no.md      | 124 +++++++++++++
 .../2023-05-25-explain_document_lg_pl.md      | 124 +++++++++++++
 .../2023-05-25-explain_document_lg_pt.md      | 124 +++++++++++++
 .../2023-05-25-explain_document_lg_ru.md      | 124 +++++++++++++
 .../2023-05-25-explain_document_lg_sv.md      | 124 +++++++++++++
 .../2023-05-25-explain_document_md_da.md      |   4 +-
 .../2023-05-25-explain_document_md_de.md      | 123 +++++++++++++
 .../2023-05-25-explain_document_md_es.md      | 123 +++++++++++++
 .../2023-05-25-explain_document_md_fi.md      |   4 +-
 .../2023-05-25-explain_document_md_fr.md      | 123 +++++++++++++
 .../2023-05-25-explain_document_md_it.md      | 123 +++++++++++++
 .../2023-05-25-explain_document_md_nl.md      | 123 +++++++++++++
 .../2023-05-25-explain_document_md_no.md      | 123 +++++++++++++
 .../2023-05-25-explain_document_md_pl.md      | 123 +++++++++++++
 .../2023-05-25-explain_document_md_pt.md      | 123 +++++++++++++
 .../2023-05-25-explain_document_md_ru.md      | 123 +++++++++++++
 .../2023-05-25-explain_document_md_sv.md      | 123 +++++++++++++
 .../2023-05-25-explain_document_ml_en.md      | 123 +++++++++++++
 .../2023-05-25-explain_document_sm_da.md      |   4 +-
 .../2023-05-25-explain_document_sm_es.md      | 123 +++++++++++++
 .../2023-05-25-explain_document_sm_fi.md      |   4 +-
 .../2023-05-25-explain_document_sm_nl.md      | 123 +++++++++++++
 .../2023-05-25-explain_document_sm_no.md      | 123 +++++++++++++
 .../2023-05-25-explain_document_sm_pl.md      | 123 +++++++++++++
 .../2023-05-25-explain_document_sm_pt.md      | 123 +++++++++++++
 .../2023-05-25-explain_document_sm_ru.md      | 123 +++++++++++++
 .../2023-05-25-explain_document_sm_sv.md      | 123 +++++++++++++
 .../2023-05-25-match_chunks_en.md             | 141 +++++++++++++++
 .../2023-05-25-match_datetime_en.md           | 127 +++++++++++++
 .../2023-05-25-match_pattern_en.md            |  77 ++++++++
 ...2023-05-25-movies_sentiment_analysis_en.md |  79 ++++++++
 ...25-onto_recognize_entities_bert_base_en.md |   4 +-
 ...5-onto_recognize_entities_bert_large_en.md |   4 +-
 ...-onto_recognize_entities_bert_medium_en.md |   4 +-
 ...25-onto_recognize_entities_bert_mini_en.md |   4 +-
 ...5-onto_recognize_entities_bert_small_en.md |   4 +-
 ...25-onto_recognize_entities_bert_tiny_en.md |   4 +-
 ...onto_recognize_entities_electra_base_en.md |   4 +-
 ...nto_recognize_entities_electra_large_en.md |   4 +-
 ...nto_recognize_entities_electra_small_en.md |   4 +-
 ...023-05-25-onto_recognize_entities_lg_en.md |  94 ++++++++++
 ...023-05-25-onto_recognize_entities_sm_en.md | 121 +++++++++++++
 .../2023-05-25-recognize_entities_bert_en.md  | 123 +++++++++++++
 .../2023-05-25-recognize_entities_dl_en.md    | 122 +++++++++++++
 .../2023-05-25-text_cleaning_en.md            |  80 +++++++++
 ...se_token_classifier_conll03_pipeline_en.md | 106 +++++++++++
 ...ge_token_classifier_conll03_pipeline_en.md | 106 +++++++++++
 ...ge_token_classifier_conll03_pipeline_en.md | 106 +++++++++++
 ...equence_classifier_age_news_pipeline_en.md |  98 ++++++++++
 ...ence_classifier_trec_coarse_pipeline_en.md | 100 +++++++++++
 ...lassifier_dutch_udlassy_ner_pipeline_nl.md | 106 +++++++++++
 ..._token_classifier_hi_en_ner_pipeline_hi.md | 113 ++++++++++++
 ...token_classifier_scandi_ner_pipeline_xx.md | 103 +++++++++++
 ...token_classifier_masakhaner_pipeline_xx.md | 108 +++++++++++
 ...en_classifier_typo_detector_pipeline_en.md | 105 +++++++++++
 ...en_classifier_typo_detector_pipeline_is.md | 102 +++++++++++
 ..._token_classifier_ontonotes_pipeline_en.md | 107 +++++++++++
 ...se_token_classifier_conll03_pipeline_en.md | 106 +++++++++++
 ...ge_token_classifier_conll03_pipeline_en.md | 106 +++++++++++
 ...23-05-26-nerdl_fewnerd_100d_pipeline_en.md | 109 +++++++++++
 ...erdl_fewnerd_subentity_100d_pipeline_en.md | 110 ++++++++++++
 ...05-26-nerdl_restaurant_100d_pipeline_en.md | 119 ++++++++++++
 .../2023-05-26-pos_ud_bokmaal_nb.md           | 138 ++++++++++++++
 .../2023-05-26-recognize_entities_dl_fa.md    | 169 ++++++++++++++++++
 ..._token_classifier_ontonotes_pipeline_en.md | 104 +++++++++++
 ...ge_token_classifier_conll03_pipeline_en.md | 106 +++++++++++
 ..._classifier_bne_capitel_ner_pipeline_es.md | 104 +++++++++++
 ...en_classifier_icelandic_ner_pipeline_is.md | 106 +++++++++++
 ...en_classifier_timex_semeval_pipeline_en.md | 110 ++++++++++++
 .../2023-05-26-spellcheck_dl_pipeline_en.md   | 112 ++++++++++++
 ...se_token_classifier_conll03_pipeline_en.md | 106 +++++++++++
 ..._token_classifier_ontonotes_pipeline_en.md | 107 +++++++++++
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 ...se_token_classifier_conll03_pipeline_en.md | 106 +++++++++++
 ...ge_token_classifier_conll03_pipeline_en.md | 103 +++++++++++
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diff --git a/docs/_posts/ahmedlone127/2023-05-25-analyze_sentiment_en.md b/docs/_posts/ahmedlone127/2023-05-25-analyze_sentiment_en.md
new file mode 100644
index 00000000000000..8bdc4ac6e9d31a
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-analyze_sentiment_en.md
@@ -0,0 +1,130 @@
+---
+layout: model
+title: Sentiment Analysis pipeline for English
+author: John Snow Labs
+name: analyze_sentiment
+date: 2023-05-25
+tags: [open_source, english, analyze_sentiment, pipeline, en]
+task: Named Entity Recognition
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The analyze_sentiment is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps 
+and recognizes entities .
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/analyze_sentiment_en_4.4.2_3.4_1685040876208.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/analyze_sentiment_en_4.4.2_3.4_1685040876208.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+
+```python
+
+from sparknlp.pretrained import PretrainedPipeline
+
+pipeline = PretrainedPipeline('analyze_sentiment', lang = 'en')
+
+result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""")
+
+
+```
+```scala
+
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
+
+val pipeline = new PretrainedPipeline("analyze_sentiment", lang = "en")
+
+val result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""")
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = ["""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!"""]
+result_df = nlu.load('en.classify').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+
+pipeline = PretrainedPipeline('analyze_sentiment', lang = 'en')
+
+result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""")
+```
+```scala
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
+
+val pipeline = new PretrainedPipeline("analyze_sentiment", lang = "en")
+
+val result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""")
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = ["""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!"""]
+result_df = nlu.load('en.classify').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | text                                                                                                                                                                                                                                                                      | sentiment   |
+|---:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------|
+|  0 | Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now! | positive    |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|analyze_sentiment|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|5.1 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- NorvigSweetingModel
+- ViveknSentimentModel
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-analyze_sentimentdl_glove_imdb_en.md b/docs/_posts/ahmedlone127/2023-05-25-analyze_sentimentdl_glove_imdb_en.md
new file mode 100644
index 00000000000000..2706b7b2ff19b3
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-analyze_sentimentdl_glove_imdb_en.md
@@ -0,0 +1,122 @@
+---
+layout: model
+title: Sentiment Analysis pipeline for English (analyze_sentimentdl_glove_imdb)
+author: John Snow Labs
+name: analyze_sentimentdl_glove_imdb
+date: 2023-05-25
+tags: [open_source, english, analyze_sentimentdl_glove_imdb, pipeline, en]
+task: Named Entity Recognition
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The analyze_sentimentdl_glove_imdb is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps 
+and recognizes entities .
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/analyze_sentimentdl_glove_imdb_en_4.4.2_3.4_1685051273135.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/analyze_sentimentdl_glove_imdb_en_4.4.2_3.4_1685051273135.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('analyze_sentimentdl_glove_imdb', lang = 'en')
+annotations =  pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("analyze_sentimentdl_glove_imdb", lang = "en")
+val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Hello from John Snow Labs ! ""]
+result_df = nlu.load('en.sentiment.glove').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('analyze_sentimentdl_glove_imdb', lang = 'en')
+annotations =  pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("analyze_sentimentdl_glove_imdb", lang = "en")
+val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Hello from John Snow Labs ! ""]
+result_df = nlu.load('en.sentiment.glove').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                         | sentence                        | tokens                                         | word_embeddings              | sentence_embeddings          | sentiment   |
+|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-----------------------------|:------------|
+|  0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[0.2668800055980682,.,...]] | [[0.0771183446049690,.,...]] | ['neg']     |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|analyze_sentimentdl_glove_imdb|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|161.6 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- SentenceEmbeddings
+- SentimentDLModel
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-check_spelling_en.md b/docs/_posts/ahmedlone127/2023-05-25-check_spelling_en.md
new file mode 100644
index 00000000000000..3569d4a7d52c3a
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-check_spelling_en.md
@@ -0,0 +1,120 @@
+---
+layout: model
+title: Spell Checking Pipeline for English
+author: John Snow Labs
+name: check_spelling
+date: 2023-05-25
+tags: [open_source, english, check_spelling, pipeline, en]
+task: Spell Check
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The check_spelling is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps 
+        and recognizes entities .
+         It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/check_spelling_en_4.4.2_3.4_1685039983204.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/check_spelling_en_4.4.2_3.4_1685039983204.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('check_spelling', lang = 'en')
+annotations =  pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("check_spelling", lang = "en")
+val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Hello from John Snow Labs ! ""]
+result_df = nlu.load('').predict(text)
+result_df
+    
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('check_spelling', lang = 'en')
+annotations =  pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("check_spelling", lang = "en")
+val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Hello from John Snow Labs ! ""]
+result_df = nlu.load('').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                         | sentence                        | token                                          | checked                                        |
+|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------------------------|
+|  0 | ['I liek to live dangertus ! '] | ['I liek to live dangertus !'] | ['I', 'liek', 'to', 'live', 'dangertus', '!'] | ['I', 'like', 'to', 'live', 'dangerous', '!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|check_spelling|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|906.2 KB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- NorvigSweetingModel
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-clean_pattern_en.md b/docs/_posts/ahmedlone127/2023-05-25-clean_pattern_en.md
new file mode 100644
index 00000000000000..f8f32369a4eda7
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-clean_pattern_en.md
@@ -0,0 +1,120 @@
+---
+layout: model
+title: Clean patterns pipeline for English
+author: John Snow Labs
+name: clean_pattern
+date: 2023-05-25
+tags: [open_source, english, clean_pattern, pipeline, en]
+task: Named Entity Recognition
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The clean_pattern is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps 
+        and recognizes entities .
+         It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/clean_pattern_en_4.4.2_3.4_1685040134078.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/clean_pattern_en_4.4.2_3.4_1685040134078.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('clean_pattern', lang = 'en')
+annotations =  pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0]
+annotations.keys()
+```
+```scala
+
+
+val pipeline = new PretrainedPipeline("clean_pattern", lang = "en")
+val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0)
+```
+
+{:.nlu-block}
+```python
+
+
+import nlu
+text = [""Hello from John Snow Labs ! ""]
+result_df = nlu.load('en.clean.pattern').predict(text)
+result_df
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('clean_pattern', lang = 'en')
+annotations =  pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("clean_pattern", lang = "en")
+val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Hello from John Snow Labs ! ""]
+result_df = nlu.load('en.clean.pattern').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
+|    | document   | sentence   | token     | normal    |
+|---:|:-----------|:-----------|:----------|:----------|
+|  0 | ['Hello']  | ['Hello']  | ['Hello'] | ['Hello'] ||    | document                         | sentence                        | token                                          | normal                                    |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|clean_pattern|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|17.2 KB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- NormalizerModel
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-clean_stop_en.md b/docs/_posts/ahmedlone127/2023-05-25-clean_stop_en.md
new file mode 100644
index 00000000000000..0f2a945ce528cf
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-clean_stop_en.md
@@ -0,0 +1,120 @@
+---
+layout: model
+title: Clean documents pipeline for English
+author: John Snow Labs
+name: clean_stop
+date: 2023-05-25
+tags: [open_source, english, clean_stop, pipeline, en]
+task: Named Entity Recognition
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The clean_stop is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps 
+and recognizes entities .
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/clean_stop_en_4.4.2_3.4_1685040188479.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/clean_stop_en_4.4.2_3.4_1685040188479.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('clean_stop', lang = 'en')
+annotations =  pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("clean_stop", lang = "en")
+val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Hello from John Snow Labs ! ""]
+result_df = nlu.load('en.clean.stop').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('clean_stop', lang = 'en')
+annotations =  pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("clean_stop", lang = "en")
+val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Hello from John Snow Labs ! ""]
+result_df = nlu.load('en.clean.stop').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                         | sentence                        | token                                          | cleanTokens                            |
+|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:---------------------------------------|
+|  0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | ['Hello', 'John', 'Snow', 'Labs', '!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|clean_stop|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|14.1 KB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- StopWordsCleaner
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-dependency_parse_en.md b/docs/_posts/ahmedlone127/2023-05-25-dependency_parse_en.md
new file mode 100644
index 00000000000000..9f45e70779dbe1
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-dependency_parse_en.md
@@ -0,0 +1,119 @@
+---
+layout: model
+title: Typed Dependency Parsing pipeline for English
+author: John Snow Labs
+name: dependency_parse
+date: 2023-05-25
+tags: [pipeline, dependency_parsing, untyped_dependency_parsing, typed_dependency_parsing, laballed_depdency_parsing, unlaballed_depdency_parsing, en, open_source]
+task: Dependency Parser
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Typed Dependency parser, trained on the on the CONLL dataset. 
+
+Dependency parsing is the task of extracting a dependency parse of a sentence that represents its grammatical structure and defines the relationships between “head” words and words, which modify those heads.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dependency_parse_en_4.4.2_3.4_1685038231137.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dependency_parse_en_4.4.2_3.4_1685038231137.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline('dependency_parse', lang = 'en')
+annotations =  pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("dependency_parse", lang = "en")
+val result = pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence")(0)
+
+```
+
+{:.nlu-block}
+```python
+
+nlu.load("dep.typed").predict("Dependencies represents relationships betweens words in a Sentence")
+
+    
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline('dependency_parse', lang = 'en')
+annotations =  pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("dependency_parse", lang = "en")
+val result = pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence")(0)
+```
+
+{:.nlu-block}
+```python
+nlu.load("dep.typed").predict("Dependencies represents relationships betweens words in a Sentence")
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
++---------------------------------------------------------------------------------+--------------------------------------------------------+
+|result                                                                           |result                                                  |
++---------------------------------------------------------------------------------+--------------------------------------------------------+
+|[ROOT, Dependencies, represents, words, relationships, Sentence, Sentence, words]|[root, parataxis, nsubj, amod, nsubj, case, nsubj, flat]|
++---------------------------------------------------------------------------------+--------------------------------------------------------+
+
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|dependency_parse|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|23.8 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- PerceptronModel
+- DependencyParserModel
+- TypedDependencyParserModel
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_da.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_da.md
index e6ef48c03cb90f..a914f9245e6972 100644
--- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_da.md
+++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_da.md
@@ -28,8 +28,8 @@ It performs most of the common text processing tasks on your dataframe
 {:.btn-box}
 <button class="button button-orange" disabled>Live Demo</button>
 <button class="button button-orange" disabled>Open in Colab</button>
-[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_da_4.4.2_3.4_1685000597398.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
-[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_da_4.4.2_3.4_1685000597398.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_da_4.4.2_3.4_1685055716132.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_da_4.4.2_3.4_1685055716132.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
 
 ## How to use
 
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_de.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_de.md
new file mode 100644
index 00000000000000..ac74fffbb10209
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_de.md
@@ -0,0 +1,122 @@
+---
+layout: model
+title: Recognize Entities DL pipeline for German - Large
+author: John Snow Labs
+name: entity_recognizer_lg
+date: 2023-05-25
+tags: [open_source, german, entity_recognizer_lg, pipeline, de]
+task: Named Entity Recognition
+language: de
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps 
+and recognizes entities .
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_de_4.4.2_3.4_1685042092802.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_de_4.4.2_3.4_1685042092802.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'de')
+annotations =  pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "de")
+val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Hallo aus John Snow Labs! ""]
+result_df = nlu.load('de.ner.recognizer.lg').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'de')
+annotations =  pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "de")
+val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Hallo aus John Snow Labs! ""]
+result_df = nlu.load('de.ner.recognizer.lg').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                       | sentence                      | token                                     | embeddings                   | ner                                   | entities            |
+|---:|:-------------------------------|:------------------------------|:------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------|
+|  0 | ['Hallo aus John Snow Labs! '] | ['Hallo aus John Snow Labs!'] | ['Hallo', 'aus', 'John', 'Snow', 'Labs!'] | [[-0.245989993214607,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|entity_recognizer_lg|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|de|
+|Size:|2.5 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_es.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_es.md
new file mode 100644
index 00000000000000..dcaf88d51af2a1
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_es.md
@@ -0,0 +1,94 @@
+---
+layout: model
+title: Entity Recognition Pipeline (Large, Spanish)
+author: John Snow Labs
+name: entity_recognizer_lg
+date: 2023-05-25
+tags: [es, open_source]
+task: Named Entity Recognition
+language: es
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities.
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_es_4.4.2_3.4_1685044314081.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_es_4.4.2_3.4_1685044314081.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline("entity_recognizer_lg", "es")
+
+result = pipeline.annotate("""I love johnsnowlabs! """)
+```
+
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("es.ner.lg").predict("""I love johnsnowlabs! """)
+```
+
+</div>
+
+{:.model-param}
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline("entity_recognizer_lg", "es")
+
+result = pipeline.annotate("""I love johnsnowlabs! """)
+```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("es.ner.lg").predict("""I love johnsnowlabs! """)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|entity_recognizer_lg|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|es|
+|Size:|2.5 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_fi.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_fi.md
index ed5f7f9a393147..013f16d4dbdb1c 100644
--- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_fi.md
+++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_fi.md
@@ -28,8 +28,8 @@ It performs most of the common text processing tasks on your dataframe
 {:.btn-box}
 <button class="button button-orange" disabled>Live Demo</button>
 <button class="button button-orange" disabled>Open in Colab</button>
-[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_fi_4.4.2_3.4_1685002127724.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
-[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_fi_4.4.2_3.4_1685002127724.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_fi_4.4.2_3.4_1685057277652.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_fi_4.4.2_3.4_1685057277652.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
 
 ## How to use
 
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_fr.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_fr.md
new file mode 100644
index 00000000000000..9c4e73c2af609a
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_fr.md
@@ -0,0 +1,122 @@
+---
+layout: model
+title: Recognize Entities DL pipeline for French - Large
+author: John Snow Labs
+name: entity_recognizer_lg
+date: 2023-05-25
+tags: [open_source, french, entity_recognizer_lg, pipeline, fr]
+task: Named Entity Recognition
+language: fr
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps 
+and recognizes entities .
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_fr_4.4.2_3.4_1685039016234.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_fr_4.4.2_3.4_1685039016234.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'fr')
+annotations =  pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "fr")
+val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Bonjour de John Snow Labs! ""]
+result_df = nlu.load('fr.ner').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'fr')
+annotations =  pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "fr")
+val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Bonjour de John Snow Labs! ""]
+result_df = nlu.load('fr.ner').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                        | sentence                       | token                                      | embeddings                   | ner                                   | entities            |
+|---:|:--------------------------------|:-------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------|
+|  0 | ['Bonjour de John Snow Labs! '] | ['Bonjour de John Snow Labs!'] | ['Bonjour', 'de', 'John', 'Snow', 'Labs!'] | [[-0.010997000150382,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|entity_recognizer_lg|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|fr|
+|Size:|2.5 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_it.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_it.md
new file mode 100644
index 00000000000000..8c194b43e3c24a
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_it.md
@@ -0,0 +1,122 @@
+---
+layout: model
+title: Recognize Entities DL pipeline for Italian - Large
+author: John Snow Labs
+name: entity_recognizer_lg
+date: 2023-05-25
+tags: [open_source, italian, entity_recognizer_lg, pipeline, it]
+task: Named Entity Recognition
+language: it
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps 
+        and recognizes entities .
+         It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_it_4.4.2_3.4_1685039686865.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_it_4.4.2_3.4_1685039686865.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'it')
+annotations =  pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "it")
+val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Ciao da John Snow Labs! ""]
+result_df = nlu.load('it.ner.lg').predict(text)
+result_df
+    
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'it')
+annotations =  pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "it")
+val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Ciao da John Snow Labs! ""]
+result_df = nlu.load('it.ner.lg').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                     | sentence                    | token                                   | embeddings                   | ner                                   | entities            |
+|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------|
+|  0 | ['Ciao da John Snow Labs! '] | ['Ciao da John Snow Labs!'] | ['Ciao', 'da', 'John', 'Snow', 'Labs!'] | [[-0.238279998302459,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|entity_recognizer_lg|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|it|
+|Size:|2.5 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_nl.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_nl.md
new file mode 100644
index 00000000000000..fd4314e0006bfc
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_nl.md
@@ -0,0 +1,94 @@
+---
+layout: model
+title: Entity Recognizer LG
+author: John Snow Labs
+name: entity_recognizer_lg
+date: 2023-05-25
+tags: [nl, open_source]
+task: Named Entity Recognition
+language: nl
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities.
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_nl_4.4.2_3.4_1685050724969.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_nl_4.4.2_3.4_1685050724969.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline("entity_recognizer_lg", "nl")
+
+result = pipeline.annotate("""I love johnsnowlabs! """)
+```
+
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("nl.ner.lg").predict("""I love johnsnowlabs! """)
+```
+
+</div>
+
+{:.model-param}
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline("entity_recognizer_lg", "nl")
+
+result = pipeline.annotate("""I love johnsnowlabs! """)
+```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("nl.ner.lg").predict("""I love johnsnowlabs! """)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|entity_recognizer_lg|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|nl|
+|Size:|2.5 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_no.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_no.md
new file mode 100644
index 00000000000000..35fa6de66de6f0
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_no.md
@@ -0,0 +1,94 @@
+---
+layout: model
+title: Entity Recognizer LG
+author: John Snow Labs
+name: entity_recognizer_lg
+date: 2023-05-25
+tags: ["no", open_source]
+task: Named Entity Recognition
+language: "no"
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities.
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_no_4.4.2_3.4_1685052497867.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_no_4.4.2_3.4_1685052497867.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline("entity_recognizer_lg", "no")
+
+result = pipeline.annotate("""I love johnsnowlabs! """)
+```
+
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("no.ner.lg").predict("""I love johnsnowlabs! """)
+```
+
+</div>
+
+{:.model-param}
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline("entity_recognizer_lg", "no")
+
+result = pipeline.annotate("""I love johnsnowlabs! """)
+```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("no.ner.lg").predict("""I love johnsnowlabs! """)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|entity_recognizer_lg|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|no|
+|Size:|2.5 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_pl.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_pl.md
new file mode 100644
index 00000000000000..44db088ba2847b
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_pl.md
@@ -0,0 +1,94 @@
+---
+layout: model
+title: Entity Recognizer LG
+author: John Snow Labs
+name: entity_recognizer_lg
+date: 2023-05-25
+tags: [pl, open_source]
+task: Named Entity Recognition
+language: pl
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities.
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_pl_4.4.2_3.4_1685049158345.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_pl_4.4.2_3.4_1685049158345.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline("entity_recognizer_lg", "pl")
+
+result = pipeline.annotate("""I love johnsnowlabs! """)
+```
+
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("pl.ner.lg").predict("""I love johnsnowlabs! """)
+```
+
+</div>
+
+{:.model-param}
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline("entity_recognizer_lg", "pl")
+
+result = pipeline.annotate("""I love johnsnowlabs! """)
+```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("pl.ner.lg").predict("""I love johnsnowlabs! """)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|entity_recognizer_lg|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|pl|
+|Size:|2.5 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_pt.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_pt.md
new file mode 100644
index 00000000000000..82b823e2afca0a
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_pt.md
@@ -0,0 +1,94 @@
+---
+layout: model
+title: Entity Recognizer LG
+author: John Snow Labs
+name: entity_recognizer_lg
+date: 2023-05-25
+tags: [pt, open_source]
+task: Named Entity Recognition
+language: pt
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities.
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_pt_4.4.2_3.4_1685047557531.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_pt_4.4.2_3.4_1685047557531.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline("entity_recognizer_lg", "pt")
+
+result = pipeline.annotate("""I love johnsnowlabs! """)
+```
+
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("pt.ner.lg").predict("""I love johnsnowlabs! """)
+```
+
+</div>
+
+{:.model-param}
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline("entity_recognizer_lg", "pt")
+
+result = pipeline.annotate("""I love johnsnowlabs! """)
+```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("pt.ner.lg").predict("""I love johnsnowlabs! """)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|entity_recognizer_lg|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|pt|
+|Size:|2.5 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_ru.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_ru.md
new file mode 100644
index 00000000000000..a4c97678e65201
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_ru.md
@@ -0,0 +1,94 @@
+---
+layout: model
+title: Entity Recognizer LG
+author: John Snow Labs
+name: entity_recognizer_lg
+date: 2023-05-25
+tags: [ru, open_source]
+task: Named Entity Recognition
+language: ru
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities.
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_ru_4.4.2_3.4_1685045880932.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_ru_4.4.2_3.4_1685045880932.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline("entity_recognizer_lg", "ru")
+
+result = pipeline.annotate("""I love johnsnowlabs! """)
+```
+
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("ru.ner.lg").predict("""I love johnsnowlabs! """)
+```
+
+</div>
+
+{:.model-param}
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline("entity_recognizer_lg", "ru")
+
+result = pipeline.annotate("""I love johnsnowlabs! """)
+```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("ru.ner.lg").predict("""I love johnsnowlabs! """)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|entity_recognizer_lg|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|ru|
+|Size:|2.5 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_sv.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_sv.md
new file mode 100644
index 00000000000000..d7bb9221fbf93c
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_sv.md
@@ -0,0 +1,94 @@
+---
+layout: model
+title: Entity Recognizer LG
+author: John Snow Labs
+name: entity_recognizer_lg
+date: 2023-05-25
+tags: [sv, open_source]
+task: Named Entity Recognition
+language: sv
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities.
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_sv_4.4.2_3.4_1685054155985.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_sv_4.4.2_3.4_1685054155985.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline("entity_recognizer_lg", "sv")
+
+result = pipeline.annotate("""I love johnsnowlabs! """)
+```
+
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("sv.ner.lg").predict("""I love johnsnowlabs! """)
+```
+
+</div>
+
+{:.model-param}
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline("entity_recognizer_lg", "sv")
+
+result = pipeline.annotate("""I love johnsnowlabs! """)
+```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("sv.ner.lg").predict("""I love johnsnowlabs! """)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|entity_recognizer_lg|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|sv|
+|Size:|2.5 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_da.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_da.md
index f83e7485baf99d..8aaa0181ed988d 100644
--- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_da.md
+++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_da.md
@@ -29,8 +29,8 @@ It performs most of the common text processing tasks on your dataframe
 {:.btn-box}
 <button class="button button-orange" disabled>Live Demo</button>
 <button class="button button-orange" disabled>Open in Colab</button>
-[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_da_4.4.2_3.4_1685000811210.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
-[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_da_4.4.2_3.4_1685000811210.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_da_4.4.2_3.4_1685055942476.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_da_4.4.2_3.4_1685055942476.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
 
 ## How to use
 
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_de.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_de.md
new file mode 100644
index 00000000000000..ad7bc5c4ca7a30
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_de.md
@@ -0,0 +1,121 @@
+---
+layout: model
+title: Recognize Entities DL Pipeline for German - Medium
+author: John Snow Labs
+name: entity_recognizer_md
+date: 2023-05-25
+tags: [open_source, german, entity_recognizer_md, pipeline, de]
+task: Named Entity Recognition
+language: de
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_de_4.4.2_3.4_1685042320125.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_de_4.4.2_3.4_1685042320125.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'de')
+annotations =  pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "de")
+val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Hallo aus John Snow Labs! ""]
+result_df = nlu.load('de.ner.recognizer').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'de')
+annotations =  pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "de")
+val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Hallo aus John Snow Labs! ""]
+result_df = nlu.load('de.ner.recognizer').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                       | sentence                      | token                                     | embeddings                   | ner                                   | entities            |
+|---:|:-------------------------------|:------------------------------|:------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------|
+|  0 | ['Hallo aus John Snow Labs! '] | ['Hallo aus John Snow Labs!'] | ['Hallo', 'aus', 'John', 'Snow', 'Labs!'] | [[0.5910000205039978,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|entity_recognizer_md|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|de|
+|Size:|461.8 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_es.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_es.md
new file mode 100644
index 00000000000000..83f9541829c9aa
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_es.md
@@ -0,0 +1,121 @@
+---
+layout: model
+title: Recognize Entities DL Pipeline for Spanish - Medium
+author: John Snow Labs
+name: entity_recognizer_md
+date: 2023-05-25
+tags: [open_source, spanish, entity_recognizer_md, pipeline, es]
+task: Named Entity Recognition
+language: es
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_es_4.4.2_3.4_1685044529658.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_es_4.4.2_3.4_1685044529658.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'es')
+annotations =  pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "es")
+val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Hola de John Snow Labs! ""]
+result_df = nlu.load('es.ner.md').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'es')
+annotations =  pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "es")
+val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Hola de John Snow Labs! ""]
+result_df = nlu.load('es.ner.md').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                     | sentence                    | token                                   | embeddings                   | ner                                   | entities            |
+|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------|
+|  0 | ['Hola de John Snow Labs! '] | ['Hola de John Snow Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | [[0.5123000144958496,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|entity_recognizer_md|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|es|
+|Size:|461.9 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_fi.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_fi.md
index 6b8bd69f15a865..fe05574fe2541a 100644
--- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_fi.md
+++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_fi.md
@@ -29,8 +29,8 @@ It performs most of the common text processing tasks on your dataframe
 {:.btn-box}
 <button class="button button-orange" disabled>Live Demo</button>
 <button class="button button-orange" disabled>Open in Colab</button>
-[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_fi_4.4.2_3.4_1685002357393.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
-[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_fi_4.4.2_3.4_1685002357393.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_fi_4.4.2_3.4_1685057503410.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_fi_4.4.2_3.4_1685057503410.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
 
 ## How to use
 
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_fr.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_fr.md
new file mode 100644
index 00000000000000..3b4d8d57efb687
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_fr.md
@@ -0,0 +1,121 @@
+---
+layout: model
+title: Recognize Entities DL Pipeline for French - Medium
+author: John Snow Labs
+name: entity_recognizer_md
+date: 2023-05-25
+tags: [open_source, french, entity_recognizer_md, pipeline, fr]
+task: Named Entity Recognition
+language: fr
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_fr_4.4.2_3.4_1685039236291.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_fr_4.4.2_3.4_1685039236291.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'fr')
+annotations =  pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "fr")
+val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Bonjour de John Snow Labs! ""]
+result_df = nlu.load('fr.ner.md').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'fr')
+annotations =  pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "fr")
+val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Bonjour de John Snow Labs! ""]
+result_df = nlu.load('fr.ner.md').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                        | sentence                       | token                                      | embeddings                   | ner                                        | entities                       |
+|---:|:--------------------------------|:-------------------------------|:-------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------------------|
+|  0 | ['Bonjour de John Snow Labs! '] | ['Bonjour de John Snow Labs!'] | ['Bonjour', 'de', 'John', 'Snow', 'Labs!'] | [[0.0783179998397827,.,...]] | ['I-MISC', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['Bonjour', 'John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|entity_recognizer_md|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|fr|
+|Size:|462.0 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_it.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_it.md
new file mode 100644
index 00000000000000..6faebb940e6857
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_it.md
@@ -0,0 +1,121 @@
+---
+layout: model
+title: Recognize Entities DL Pipeline for Italian - Medium
+author: John Snow Labs
+name: entity_recognizer_md
+date: 2023-05-25
+tags: [open_source, italian, entity_recognizer_md, pipeline, it]
+task: Named Entity Recognition
+language: it
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+         It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_it_4.4.2_3.4_1685039912274.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_it_4.4.2_3.4_1685039912274.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'it')
+annotations =  pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "it")
+val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Ciao da John Snow Labs! ""]
+result_df = nlu.load('it.ner').predict(text)
+result_df
+    
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'it')
+annotations =  pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "it")
+val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Ciao da John Snow Labs! ""]
+result_df = nlu.load('it.ner').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                     | sentence                    | token                                   | embeddings                   | ner                                   | entities            |
+|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------|
+|  0 | ['Ciao da John Snow Labs! '] | ['Ciao da John Snow Labs!'] | ['Ciao', 'da', 'John', 'Snow', 'Labs!'] | [[-0.146050006151199,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|entity_recognizer_md|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|it|
+|Size:|462.0 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_nl.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_nl.md
new file mode 100644
index 00000000000000..ead207b4c33eb7
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_nl.md
@@ -0,0 +1,121 @@
+---
+layout: model
+title: Recognize Entities DL Pipeline for Dutch - Medium
+author: John Snow Labs
+name: entity_recognizer_md
+date: 2023-05-25
+tags: [open_source, dutch, entity_recognizer_md, pipeline, nl]
+task: Named Entity Recognition
+language: nl
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+         It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_nl_4.4.2_3.4_1685050943374.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_nl_4.4.2_3.4_1685050943374.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'nl')
+annotations =  pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "nl")
+val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Hallo van John Snow Labs! ""]
+result_df = nlu.load('nl.ner.md').predict(text)
+result_df
+    
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'nl')
+annotations =  pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "nl")
+val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Hallo van John Snow Labs! ""]
+result_df = nlu.load('nl.ner.md').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                       | sentence                      | token                                     | embeddings                   | ner                                   | entities            |
+|---:|:-------------------------------|:------------------------------|:------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------|
+|  0 | ['Hallo van John Snow Labs! '] | ['Hallo van John Snow Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | [[0.5910000205039978,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|entity_recognizer_md|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|nl|
+|Size:|462.0 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_no.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_no.md
new file mode 100644
index 00000000000000..fd95d668ad2f48
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_no.md
@@ -0,0 +1,121 @@
+---
+layout: model
+title: Recognize Entities DL Pipeline for Norwegian (Bokmal) - Medium
+author: John Snow Labs
+name: entity_recognizer_md
+date: 2023-05-25
+tags: [open_source, norwegian_bokmal, entity_recognizer_md, pipeline, "no"]
+task: Named Entity Recognition
+language: "no"
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_no_4.4.2_3.4_1685052723511.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_no_4.4.2_3.4_1685052723511.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'no')
+annotations =  pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "no")
+val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Hei fra John Snow Labs! ""]
+result_df = nlu.load('no.ner.md').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'no')
+annotations =  pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "no")
+val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Hei fra John Snow Labs! ""]
+result_df = nlu.load('no.ner.md').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                     | sentence                    | token                                   | embeddings                   | ner                                   | entities            |
+|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------|
+|  0 | ['Hei fra John Snow Labs! '] | ['Hei fra John Snow Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | [[0.1868100017309188,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|entity_recognizer_md|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|no|
+|Size:|462.8 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_pl.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_pl.md
new file mode 100644
index 00000000000000..0daaff9cd34996
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_pl.md
@@ -0,0 +1,121 @@
+---
+layout: model
+title: Recognize Entities DL Pipeline for Polish - Medium
+author: John Snow Labs
+name: entity_recognizer_md
+date: 2023-05-25
+tags: [open_source, polish, entity_recognizer_md, pipeline, pl]
+task: Named Entity Recognition
+language: pl
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+         It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_pl_4.4.2_3.4_1685049374697.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_pl_4.4.2_3.4_1685049374697.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'pl')
+annotations =  pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "pl")
+val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Witaj z John Snow Labs! ""]
+result_df = nlu.load('pl.ner.md').predict(text)
+result_df
+    
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'pl')
+annotations =  pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "pl")
+val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Witaj z John Snow Labs! ""]
+result_df = nlu.load('pl.ner.md').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                     | sentence                    | token                                   | embeddings                   | ner                                   | entities            |
+|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------|
+|  0 | ['Witaj z John Snow Labs! '] | ['Witaj z John Snow Labs!'] | ['Witaj', 'z', 'John', 'Snow', 'Labs!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|entity_recognizer_md|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|pl|
+|Size:|462.2 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_pt.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_pt.md
new file mode 100644
index 00000000000000..697bdc63f341cb
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_pt.md
@@ -0,0 +1,121 @@
+---
+layout: model
+title: Recognize Entities DL Pipeline for Portuguese - Medium
+author: John Snow Labs
+name: entity_recognizer_md
+date: 2023-05-25
+tags: [open_source, portuguese, entity_recognizer_md, pipeline, pt]
+task: Named Entity Recognition
+language: pt
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+         It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_pt_4.4.2_3.4_1685047791424.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_pt_4.4.2_3.4_1685047791424.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'pt')
+annotations =  pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "pt")
+val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Olá de John Snow Labs! ""]
+result_df = nlu.load('pt.ner.md').predict(text)
+result_df
+    
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'pt')
+annotations =  pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "pt")
+val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Olá de John Snow Labs! ""]
+result_df = nlu.load('pt.ner.md').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                    | sentence                   | token                                  | embeddings                   | ner                                   | entities            |
+|---:|:----------------------------|:---------------------------|:---------------------------------------|:-----------------------------|:--------------------------------------|:--------------------|
+|  0 | ['Olá de John Snow Labs! '] | ['Olá de John Snow Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|entity_recognizer_md|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|pt|
+|Size:|462.2 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_ru.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_ru.md
new file mode 100644
index 00000000000000..c05bbe85ae6a23
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_ru.md
@@ -0,0 +1,121 @@
+---
+layout: model
+title: Recognize Entities DL Pipeline for Russian - Medium
+author: John Snow Labs
+name: entity_recognizer_md
+date: 2023-05-25
+tags: [open_source, russian, entity_recognizer_md, pipeline, ru]
+task: Named Entity Recognition
+language: ru
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_ru_4.4.2_3.4_1685046110885.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_ru_4.4.2_3.4_1685046110885.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'ru')
+annotations =  pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "ru")
+val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Здравствуйте из Джона Снежных Лабораторий! ""]
+result_df = nlu.load('ru.ner.md').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'ru')
+annotations =  pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "ru")
+val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Здравствуйте из Джона Снежных Лабораторий! ""]
+result_df = nlu.load('ru.ner.md').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                                        | sentence                                       | token                                                      | embeddings                   | ner                                   | entities                       |
+|---:|:------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------------------|:-----------------------------|:--------------------------------------|:-------------------------------|
+|  0 | ['Здравствуйте из Джона Снежных Лабораторий! '] | ['Здравствуйте из Джона Снежных Лабораторий!'] | ['Здравствуйте', 'из', 'Джона', 'Снежных', 'Лабораторий!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-LOC', 'I-LOC', 'I-LOC'] | ['Джона Снежных Лабораторий!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|entity_recognizer_md|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|ru|
+|Size:|462.2 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_sv.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_sv.md
new file mode 100644
index 00000000000000..0d31ac7e45bec8
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_sv.md
@@ -0,0 +1,121 @@
+---
+layout: model
+title: Recognize Entities DL Pipeline for Swedish - Medium
+author: John Snow Labs
+name: entity_recognizer_md
+date: 2023-05-25
+tags: [open_source, swedish, entity_recognizer_md, pipeline, sv]
+task: Named Entity Recognition
+language: sv
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_sv_4.4.2_3.4_1685054380902.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_sv_4.4.2_3.4_1685054380902.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'sv')
+annotations =  pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "sv")
+val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Hej från John Snow Labs! ""]
+result_df = nlu.load('sv.ner.md').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'sv')
+annotations =  pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "sv")
+val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Hej från John Snow Labs! ""]
+result_df = nlu.load('sv.ner.md').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                      | sentence                     | token                                    | embeddings                   | ner                                   | entities            |
+|---:|:------------------------------|:-----------------------------|:-----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------|
+|  0 | ['Hej från John Snow Labs! '] | ['Hej från John Snow Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | [[0.4006600081920624,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|entity_recognizer_md|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|sv|
+|Size:|461.9 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_da.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_da.md
index 253d488b819fa2..b51c4758a0cf36 100644
--- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_da.md
+++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_da.md
@@ -28,8 +28,8 @@ It performs most of the common text processing tasks on your dataframe
 {:.btn-box}
 <button class="button button-orange" disabled>Live Demo</button>
 <button class="button button-orange" disabled>Open in Colab</button>
-[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_da_4.4.2_3.4_1685000915897.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
-[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_da_4.4.2_3.4_1685000915897.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_da_4.4.2_3.4_1685056048244.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_da_4.4.2_3.4_1685056048244.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
 
 ## How to use
 
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_es.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_es.md
new file mode 100644
index 00000000000000..d6b573223d9fb9
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_es.md
@@ -0,0 +1,121 @@
+---
+layout: model
+title: Recognize Entities DL Pipeline for Spanish - Small
+author: John Snow Labs
+name: entity_recognizer_sm
+date: 2023-05-25
+tags: [open_source, spanish, entity_recognizer_sm, pipeline, es]
+task: Named Entity Recognition
+language: es
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_es_4.4.2_3.4_1685044640150.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_es_4.4.2_3.4_1685044640150.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'es')
+annotations =  pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "es")
+val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Hola de John Snow Labs! ""]
+result_df = nlu.load('es.ner').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'es')
+annotations =  pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "es")
+val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Hola de John Snow Labs! ""]
+result_df = nlu.load('es.ner').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                     | sentence                    | token                                   | embeddings                   | ner                                    | entities               |
+|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:---------------------------------------|:-----------------------|
+|  0 | ['Hola de John Snow Labs! '] | ['Hola de John Snow Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | [[0.1754499971866607,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'B-MISC'] | ['John Snow', 'Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|entity_recognizer_sm|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|es|
+|Size:|166.7 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_fi.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_fi.md
index 1965380568e49c..b3a87b93f6edbd 100644
--- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_fi.md
+++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_fi.md
@@ -28,8 +28,8 @@ It performs most of the common text processing tasks on your dataframe
 {:.btn-box}
 <button class="button button-orange" disabled>Live Demo</button>
 <button class="button button-orange" disabled>Open in Colab</button>
-[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_fi_4.4.2_3.4_1685002462888.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
-[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_fi_4.4.2_3.4_1685002462888.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_fi_4.4.2_3.4_1685057608991.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_fi_4.4.2_3.4_1685057608991.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
 
 ## How to use
 
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_nl.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_nl.md
new file mode 100644
index 00000000000000..838aeccec01e45
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_nl.md
@@ -0,0 +1,121 @@
+---
+layout: model
+title: Recognize Entities DL Pipeline for Dutch - Small
+author: John Snow Labs
+name: entity_recognizer_sm
+date: 2023-05-25
+tags: [open_source, dutch, entity_recognizer_sm, pipeline, nl]
+task: Named Entity Recognition
+language: nl
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+         It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_nl_4.4.2_3.4_1685051046186.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_nl_4.4.2_3.4_1685051046186.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'nl')
+annotations =  pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "nl")
+val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Hallo van John Snow Labs! ""]
+result_df = nlu.load('nl.ner').predict(text)
+result_df
+    
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'nl')
+annotations =  pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "nl")
+val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Hallo van John Snow Labs! ""]
+result_df = nlu.load('nl.ner').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                       | sentence                      | token                                     | embeddings                   | ner                                   | entities            |
+|---:|:-------------------------------|:------------------------------|:------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------|
+|  0 | ['Hallo van John Snow Labs! '] | ['Hallo van John Snow Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | [[0.3653799891471863,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|entity_recognizer_sm|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|nl|
+|Size:|166.9 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_no.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_no.md
new file mode 100644
index 00000000000000..123e862837174b
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_no.md
@@ -0,0 +1,121 @@
+---
+layout: model
+title: Recognize Entities DL Pipeline for Norwegian (Bokmal) - Small
+author: John Snow Labs
+name: entity_recognizer_sm
+date: 2023-05-25
+tags: [open_source, norwegian_bokmal, entity_recognizer_sm, pipeline, "no"]
+task: Named Entity Recognition
+language: "no"
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_no_4.4.2_3.4_1685052827860.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_no_4.4.2_3.4_1685052827860.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'no')
+annotations =  pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "no")
+val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Hei fra John Snow Labs! ""]
+result_df = nlu.load('no.ner').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'no')
+annotations =  pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "no")
+val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Hei fra John Snow Labs! ""]
+result_df = nlu.load('no.ner').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                     | sentence                    | token                                   | embeddings                   | ner                                   | entities            |
+|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------|
+|  0 | ['Hei fra John Snow Labs! '] | ['Hei fra John Snow Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | [[-0.394499987363815,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|entity_recognizer_sm|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|no|
+|Size:|167.6 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_pl.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_pl.md
new file mode 100644
index 00000000000000..0f83d5a4d2903d
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_pl.md
@@ -0,0 +1,121 @@
+---
+layout: model
+title: Recognize Entities DL Pipeline for Polish - Small
+author: John Snow Labs
+name: entity_recognizer_sm
+date: 2023-05-25
+tags: [open_source, polish, entity_recognizer_sm, pipeline, pl]
+task: Named Entity Recognition
+language: pl
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+         It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_pl_4.4.2_3.4_1685049478793.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_pl_4.4.2_3.4_1685049478793.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'pl')
+annotations =  pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "pl")
+val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Witaj z John Snow Labs! ""]
+result_df = nlu.load('pl.ner').predict(text)
+result_df
+    
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'pl')
+annotations =  pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "pl")
+val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Witaj z John Snow Labs! ""]
+result_df = nlu.load('pl.ner').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                     | sentence                    | token                                   | embeddings                   | ner                                   | entities            |
+|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------|
+|  0 | ['Witaj z John Snow Labs! '] | ['Witaj z John Snow Labs!'] | ['Witaj', 'z', 'John', 'Snow', 'Labs!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|entity_recognizer_sm|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|pl|
+|Size:|167.1 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_pt.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_pt.md
new file mode 100644
index 00000000000000..f9c6d47bbf9089
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_pt.md
@@ -0,0 +1,121 @@
+---
+layout: model
+title: Recognize Entities DL Pipeline for Portuguese - Small
+author: John Snow Labs
+name: entity_recognizer_sm
+date: 2023-05-25
+tags: [open_source, portuguese, entity_recognizer_sm, pipeline, pt]
+task: Named Entity Recognition
+language: pt
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+         It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_pt_4.4.2_3.4_1685047899396.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_pt_4.4.2_3.4_1685047899396.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'pt')
+annotations =  pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "pt")
+val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Olá de John Snow Labs! ""]
+result_df = nlu.load('pt.ner').predict(text)
+result_df
+    
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'pt')
+annotations =  pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "pt")
+val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Olá de John Snow Labs! ""]
+result_df = nlu.load('pt.ner').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                    | sentence                   | token                                  | embeddings                   | ner                                   | entities            |
+|---:|:----------------------------|:---------------------------|:---------------------------------------|:-----------------------------|:--------------------------------------|:--------------------|
+|  0 | ['Olá de John Snow Labs! '] | ['Olá de John Snow Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|entity_recognizer_sm|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|pt|
+|Size:|167.0 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_ru.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_ru.md
new file mode 100644
index 00000000000000..0ed56858781ab1
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_ru.md
@@ -0,0 +1,121 @@
+---
+layout: model
+title: Recognize Entities DL Pipeline for Russian - Small
+author: John Snow Labs
+name: entity_recognizer_sm
+date: 2023-05-25
+tags: [open_source, russian, entity_recognizer_sm, pipeline, ru]
+task: Named Entity Recognition
+language: ru
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_ru_4.4.2_3.4_1685046218620.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_ru_4.4.2_3.4_1685046218620.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'ru')
+annotations =  pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "ru")
+val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Здравствуйте из Джона Снежных Лабораторий! ""]
+result_df = nlu.load('ru.ner').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'ru')
+annotations =  pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "ru")
+val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Здравствуйте из Джона Снежных Лабораторий! ""]
+result_df = nlu.load('ru.ner').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                                        | sentence                                       | token                                                      | embeddings                   | ner                                   | entities                       |
+|---:|:------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------------------|:-----------------------------|:--------------------------------------|:-------------------------------|
+|  0 | ['Здравствуйте из Джона Снежных Лабораторий! '] | ['Здравствуйте из Джона Снежных Лабораторий!'] | ['Здравствуйте', 'из', 'Джона', 'Снежных', 'Лабораторий!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['Джона Снежных Лабораторий!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|entity_recognizer_sm|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|ru|
+|Size:|167.0 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_sv.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_sv.md
new file mode 100644
index 00000000000000..5d8cd94201d0f6
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_sv.md
@@ -0,0 +1,121 @@
+---
+layout: model
+title: Recognize Entities DL Pipeline for Swedish - Small
+author: John Snow Labs
+name: entity_recognizer_sm
+date: 2023-05-25
+tags: [open_source, swedish, entity_recognizer_sm, pipeline, sv]
+task: Named Entity Recognition
+language: sv
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_sv_4.4.2_3.4_1685054487398.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_sv_4.4.2_3.4_1685054487398.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'sv')
+annotations =  pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "sv")
+val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Hej från John Snow Labs! ""]
+result_df = nlu.load('sv.ner').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'sv')
+annotations =  pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "sv")
+val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Hej från John Snow Labs! ""]
+result_df = nlu.load('sv.ner').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                      | sentence                     | token                                    | embeddings                   | ner                                   | entities            |
+|---:|:------------------------------|:-----------------------------|:-----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------|
+|  0 | ['Hej från John Snow Labs! '] | ['Hej från John Snow Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | [[0.0306969992816448,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|entity_recognizer_sm|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|sv|
+|Size:|166.7 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_dl_en.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_dl_en.md
new file mode 100644
index 00000000000000..48ac092cf76157
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_dl_en.md
@@ -0,0 +1,128 @@
+---
+layout: model
+title: Explain Document DL Pipeline for English
+author: John Snow Labs
+name: explain_document_dl
+date: 2023-05-25
+tags: [open_source, english, explain_document_dl, pipeline, en]
+task: Named Entity Recognition
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The explain_document_dl is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps 
+and recognizes entities .
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_dl_en_4.4.2_3.4_1685040992507.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_dl_en_4.4.2_3.4_1685040992507.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline('explain_document_dl', lang = 'en')
+annotations =  pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("explain_document_dl", lang = "en")
+val result = pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = ["The Mona Lisa is an oil painting from the 16th century."]
+result_df = nlu.load('en.explain.dl').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline('explain_document_dl', lang = 'en')
+annotations =  pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("explain_document_dl", lang = "en")
+val result = pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = ["The Mona Lisa is an oil painting from the 16th century."]
+result_df = nlu.load('en.explain.dl').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
++--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------+-----------+
+|                                              text|                                          document|                                          sentence|                                             token|                                           checked|                                             lemma|                                              stem|                                               pos|                                        embeddings|                                         ner|   entities|
++--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------+-----------+
+|The Mona Lisa is an oil painting from the 16th ...|[The Mona Lisa is an oil painting from the 16th...|[The Mona Lisa is an oil painting from the 16th...|[The, Mona, Lisa, is, an, oil, painting, from, ...|[The, Mona, Lisa, is, an, oil, painting, from, ...|[The, Mona, Lisa, be, an, oil, painting, from, ...|[the, mona, lisa, i, an, oil, paint, from, the,...|[DT, NNP, NNP, VBZ, DT, NN, NN, IN, DT, JJ, NN, .]|[[-0.038194, -0.24487, 0.72812, -0.39961, 0.083...|[O, B-PER, I-PER, O, O, O, O, O, O, O, O, O]|[Mona Lisa]|
++--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------+-----------+
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|explain_document_dl|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|176.2 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- NorvigSweetingModel
+- LemmatizerModel
+- Stemmer
+- PerceptronModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_da.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_da.md
index 288382a9d9610d..0ccf156f06db78 100644
--- a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_da.md
+++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_da.md
@@ -29,8 +29,8 @@ It performs most of the common text processing tasks on your dataframe
 {:.btn-box}
 <button class="button button-orange" disabled>Live Demo</button>
 <button class="button button-orange" disabled>Open in Colab</button>
-[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_da_4.4.2_3.4_1684999826923.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
-[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_da_4.4.2_3.4_1684999826923.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_da_4.4.2_3.4_1685054923023.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_da_4.4.2_3.4_1685054923023.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
 
 ## How to use
 
diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_es.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_es.md
new file mode 100644
index 00000000000000..60436844a502a7
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_es.md
@@ -0,0 +1,124 @@
+---
+layout: model
+title: Explain Document pipeline for Spanish (explain_document_lg)
+author: John Snow Labs
+name: explain_document_lg
+date: 2023-05-25
+tags: [open_source, spanish, explain_document_lg, pipeline, es]
+task: Named Entity Recognition
+language: es
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps 
+and recognizes entities .
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_es_4.4.2_3.4_1685043480517.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_es_4.4.2_3.4_1685043480517.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_lg', lang = 'es')
+annotations =  pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("explain_document_lg", lang = "es")
+val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Hola de John Snow Labs! ""]
+result_df = nlu.load('es.explain.lg').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_lg', lang = 'es')
+annotations =  pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("explain_document_lg", lang = "es")
+val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Hola de John Snow Labs! ""]
+result_df = nlu.load('es.explain.lg').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                     | sentence                    | token                                   | lemma                                   | pos                                        | embeddings                   | ner                                   | entities            |
+|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------|
+|  0 | ['Hola de John Snow Labs! '] | ['Hola de John Snow Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['PART', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[-0.016199000179767,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|explain_document_lg|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|es|
+|Size:|2.5 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- LemmatizerModel
+- PerceptronModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_fi.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_fi.md
index 680c5e0c3550cb..159abee951a035 100644
--- a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_fi.md
+++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_fi.md
@@ -29,8 +29,8 @@ It performs most of the common text processing tasks on your dataframe
 {:.btn-box}
 <button class="button button-orange" disabled>Live Demo</button>
 <button class="button button-orange" disabled>Open in Colab</button>
-[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_fi_4.4.2_3.4_1685001344456.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
-[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_fi_4.4.2_3.4_1685001344456.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_fi_4.4.2_3.4_1685056502478.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_fi_4.4.2_3.4_1685056502478.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
 
 ## How to use
 
diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_nl.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_nl.md
new file mode 100644
index 00000000000000..c1ae88f159d9af
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_nl.md
@@ -0,0 +1,124 @@
+---
+layout: model
+title: Explain Document pipeline for Dutch (explain_document_lg)
+author: John Snow Labs
+name: explain_document_lg
+date: 2023-05-25
+tags: [open_source, dutch, explain_document_lg, pipeline, nl]
+task: Named Entity Recognition
+language: nl
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps 
+        and recognizes entities .
+         It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_nl_4.4.2_3.4_1685049928456.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_nl_4.4.2_3.4_1685049928456.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_lg', lang = 'nl')
+annotations =  pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("explain_document_lg", lang = "nl")
+val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Hallo van John Snow Labs! ""]
+result_df = nlu.load('nl.explain.lg').predict(text)
+result_df
+    
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_lg', lang = 'nl')
+annotations =  pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("explain_document_lg", lang = "nl")
+val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Hallo van John Snow Labs! ""]
+result_df = nlu.load('nl.explain.lg').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                       | sentence                      | token                                     | lemma                                     | pos                                         | embeddings                   | ner                                       | entities                     |
+|---:|:-------------------------------|:------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------------|:-----------------------------|:------------------------------------------|:-----------------------------|
+|  0 | ['Hallo van John Snow Labs! '] | ['Hallo van John Snow Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[-0.245989993214607,.,...]] | ['B-PER', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['Hallo', 'John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|explain_document_lg|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|nl|
+|Size:|2.5 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- LemmatizerModel
+- PerceptronModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_no.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_no.md
new file mode 100644
index 00000000000000..43f95fdfc89ad0
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_no.md
@@ -0,0 +1,124 @@
+---
+layout: model
+title: Explain Document pipeline for Norwegian (Bokmal) (explain_document_lg)
+author: John Snow Labs
+name: explain_document_lg
+date: 2023-05-25
+tags: [open_source, norwegian_bokmal, explain_document_lg, pipeline, "no"]
+task: Named Entity Recognition
+language: "no"
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps 
+and recognizes entities .
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_no_4.4.2_3.4_1685051715152.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_no_4.4.2_3.4_1685051715152.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_lg', lang = 'no')
+annotations =  pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("explain_document_lg", lang = "no")
+val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Hei fra John Snow Labs! ""]
+result_df = nlu.load('no.explain.lg').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_lg', lang = 'no')
+annotations =  pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("explain_document_lg", lang = "no")
+val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Hei fra John Snow Labs! ""]
+result_df = nlu.load('no.explain.lg').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                     | sentence                    | token                                   | lemma                                   | pos                                         | embeddings                   | ner                                    | entities               |
+|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:--------------------------------------------|:-----------------------------|:---------------------------------------|:-----------------------|
+|  0 | ['Hei fra John Snow Labs! '] | ['Hei fra John Snow Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0639619976282119,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'B-PROD'] | ['John Snow', 'Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|explain_document_lg|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|no|
+|Size:|2.5 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- LemmatizerModel
+- PerceptronModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_pl.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_pl.md
new file mode 100644
index 00000000000000..b7a4d9d8b2f91e
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_pl.md
@@ -0,0 +1,124 @@
+---
+layout: model
+title: Explain Document pipeline for Polish (explain_document_lg)
+author: John Snow Labs
+name: explain_document_lg
+date: 2023-05-25
+tags: [open_source, polish, explain_document_lg, pipeline, pl]
+task: Named Entity Recognition
+language: pl
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps 
+        and recognizes entities .
+         It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_pl_4.4.2_3.4_1685048347765.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_pl_4.4.2_3.4_1685048347765.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_lg', lang = 'pl')
+annotations =  pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("explain_document_lg", lang = "pl")
+val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Witaj z John Snow Labs! ""]
+result_df = nlu.load('pl.explain.lg').predict(text)
+result_df
+    
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_lg', lang = 'pl')
+annotations =  pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("explain_document_lg", lang = "pl")
+val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Witaj z John Snow Labs! ""]
+result_df = nlu.load('pl.explain.lg').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                     | sentence                    | token                                   | lemma                                   | pos                                        | embeddings                   | ner                                   | entities            |
+|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------|
+|  0 | ['Witaj z John Snow Labs! '] | ['Witaj z John Snow Labs!'] | ['Witaj', 'z', 'John', 'Snow', 'Labs!'] | ['witać', 'z', 'John', 'Snow', 'Labs!'] | ['VERB', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.4977500140666961,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|explain_document_lg|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|pl|
+|Size:|2.5 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- LemmatizerModel
+- PerceptronModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_pt.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_pt.md
new file mode 100644
index 00000000000000..1a8369c1d8560c
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_pt.md
@@ -0,0 +1,124 @@
+---
+layout: model
+title: Explain Document pipeline for Portuguese (explain_document_lg)
+author: John Snow Labs
+name: explain_document_lg
+date: 2023-05-25
+tags: [open_source, portuguese, explain_document_lg, pipeline, pt]
+task: Named Entity Recognition
+language: pt
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps 
+        and recognizes entities .
+         It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_pt_4.4.2_3.4_1685046739453.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_pt_4.4.2_3.4_1685046739453.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_lg', lang = 'pt')
+annotations =  pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("explain_document_lg", lang = "pt")
+val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Olá de John Snow Labs! ""]
+result_df = nlu.load('pt.explain.lg').predict(text)
+result_df
+    
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_lg', lang = 'pt')
+annotations =  pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("explain_document_lg", lang = "pt")
+val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Olá de John Snow Labs! ""]
+result_df = nlu.load('pt.explain.lg').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                    | sentence                   | token                                  | lemma                                  | pos                                         | embeddings                   | ner                                   | entities            |
+|---:|:----------------------------|:---------------------------|:---------------------------------------|:---------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------|
+|  0 | ['Olá de John Snow Labs! '] | ['Olá de John Snow Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.4388400018215179,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|explain_document_lg|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|pt|
+|Size:|2.5 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- LemmatizerModel
+- PerceptronModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_ru.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_ru.md
new file mode 100644
index 00000000000000..4aef430d1f1da0
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_ru.md
@@ -0,0 +1,124 @@
+---
+layout: model
+title: Explain Document pipeline for Russian (explain_document_lg)
+author: John Snow Labs
+name: explain_document_lg
+date: 2023-05-25
+tags: [open_source, russian, explain_document_lg, pipeline, ru]
+task: Named Entity Recognition
+language: ru
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps 
+and recognizes entities .
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_ru_4.4.2_3.4_1685045082115.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_ru_4.4.2_3.4_1685045082115.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_lg', lang = 'ru')
+annotations =  pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("explain_document_lg", lang = "ru")
+val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Здравствуйте из Джона Снежных Лабораторий! ""]
+result_df = nlu.load('ru.explain.lg').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_lg', lang = 'ru')
+annotations =  pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("explain_document_lg", lang = "ru")
+val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Здравствуйте из Джона Снежных Лабораторий! ""]
+result_df = nlu.load('ru.explain.lg').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                                        | sentence                                       | token                                                      | lemma                                                      | pos                                        | embeddings                   | ner                                   | entities                       |
+|---:|:------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------------------|:-----------------------------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:-------------------------------|
+|  0 | ['Здравствуйте из Джона Снежных Лабораторий! '] | ['Здравствуйте из Джона Снежных Лабораторий!'] | ['Здравствуйте', 'из', 'Джона', 'Снежных', 'Лабораторий!'] | ['здравствовать', 'из', 'Джон', 'Снежных', 'Лабораторий!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['Джона Снежных Лабораторий!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|explain_document_lg|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|ru|
+|Size:|2.5 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- LemmatizerModel
+- PerceptronModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_sv.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_sv.md
new file mode 100644
index 00000000000000..61113b16764d0a
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_sv.md
@@ -0,0 +1,124 @@
+---
+layout: model
+title: Explain Document pipeline for Swedish (explain_document_lg)
+author: John Snow Labs
+name: explain_document_lg
+date: 2023-05-25
+tags: [open_source, swedish, explain_document_lg, pipeline, sv]
+task: Named Entity Recognition
+language: sv
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps 
+and recognizes entities .
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_sv_4.4.2_3.4_1685053346707.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_sv_4.4.2_3.4_1685053346707.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_lg', lang = 'sv')
+annotations =  pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("explain_document_lg", lang = "sv")
+val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Hej från John Snow Labs! ""]
+result_df = nlu.load('sv.explain.lg').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_lg', lang = 'sv')
+annotations =  pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("explain_document_lg", lang = "sv")
+val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Hej från John Snow Labs! ""]
+result_df = nlu.load('sv.explain.lg').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                      | sentence                     | token                                    | lemma                                    | pos                                        | embeddings                   | ner                                   | entities            |
+|---:|:------------------------------|:-----------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------|
+|  0 | ['Hej från John Snow Labs! '] | ['Hej från John Snow Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0306969992816448,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|explain_document_lg|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|sv|
+|Size:|2.5 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- LemmatizerModel
+- PerceptronModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_da.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_da.md
index ca902b34fb791e..fbfeb598e42d42 100644
--- a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_da.md
+++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_da.md
@@ -28,8 +28,8 @@ It performs most of the common text processing tasks on your dataframe
 {:.btn-box}
 <button class="button button-orange" disabled>Live Demo</button>
 <button class="button button-orange" disabled>Open in Colab</button>
-[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_da_4.4.2_3.4_1685000055604.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
-[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_da_4.4.2_3.4_1685000055604.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_da_4.4.2_3.4_1685055174051.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_da_4.4.2_3.4_1685055174051.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
 
 ## How to use
 
diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_de.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_de.md
new file mode 100644
index 00000000000000..61a671d663cc95
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_de.md
@@ -0,0 +1,123 @@
+---
+layout: model
+title: Explain Document Pipeline for German
+author: John Snow Labs
+name: explain_document_md
+date: 2023-05-25
+tags: [open_source, german, explain_document_md, pipeline, de]
+task: Named Entity Recognition
+language: de
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_de_4.4.2_3.4_1685041632110.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_de_4.4.2_3.4_1685041632110.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_md', lang = 'de')
+annotations =  pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("explain_document_md", lang = "de")
+val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Hallo aus John Snow Labs! ""]
+result_df = nlu.load('de.explain.document').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_md', lang = 'de')
+annotations =  pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("explain_document_md", lang = "de")
+val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Hallo aus John Snow Labs! ""]
+result_df = nlu.load('de.explain.document').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                       | sentence                      | token                                     | lemma                                     | pos                                        | embeddings                   | ner                                   | entities            |
+|---:|:-------------------------------|:------------------------------|:------------------------------------------|:------------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------|
+|  0 | ['Hallo aus John Snow Labs! '] | ['Hallo aus John Snow Labs!'] | ['Hallo', 'aus', 'John', 'Snow', 'Labs!'] | ['Hallo', 'aus', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.5910000205039978,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|explain_document_md|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|de|
+|Size:|470.9 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- LemmatizerModel
+- PerceptronModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_es.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_es.md
new file mode 100644
index 00000000000000..750f307ec55d0e
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_es.md
@@ -0,0 +1,123 @@
+---
+layout: model
+title: Explain Document Pipeline for Spanish
+author: John Snow Labs
+name: explain_document_md
+date: 2023-05-25
+tags: [open_source, spanish, explain_document_md, pipeline, es]
+task: Named Entity Recognition
+language: es
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_es_4.4.2_3.4_1685043736525.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_es_4.4.2_3.4_1685043736525.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_md', lang = 'es')
+annotations =  pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("explain_document_md", lang = "es")
+val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Hola de John Snow Labs! ""]
+result_df = nlu.load('es.explain.md').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_md', lang = 'es')
+annotations =  pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("explain_document_md", lang = "es")
+val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Hola de John Snow Labs! ""]
+result_df = nlu.load('es.explain.md').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                     | sentence                    | token                                   | lemma                                   | pos                                        | embeddings                   | ner                                   | entities            |
+|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------|
+|  0 | ['Hola de John Snow Labs! '] | ['Hola de John Snow Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['PART', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.5123000144958496,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|explain_document_md|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|es|
+|Size:|472.5 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- LemmatizerModel
+- PerceptronModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_fi.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_fi.md
index c6f312c9fc6ece..a5a183487a9fd5 100644
--- a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_fi.md
+++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_fi.md
@@ -28,8 +28,8 @@ It performs most of the common text processing tasks on your dataframe
 {:.btn-box}
 <button class="button button-orange" disabled>Live Demo</button>
 <button class="button button-orange" disabled>Open in Colab</button>
-[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_fi_4.4.2_3.4_1685001588322.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
-[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_fi_4.4.2_3.4_1685001588322.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_fi_4.4.2_3.4_1685056729372.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_fi_4.4.2_3.4_1685056729372.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
 
 ## How to use
 
diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_fr.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_fr.md
new file mode 100644
index 00000000000000..4e3bd64d36f4b1
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_fr.md
@@ -0,0 +1,123 @@
+---
+layout: model
+title: Explain Document Pipeline for French
+author: John Snow Labs
+name: explain_document_md
+date: 2023-05-25
+tags: [open_source, french, explain_document_md, pipeline, fr]
+task: Named Entity Recognition
+language: fr
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_fr_4.4.2_3.4_1685035526859.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_fr_4.4.2_3.4_1685035526859.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_md', lang = 'fr')
+annotations =  pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("explain_document_md", lang = "fr")
+val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Bonjour de John Snow Labs! ""]
+result_df = nlu.load('fr.explain.md').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_md', lang = 'fr')
+annotations =  pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("explain_document_md", lang = "fr")
+val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Bonjour de John Snow Labs! ""]
+result_df = nlu.load('fr.explain.md').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                        | sentence                       | token                                      | lemma                                      | pos                                        | embeddings                   | ner                                        | entities                       |
+|---:|:--------------------------------|:-------------------------------|:-------------------------------------------|:-------------------------------------------|:-------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------------------|
+|  0 | ['Bonjour de John Snow Labs! '] | ['Bonjour de John Snow Labs!'] | ['Bonjour', 'de', 'John', 'Snow', 'Labs!'] | ['Bonjour', 'de', 'John', 'Snow', 'Labs!'] | ['INTJ', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0783179998397827,.,...]] | ['I-MISC', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['Bonjour', 'John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|explain_document_md|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|fr|
+|Size:|467.6 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- LemmatizerModel
+- PerceptronModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_it.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_it.md
new file mode 100644
index 00000000000000..2964ed3333b5c5
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_it.md
@@ -0,0 +1,123 @@
+---
+layout: model
+title: Explain Document Pipeline for Italian
+author: John Snow Labs
+name: explain_document_md
+date: 2023-05-25
+tags: [open_source, italian, explain_document_md, pipeline, it]
+task: Named Entity Recognition
+language: it
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+         It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_it_4.4.2_3.4_1685038565599.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_it_4.4.2_3.4_1685038565599.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_md', lang = 'it')
+annotations =  pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("explain_document_md", lang = "it")
+val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Ciao da John Snow Labs! ""]
+result_df = nlu.load('it.explain.document').predict(text)
+result_df
+    
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_md', lang = 'it')
+annotations =  pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("explain_document_md", lang = "it")
+val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Ciao da John Snow Labs! ""]
+result_df = nlu.load('it.explain.document').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                     | sentence                    | token                                   | lemma                                   | pos                                        | embeddings                   | ner                                   | entities            |
+|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------|
+|  0 | ['Ciao da John Snow Labs! '] | ['Ciao da John Snow Labs!'] | ['Ciao', 'da', 'John', 'Snow', 'Labs!'] | ['Ciao', 'da', 'John', 'Snow', 'Labs!'] | ['VERB', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[-0.146050006151199,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|explain_document_md|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|it|
+|Size:|468.1 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- LemmatizerModel
+- PerceptronModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_nl.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_nl.md
new file mode 100644
index 00000000000000..45db3304164c2d
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_nl.md
@@ -0,0 +1,123 @@
+---
+layout: model
+title: Explain Document Pipeline for Dutch
+author: John Snow Labs
+name: explain_document_md
+date: 2023-05-25
+tags: [open_source, dutch, explain_document_md, pipeline, nl]
+task: Named Entity Recognition
+language: nl
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+         It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_nl_4.4.2_3.4_1685050177103.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_nl_4.4.2_3.4_1685050177103.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_md', lang = 'nl')
+annotations =  pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("explain_document_md", lang = "nl")
+val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Hallo van John Snow Labs! ""]
+result_df = nlu.load('nl.explain.md').predict(text)
+result_df
+    
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_md', lang = 'nl')
+annotations =  pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("explain_document_md", lang = "nl")
+val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Hallo van John Snow Labs! ""]
+result_df = nlu.load('nl.explain.md').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                       | sentence                      | token                                     | lemma                                     | pos                                         | embeddings                   | ner                                   | entities            |
+|---:|:-------------------------------|:------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------|
+|  0 | ['Hallo van John Snow Labs! '] | ['Hallo van John Snow Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.5910000205039978,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|explain_document_md|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|nl|
+|Size:|464.9 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- LemmatizerModel
+- PerceptronModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_no.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_no.md
new file mode 100644
index 00000000000000..ed848865a3f17e
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_no.md
@@ -0,0 +1,123 @@
+---
+layout: model
+title: Explain Document Pipeline for Norwegian (Bokmal)
+author: John Snow Labs
+name: explain_document_md
+date: 2023-05-25
+tags: [open_source, norwegian_bokmal, explain_document_md, pipeline, "no"]
+task: Named Entity Recognition
+language: "no"
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_no_4.4.2_3.4_1685051937316.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_no_4.4.2_3.4_1685051937316.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_md', lang = 'no')
+annotations =  pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("explain_document_md", lang = "no")
+val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Hei fra John Snow Labs! ""]
+result_df = nlu.load('no.explain.md').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_md', lang = 'no')
+annotations =  pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("explain_document_md", lang = "no")
+val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Hei fra John Snow Labs! ""]
+result_df = nlu.load('no.explain.md').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                     | sentence                    | token                                   | lemma                                   | pos                                         | embeddings                   | ner                                   | entities            |
+|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------|
+|  0 | ['Hei fra John Snow Labs! '] | ['Hei fra John Snow Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.1868100017309188,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|explain_document_md|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|no|
+|Size:|465.3 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- LemmatizerModel
+- PerceptronModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_pl.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_pl.md
new file mode 100644
index 00000000000000..fde99cd1e8891f
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_pl.md
@@ -0,0 +1,123 @@
+---
+layout: model
+title: Explain Document Pipeline for Polish
+author: John Snow Labs
+name: explain_document_md
+date: 2023-05-25
+tags: [open_source, polish, explain_document_md, pipeline, pl]
+task: Named Entity Recognition
+language: pl
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+         It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_pl_4.4.2_3.4_1685048596474.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_pl_4.4.2_3.4_1685048596474.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_md', lang = 'pl')
+annotations =  pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("explain_document_md", lang = "pl")
+val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Witaj z John Snow Labs! ""]
+result_df = nlu.load('pl.explain.md').predict(text)
+result_df
+    
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_md', lang = 'pl')
+annotations =  pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("explain_document_md", lang = "pl")
+val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Witaj z John Snow Labs! ""]
+result_df = nlu.load('pl.explain.md').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                     | sentence                    | token                                   | lemma                                   | pos                                        | embeddings                   | ner                                   | entities            |
+|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------|
+|  0 | ['Witaj z John Snow Labs! '] | ['Witaj z John Snow Labs!'] | ['Witaj', 'z', 'John', 'Snow', 'Labs!'] | ['witać', 'z', 'John', 'Snow', 'Labs!'] | ['VERB', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|explain_document_md|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|pl|
+|Size:|464.8 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- LemmatizerModel
+- PerceptronModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_pt.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_pt.md
new file mode 100644
index 00000000000000..6ff7d29502f92f
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_pt.md
@@ -0,0 +1,123 @@
+---
+layout: model
+title: Explain Document Pipeline for Portuguese
+author: John Snow Labs
+name: explain_document_md
+date: 2023-05-25
+tags: [open_source, portuguese, explain_document_md, pipeline, pt]
+task: Named Entity Recognition
+language: pt
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+         It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_pt_4.4.2_3.4_1685046975096.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_pt_4.4.2_3.4_1685046975096.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_md', lang = 'pt')
+annotations =  pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("explain_document_md", lang = "pt")
+val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Olá de John Snow Labs! ""]
+result_df = nlu.load('pt.explain.md').predict(text)
+result_df
+    
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_md', lang = 'pt')
+annotations =  pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("explain_document_md", lang = "pt")
+val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Olá de John Snow Labs! ""]
+result_df = nlu.load('pt.explain.md').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                    | sentence                   | token                                  | lemma                                  | pos                                         | embeddings                   | ner                                   | entities            |
+|---:|:----------------------------|:---------------------------|:---------------------------------------|:---------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------|
+|  0 | ['Olá de John Snow Labs! '] | ['Olá de John Snow Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|explain_document_md|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|pt|
+|Size:|473.9 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- LemmatizerModel
+- PerceptronModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_ru.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_ru.md
new file mode 100644
index 00000000000000..10741f307e1adf
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_ru.md
@@ -0,0 +1,123 @@
+---
+layout: model
+title: Explain Document Pipeline for Russian
+author: John Snow Labs
+name: explain_document_md
+date: 2023-05-25
+tags: [open_source, russian, explain_document_md, pipeline, ru]
+task: Named Entity Recognition
+language: ru
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_ru_4.4.2_3.4_1685045315176.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_ru_4.4.2_3.4_1685045315176.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_md', lang = 'ru')
+annotations =  pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("explain_document_md", lang = "ru")
+val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Здравствуйте из Джона Снежных Лабораторий! ""]
+result_df = nlu.load('ru.explain.md').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_md', lang = 'ru')
+annotations =  pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("explain_document_md", lang = "ru")
+val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Здравствуйте из Джона Снежных Лабораторий! ""]
+result_df = nlu.load('ru.explain.md').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                                        | sentence                                       | token                                                      | lemma                                                      | pos                                        | embeddings                   | ner                                   | entities                       |
+|---:|:------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------------------|:-----------------------------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:-------------------------------|
+|  0 | ['Здравствуйте из Джона Снежных Лабораторий! '] | ['Здравствуйте из Джона Снежных Лабораторий!'] | ['Здравствуйте', 'из', 'Джона', 'Снежных', 'Лабораторий!'] | ['здравствовать', 'из', 'Джон', 'Снежных', 'Лабораторий!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-LOC', 'I-LOC', 'I-LOC'] | ['Джона Снежных Лабораторий!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|explain_document_md|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|ru|
+|Size:|465.5 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- LemmatizerModel
+- PerceptronModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_sv.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_sv.md
new file mode 100644
index 00000000000000..bd082756480915
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_sv.md
@@ -0,0 +1,123 @@
+---
+layout: model
+title: Explain Document Pipeline for Swedish
+author: John Snow Labs
+name: explain_document_md
+date: 2023-05-25
+tags: [open_source, swedish, explain_document_md, pipeline, sv]
+task: Named Entity Recognition
+language: sv
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_sv_4.4.2_3.4_1685053591124.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_sv_4.4.2_3.4_1685053591124.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_md', lang = 'sv')
+annotations =  pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("explain_document_md", lang = "sv")
+val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Hej från John Snow Labs! ""]
+result_df = nlu.load('sv.explain.md').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_md', lang = 'sv')
+annotations =  pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("explain_document_md", lang = "sv")
+val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Hej från John Snow Labs! ""]
+result_df = nlu.load('sv.explain.md').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                      | sentence                     | token                                    | lemma                                    | pos                                        | embeddings                   | ner                                   | entities            |
+|---:|:------------------------------|:-----------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------|
+|  0 | ['Hej från John Snow Labs! '] | ['Hej från John Snow Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.4006600081920624,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|explain_document_md|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|sv|
+|Size:|471.1 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- LemmatizerModel
+- PerceptronModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_ml_en.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_ml_en.md
new file mode 100644
index 00000000000000..ab3e62efb1a1d2
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_ml_en.md
@@ -0,0 +1,123 @@
+---
+layout: model
+title: Explain Document ML Pipeline for English
+author: John Snow Labs
+name: explain_document_ml
+date: 2023-05-25
+tags: [open_source, english, explain_document_ml, pipeline, en]
+task: Named Entity Recognition
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The explain_document_ml is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps 
+and recognizes entities .
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_ml_en_4.4.2_3.4_1685040778279.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_ml_en_4.4.2_3.4_1685040778279.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline('explain_document_ml', lang = 'en')
+annotations =  pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0]
+annotations.keys()
+```
+```scala
+
+
+val pipeline = new PretrainedPipeline("explain_document_ml", lang = "en")
+val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0)
+```
+
+{:.nlu-block}
+```python
+
+
+import nlu
+text = [""Hello from John Snow Labs ! ""]
+result_df = nlu.load('en.explain').predict(text)
+result_df
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline('explain_document_ml', lang = 'en')
+annotations =  pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("explain_document_ml", lang = "en")
+val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Hello from John Snow Labs ! ""]
+result_df = nlu.load('en.explain').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
+|    | document                         | sentence                         | token                                            | spell                                           | lemmas                                          | stems                                          | pos                                    |
+|---:|:---------------------------------|:---------------------------------|:-------------------------------------------------|:------------------------------------------------|:------------------------------------------------|:-----------------------------------------------|:---------------------------------------|
+|  0 | ['Hello fronm John Snwow Labs!'] | ['Hello fronm John Snwow Labs!'] | ['Hello', 'fronm', 'John', 'Snwow', 'Labs', '!'] | ['Hello', 'front', 'John', 'Snow', 'Labs', '!'] | ['Hello', 'front', 'John', 'Snow', 'Labs', '!'] | ['hello', 'front', 'john', 'snow', 'lab', '!'] | ['UH', 'NN', 'NNP', 'NNP', 'NNP', '.'] ||    | document   | sentence   | token     | spell     | lemmas    | stems     | pos    |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|explain_document_ml|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|9.5 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- NorvigSweetingModel
+- LemmatizerModel
+- Stemmer
+- PerceptronModel
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_da.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_da.md
index aa006a5390110b..3bdacc90f82667 100644
--- a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_da.md
+++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_da.md
@@ -28,8 +28,8 @@ It performs most of the common text processing tasks on your dataframe
 {:.btn-box}
 <button class="button button-orange" disabled>Live Demo</button>
 <button class="button button-orange" disabled>Open in Colab</button>
-[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_da_4.4.2_3.4_1685000168570.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
-[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_da_4.4.2_3.4_1685000168570.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_da_4.4.2_3.4_1685055287722.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_da_4.4.2_3.4_1685055287722.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
 
 ## How to use
 
diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_es.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_es.md
new file mode 100644
index 00000000000000..38a2e76ff4e209
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_es.md
@@ -0,0 +1,123 @@
+---
+layout: model
+title: Explain Document Pipeline for Spanish
+author: John Snow Labs
+name: explain_document_sm
+date: 2023-05-25
+tags: [open_source, spanish, explain_document_sm, pipeline, es]
+task: Named Entity Recognition
+language: es
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_es_4.4.2_3.4_1685043872827.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_es_4.4.2_3.4_1685043872827.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_sm', lang = 'es')
+annotations =  pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("explain_document_sm", lang = "es")
+val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Hola de John Snow Labs! ""]
+result_df = nlu.load('es.explain').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_sm', lang = 'es')
+annotations =  pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("explain_document_sm", lang = "es")
+val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Hola de John Snow Labs! ""]
+result_df = nlu.load('es.explain').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                     | sentence                    | token                                   | lemma                                   | pos                                        | embeddings                   | ner                                    | entities               |
+|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:---------------------------------------|:-----------------------|
+|  0 | ['Hola de John Snow Labs! '] | ['Hola de John Snow Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['PART', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.1754499971866607,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'B-MISC'] | ['John Snow', 'Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|explain_document_sm|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|es|
+|Size:|177.4 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- LemmatizerModel
+- PerceptronModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_fi.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_fi.md
index 2c72443ea6ba1c..be1359fc083280 100644
--- a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_fi.md
+++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_fi.md
@@ -28,8 +28,8 @@ It performs most of the common text processing tasks on your dataframe
 {:.btn-box}
 <button class="button button-orange" disabled>Live Demo</button>
 <button class="button button-orange" disabled>Open in Colab</button>
-[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_fi_4.4.2_3.4_1685001706482.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
-[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_fi_4.4.2_3.4_1685001706482.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_fi_4.4.2_3.4_1685056849986.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_fi_4.4.2_3.4_1685056849986.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
 
 ## How to use
 
diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_nl.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_nl.md
new file mode 100644
index 00000000000000..1efa36b19fec31
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_nl.md
@@ -0,0 +1,123 @@
+---
+layout: model
+title: Explain Document Pipeline for Dutch
+author: John Snow Labs
+name: explain_document_sm
+date: 2023-05-25
+tags: [open_source, dutch, explain_document_sm, pipeline, nl]
+task: Named Entity Recognition
+language: nl
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+         It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_nl_4.4.2_3.4_1685050292506.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_nl_4.4.2_3.4_1685050292506.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_sm', lang = 'nl')
+annotations =  pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("explain_document_sm", lang = "nl")
+val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Hallo van John Snow Labs! ""]
+result_df = nlu.load('nl.explain').predict(text)
+result_df
+    
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_sm', lang = 'nl')
+annotations =  pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("explain_document_sm", lang = "nl")
+val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Hallo van John Snow Labs! ""]
+result_df = nlu.load('nl.explain').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                       | sentence                      | token                                     | lemma                                     | pos                                         | embeddings                   | ner                                   | entities            |
+|---:|:-------------------------------|:------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------|
+|  0 | ['Hallo van John Snow Labs! '] | ['Hallo van John Snow Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.3653799891471863,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|explain_document_sm|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|nl|
+|Size:|169.8 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- LemmatizerModel
+- PerceptronModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_no.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_no.md
new file mode 100644
index 00000000000000..1e76fef347c49a
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_no.md
@@ -0,0 +1,123 @@
+---
+layout: model
+title: Explain Document Pipeline for Norwegian (Bokmal)
+author: John Snow Labs
+name: explain_document_sm
+date: 2023-05-25
+tags: [open_source, norwegian_bokmal, explain_document_sm, pipeline, "no"]
+task: Named Entity Recognition
+language: "no"
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_no_4.4.2_3.4_1685052052087.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_no_4.4.2_3.4_1685052052087.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_sm', lang = 'no')
+annotations =  pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("explain_document_sm", lang = "no")
+val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Hei fra John Snow Labs! ""]
+result_df = nlu.load('no.explain').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_sm', lang = 'no')
+annotations =  pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("explain_document_sm", lang = "no")
+val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Hei fra John Snow Labs! ""]
+result_df = nlu.load('no.explain').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                     | sentence                    | token                                   | lemma                                   | pos                                         | embeddings                   | ner                                   | entities            |
+|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------|
+|  0 | ['Hei fra John Snow Labs! '] | ['Hei fra John Snow Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[-0.394499987363815,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|explain_document_sm|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|no|
+|Size:|170.2 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- LemmatizerModel
+- PerceptronModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_pl.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_pl.md
new file mode 100644
index 00000000000000..1aafb36a4573b7
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_pl.md
@@ -0,0 +1,123 @@
+---
+layout: model
+title: Explain Document Pipeline for Polish
+author: John Snow Labs
+name: explain_document_sm
+date: 2023-05-25
+tags: [open_source, polish, explain_document_sm, pipeline, pl]
+task: Named Entity Recognition
+language: pl
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+         It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_pl_4.4.2_3.4_1685048715273.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_pl_4.4.2_3.4_1685048715273.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_sm', lang = 'pl')
+annotations =  pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("explain_document_sm", lang = "pl")
+val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Witaj z John Snow Labs! ""]
+result_df = nlu.load('pl.explain').predict(text)
+result_df
+    
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_sm', lang = 'pl')
+annotations =  pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("explain_document_sm", lang = "pl")
+val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Witaj z John Snow Labs! ""]
+result_df = nlu.load('pl.explain').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                     | sentence                    | token                                   | lemma                                   | pos                                        | embeddings                   | ner                                   | entities            |
+|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------|
+|  0 | ['Witaj z John Snow Labs! '] | ['Witaj z John Snow Labs!'] | ['Witaj', 'z', 'John', 'Snow', 'Labs!'] | ['witać', 'z', 'John', 'Snow', 'Labs!'] | ['VERB', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|explain_document_sm|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|pl|
+|Size:|169.7 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- LemmatizerModel
+- PerceptronModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_pt.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_pt.md
new file mode 100644
index 00000000000000..941b5d493829bc
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_pt.md
@@ -0,0 +1,123 @@
+---
+layout: model
+title: Explain Document Pipeline for Portuguese
+author: John Snow Labs
+name: explain_document_sm
+date: 2023-05-25
+tags: [open_source, portuguese, explain_document_sm, pipeline, pt]
+task: Named Entity Recognition
+language: pt
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+         It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_pt_4.4.2_3.4_1685047107909.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_pt_4.4.2_3.4_1685047107909.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_sm', lang = 'pt')
+annotations =  pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("explain_document_sm", lang = "pt")
+val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Olá de John Snow Labs! ""]
+result_df = nlu.load('pt.explain').predict(text)
+result_df
+    
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_sm', lang = 'pt')
+annotations =  pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("explain_document_sm", lang = "pt")
+val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Olá de John Snow Labs! ""]
+result_df = nlu.load('pt.explain').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                    | sentence                   | token                                  | lemma                                  | pos                                         | embeddings                   | ner                                   | entities            |
+|---:|:----------------------------|:---------------------------|:---------------------------------------|:---------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------|
+|  0 | ['Olá de John Snow Labs! '] | ['Olá de John Snow Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|explain_document_sm|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|pt|
+|Size:|178.7 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- LemmatizerModel
+- PerceptronModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_ru.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_ru.md
new file mode 100644
index 00000000000000..73af8d334fe53d
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_ru.md
@@ -0,0 +1,123 @@
+---
+layout: model
+title: Explain Document Pipeline for Russian
+author: John Snow Labs
+name: explain_document_sm
+date: 2023-05-25
+tags: [open_source, russian, explain_document_sm, pipeline, ru]
+task: Named Entity Recognition
+language: ru
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_ru_4.4.2_3.4_1685045434988.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_ru_4.4.2_3.4_1685045434988.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_sm', lang = 'ru')
+annotations =  pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("explain_document_sm", lang = "ru")
+val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Здравствуйте из Джона Снежных Лабораторий! ""]
+result_df = nlu.load('ru.explain').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_sm', lang = 'ru')
+annotations =  pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("explain_document_sm", lang = "ru")
+val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Здравствуйте из Джона Снежных Лабораторий! ""]
+result_df = nlu.load('ru.explain').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                                        | sentence                                       | token                                                      | lemma                                                      | pos                                        | embeddings                   | ner                                   | entities                       |
+|---:|:------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------------------|:-----------------------------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:-------------------------------|
+|  0 | ['Здравствуйте из Джона Снежных Лабораторий! '] | ['Здравствуйте из Джона Снежных Лабораторий!'] | ['Здравствуйте', 'из', 'Джона', 'Снежных', 'Лабораторий!'] | ['здравствовать', 'из', 'Джон', 'Снежных', 'Лабораторий!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['Джона Снежных Лабораторий!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|explain_document_sm|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|ru|
+|Size:|170.3 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- LemmatizerModel
+- PerceptronModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_sv.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_sv.md
new file mode 100644
index 00000000000000..014650d0410a7b
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_sv.md
@@ -0,0 +1,123 @@
+---
+layout: model
+title: Explain Document Pipeline for Swedish
+author: John Snow Labs
+name: explain_document_sm
+date: 2023-05-25
+tags: [open_source, swedish, explain_document_sm, pipeline, sv]
+task: Named Entity Recognition
+language: sv
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_sv_4.4.2_3.4_1685053721244.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_sv_4.4.2_3.4_1685053721244.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_sm', lang = 'sv')
+annotations =  pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("explain_document_sm", lang = "sv")
+val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Hej från John Snow Labs! ""]
+result_df = nlu.load('sv.explain').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_sm', lang = 'sv')
+annotations =  pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("explain_document_sm", lang = "sv")
+val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Hej från John Snow Labs! ""]
+result_df = nlu.load('sv.explain').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                      | sentence                     | token                                    | lemma                                    | pos                                        | embeddings                   | ner                                   | entities            |
+|---:|:------------------------------|:-----------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------|
+|  0 | ['Hej från John Snow Labs! '] | ['Hej från John Snow Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0306969992816448,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|explain_document_sm|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|sv|
+|Size:|176.0 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- LemmatizerModel
+- PerceptronModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-match_chunks_en.md b/docs/_posts/ahmedlone127/2023-05-25-match_chunks_en.md
new file mode 100644
index 00000000000000..903012f4a225c2
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-match_chunks_en.md
@@ -0,0 +1,141 @@
+---
+layout: model
+title: Match Chunks in Texts
+author: John Snow Labs
+name: match_chunks
+date: 2023-05-25
+tags: [en, open_source]
+task: Text Classification
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The pipeline uses regex `<DT/>?/<JJ/>*<NN>+`
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/match_chunks_en_4.4.2_3.4_1685043019049.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/match_chunks_en_4.4.2_3.4_1685043019049.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipeline
+
+pipeline_local = PretrainedPipeline('match_chunks')
+
+result = pipeline_local.annotate("David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.")
+
+result['chunk']
+```
+```scala
+
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP
+
+SparkNLP.version()
+
+val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text")
+
+val pipeline = PretrainedPipeline("match_chunks", lang="en")
+
+val annotation = pipeline.transform(testData)
+
+annotation.show()
+```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.match.chunks").predict("""David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.""")
+```
+
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+
+pipeline_local = PretrainedPipeline('match_chunks')
+
+result = pipeline_local.annotate("David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.")
+
+result['chunk']
+```
+```scala
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP
+
+SparkNLP.version()
+
+val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text")
+
+val pipeline = PretrainedPipeline("match_chunks", lang="en")
+
+val annotation = pipeline.transform(testData)
+
+annotation.show()
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.match.chunks").predict("""David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.""")
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
+['the restaurant yesterday',
+'family',
+'the day',
+'that time',
+'today',
+'the food',
+'tomorrow']
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|match_chunks|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|4.1 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- PerceptronModel
+- Chunker
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-match_datetime_en.md b/docs/_posts/ahmedlone127/2023-05-25-match_datetime_en.md
new file mode 100644
index 00000000000000..e5f01baffac33f
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-match_datetime_en.md
@@ -0,0 +1,127 @@
+---
+layout: model
+title: Match Datetime in Texts
+author: John Snow Labs
+name: match_datetime
+date: 2023-05-25
+tags: [en, open_source]
+task: Text Classification
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+DateMatcher based on yyyy/MM/dd
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/match_datetime_en_4.4.2_3.4_1685040031472.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/match_datetime_en_4.4.2_3.4_1685040031472.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+pipeline_local = PretrainedPipeline('match_datetime')
+
+tres = pipeline_local.fullAnnotate(input_list)[0]
+for dte in tres['date']:
+    sent = tres['sentence'][int(dte.metadata['sentence'])]
+    print (f'text/chunk {sent.result[dte.begin:dte.end+1]} | mapped_date: {dte.result}')
+```
+```scala
+
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP
+
+SparkNLP.version()
+
+val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family. 
+He also visited and the day before, but at that time he was alone.
+David again visited today with his colleagues.
+He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text")
+
+val pipeline = PretrainedPipeline("match_datetime", lang="en")
+
+val annotation = pipeline.transform(testData)
+
+annotation.show()
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+pipeline_local = PretrainedPipeline('match_datetime')
+
+tres = pipeline_local.fullAnnotate(input_list)[0]
+for dte in tres['date']:
+    sent = tres['sentence'][int(dte.metadata['sentence'])]
+    print (f'text/chunk {sent.result[dte.begin:dte.end+1]} | mapped_date: {dte.result}')
+```
+```scala
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP
+
+SparkNLP.version()
+
+val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family. 
+He also visited and the day before, but at that time he was alone.
+David again visited today with his colleagues.
+He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text")
+
+val pipeline = PretrainedPipeline("match_datetime", lang="en")
+
+val annotation = pipeline.transform(testData)
+
+annotation.show()
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
+text/chunk yesterday | mapped_date: 2022/01/02
+text/chunk  day before | mapped_date: 2022/01/02
+text/chunk today | mapped_date: 2022/01/03
+text/chunk tomorrow | mapped_date: 2022/01/04
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|match_datetime|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|12.9 KB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- MultiDateMatcher
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-match_pattern_en.md b/docs/_posts/ahmedlone127/2023-05-25-match_pattern_en.md
new file mode 100644
index 00000000000000..67b720cb60cbc5
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-match_pattern_en.md
@@ -0,0 +1,77 @@
+---
+layout: model
+title: Match Pattern
+author: John Snow Labs
+name: match_pattern
+date: 2023-05-25
+tags: [en, open_source]
+task: Text Classification
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The match_pattern is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and matches pattrens  .
+	It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/match_pattern_en_4.4.2_3.4_1685040085566.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/match_pattern_en_4.4.2_3.4_1685040085566.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline("match_pattern", "en", "clinical/models")
+	result = pipeline.annotate("""I love johnsnowlabs!  """)
+```
+
+</div>
+
+{:.model-param}
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline("match_pattern", "en", "clinical/models")
+	result = pipeline.annotate("""I love johnsnowlabs!  """)
+```
+
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|match_pattern|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|17.4 KB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- RegexMatcherModel
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-movies_sentiment_analysis_en.md b/docs/_posts/ahmedlone127/2023-05-25-movies_sentiment_analysis_en.md
new file mode 100644
index 00000000000000..1aa1e43fe345b0
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-movies_sentiment_analysis_en.md
@@ -0,0 +1,79 @@
+---
+layout: model
+title: Movies Sentiment Analysis
+author: John Snow Labs
+name: movies_sentiment_analysis
+date: 2023-05-25
+tags: [en, open_source]
+task: Sentiment Analysis
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The movies_sentiment_analysis is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and predicts sentiment .
+         It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/movies_sentiment_analysis_en_4.4.2_3.4_1685040688121.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/movies_sentiment_analysis_en_4.4.2_3.4_1685040688121.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline("movies_sentiment_analysis", "en")
+
+result = pipeline.annotate("""I love johnsnowlabs!  """)
+```
+
+</div>
+
+{:.model-param}
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline("movies_sentiment_analysis", "en")
+
+result = pipeline.annotate("""I love johnsnowlabs!  """)
+```
+
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|movies_sentiment_analysis|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|207.2 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- SymmetricDeleteModel
+- SentimentDetectorModel
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_base_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_base_en.md
index 2b784c34e2b082..4991fe05619320 100644
--- a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_base_en.md
+++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_base_en.md
@@ -29,8 +29,8 @@ It performs most of the common text processing tasks on your dataframe
 {:.btn-box}
 <button class="button button-orange" disabled>Live Demo</button>
 <button class="button button-orange" disabled>Open in Colab</button>
-[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_base_en_4.4.2_3.4_1685002595738.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
-[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_base_en_4.4.2_3.4_1685002595738.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_base_en_4.4.2_3.4_1685057740362.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_base_en_4.4.2_3.4_1685057740362.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
 
 ## How to use
 
diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_large_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_large_en.md
index e57085f3457201..e34af1639fe2b7 100644
--- a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_large_en.md
+++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_large_en.md
@@ -29,8 +29,8 @@ It performs most of the common text processing tasks on your dataframe
 {:.btn-box}
 <button class="button button-orange" disabled>Live Demo</button>
 <button class="button button-orange" disabled>Open in Colab</button>
-[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_large_en_4.4.2_3.4_1685002876888.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
-[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_large_en_4.4.2_3.4_1685002876888.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_large_en_4.4.2_3.4_1685058011750.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_large_en_4.4.2_3.4_1685058011750.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
 
 ## How to use
 
diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_medium_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_medium_en.md
index 0111a1a5b603c0..5e5b33a34999f0 100644
--- a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_medium_en.md
+++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_medium_en.md
@@ -29,8 +29,8 @@ It performs most of the common text processing tasks on your dataframe
 {:.btn-box}
 <button class="button button-orange" disabled>Live Demo</button>
 <button class="button button-orange" disabled>Open in Colab</button>
-[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_medium_en_4.4.2_3.4_1685003004663.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
-[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_medium_en_4.4.2_3.4_1685003004663.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_medium_en_4.4.2_3.4_1685058141036.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_medium_en_4.4.2_3.4_1685058141036.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
 
 ## How to use
 
diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_mini_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_mini_en.md
index 3b0399d4c97731..18514bae287899 100644
--- a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_mini_en.md
+++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_mini_en.md
@@ -29,8 +29,8 @@ It performs most of the common text processing tasks on your dataframe
 {:.btn-box}
 <button class="button button-orange" disabled>Live Demo</button>
 <button class="button button-orange" disabled>Open in Colab</button>
-[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_mini_en_4.4.2_3.4_1685003078331.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
-[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_mini_en_4.4.2_3.4_1685003078331.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_mini_en_4.4.2_3.4_1685058217920.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_mini_en_4.4.2_3.4_1685058217920.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
 
 ## How to use
 
diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_small_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_small_en.md
index 8159fb8f3542e0..b6278456400e9e 100644
--- a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_small_en.md
+++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_small_en.md
@@ -28,8 +28,8 @@ It performs most of the common text processing tasks on your dataframe
 {:.btn-box}
 <button class="button button-orange" disabled>Live Demo</button>
 <button class="button button-orange" disabled>Open in Colab</button>
-[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_small_en_4.4.2_3.4_1685003156550.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
-[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_small_en_4.4.2_3.4_1685003156550.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_small_en_4.4.2_3.4_1685058297770.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_small_en_4.4.2_3.4_1685058297770.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
 
 ## How to use
 
diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_tiny_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_tiny_en.md
index 3422a929ec3015..7efda03ad07cda 100644
--- a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_tiny_en.md
+++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_tiny_en.md
@@ -29,8 +29,8 @@ It performs most of the common text processing tasks on your dataframe
 {:.btn-box}
 <button class="button button-orange" disabled>Live Demo</button>
 <button class="button button-orange" disabled>Open in Colab</button>
-[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_tiny_en_4.4.2_3.4_1685003227086.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
-[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_tiny_en_4.4.2_3.4_1685003227086.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_tiny_en_4.4.2_3.4_1685058370008.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_tiny_en_4.4.2_3.4_1685058370008.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
 
 ## How to use
 
diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_base_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_base_en.md
index 675da0be7072ec..b85ab4b633b442 100644
--- a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_base_en.md
+++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_base_en.md
@@ -29,8 +29,8 @@ It performs most of the common text processing tasks on your dataframe
 {:.btn-box}
 <button class="button button-orange" disabled>Live Demo</button>
 <button class="button button-orange" disabled>Open in Colab</button>
-[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_base_en_4.4.2_3.4_1685003364230.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
-[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_base_en_4.4.2_3.4_1685003364230.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_base_en_4.4.2_3.4_1685058505292.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_base_en_4.4.2_3.4_1685058505292.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
 
 ## How to use
 
diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_large_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_large_en.md
index b9322a0f4b7d99..91b5c23ebbd365 100644
--- a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_large_en.md
+++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_large_en.md
@@ -29,8 +29,8 @@ It performs most of the common text processing tasks on your dataframe
 {:.btn-box}
 <button class="button button-orange" disabled>Live Demo</button>
 <button class="button button-orange" disabled>Open in Colab</button>
-[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_large_en_4.4.2_3.4_1685003723900.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
-[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_large_en_4.4.2_3.4_1685003723900.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_large_en_4.4.2_3.4_1685058869048.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_large_en_4.4.2_3.4_1685058869048.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
 
 ## How to use
 
diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_small_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_small_en.md
index 05fab26fa6a07f..8ccf11eed91884 100644
--- a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_small_en.md
+++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_small_en.md
@@ -28,8 +28,8 @@ It performs most of the common text processing tasks on your dataframe
 {:.btn-box}
 <button class="button button-orange" disabled>Live Demo</button>
 <button class="button button-orange" disabled>Open in Colab</button>
-[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_small_en_4.4.2_3.4_1685003455062.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
-[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_small_en_4.4.2_3.4_1685003455062.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_small_en_4.4.2_3.4_1685058599061.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_small_en_4.4.2_3.4_1685058599061.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
 
 ## How to use
 
diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_lg_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_lg_en.md
new file mode 100644
index 00000000000000..0425702cf6b0d8
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_lg_en.md
@@ -0,0 +1,94 @@
+---
+layout: model
+title: Onto Recognize Entities Lg
+author: John Snow Labs
+name: onto_recognize_entities_lg
+date: 2023-05-25
+tags: [en, open_source]
+task: Named Entity Recognition
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The onto_recognize_entities_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entites.
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_lg_en_4.4.2_3.4_1685042868130.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_lg_en_4.4.2_3.4_1685042868130.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline("onto_recognize_entities_lg", "en")
+
+result = pipeline.annotate("""I love johnsnowlabs!  """)
+```
+
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.ner.onto.lg").predict("""I love johnsnowlabs!  """)
+```
+
+</div>
+
+{:.model-param}
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline("onto_recognize_entities_lg", "en")
+
+result = pipeline.annotate("""I love johnsnowlabs!  """)
+```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.ner.onto.lg").predict("""I love johnsnowlabs!  """)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|onto_recognize_entities_lg|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|2.5 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_sm_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_sm_en.md
new file mode 100644
index 00000000000000..91b9700a46de0f
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_sm_en.md
@@ -0,0 +1,121 @@
+---
+layout: model
+title: Recognize Entities DL pipeline for English - Small
+author: John Snow Labs
+name: onto_recognize_entities_sm
+date: 2023-05-25
+tags: [open_source, english, onto_recognize_entities_sm, pipeline, en]
+task: Named Entity Recognition
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The onto_recognize_entities_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_sm_en_4.4.2_3.4_1685042425870.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_sm_en_4.4.2_3.4_1685042425870.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('onto_recognize_entities_sm', lang = 'en')
+annotations =  pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("onto_recognize_entities_sm", lang = "en")
+val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Hello from John Snow Labs ! ""]
+result_df = nlu.load('en.ner.onto.sm').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('onto_recognize_entities_sm', lang = 'en')
+annotations =  pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("onto_recognize_entities_sm", lang = "en")
+val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Hello from John Snow Labs ! ""]
+result_df = nlu.load('en.ner.onto.sm').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                         | sentence                        | token                                          | embeddings                   | ner                                        | entities           |
+|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------|
+|  0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[0.2668800055980682,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|onto_recognize_entities_sm|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|166.7 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-recognize_entities_bert_en.md b/docs/_posts/ahmedlone127/2023-05-25-recognize_entities_bert_en.md
new file mode 100644
index 00000000000000..404c38a15a40b7
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-recognize_entities_bert_en.md
@@ -0,0 +1,123 @@
+---
+layout: model
+title: Recognize Entities DL pipeline for English - BERT
+author: John Snow Labs
+name: recognize_entities_bert
+date: 2023-05-25
+tags: [open_source, english, recognize_entities_bert, pipeline, en]
+task: Named Entity Recognition
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The recognize_entities_bert is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps 
+and recognizes entities .
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/recognize_entities_bert_en_4.4.2_3.4_1685041284903.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/recognize_entities_bert_en_4.4.2_3.4_1685041284903.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('recognize_entities_bert', lang = 'en')
+annotations =  pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("recognize_entities_bert", lang = "en")
+val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Hello from John Snow Labs ! ""]
+result_df = nlu.load('en.ner.bert').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('recognize_entities_bert', lang = 'en')
+annotations =  pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("recognize_entities_bert", lang = "en")
+val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Hello from John Snow Labs ! ""]
+result_df = nlu.load('en.ner.bert').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                         | sentence                        | token                                          | embeddings                   | ner                                        | entities           |
+|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------|
+|  0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[-0.085488274693489,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-ORG', 'O'] | ['John Snow Labs'] ||    | document                         | sentence                        | token                                          | embeddings                   | ner                                        | entities           |
+
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|recognize_entities_bert|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|424.5 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- BertEmbeddings
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-recognize_entities_dl_en.md b/docs/_posts/ahmedlone127/2023-05-25-recognize_entities_dl_en.md
new file mode 100644
index 00000000000000..0c3dc5fcf95ca4
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-recognize_entities_dl_en.md
@@ -0,0 +1,122 @@
+---
+layout: model
+title: Recognize Entities DL Pipeline for English
+author: John Snow Labs
+name: recognize_entities_dl
+date: 2023-05-25
+tags: [open_source, english, recognize_entities_dl, pipeline, en]
+task: Named Entity Recognition
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The recognize_entities_dl is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps 
+and recognizes entities .
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/recognize_entities_dl_en_4.4.2_3.4_1685041151703.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/recognize_entities_dl_en_4.4.2_3.4_1685041151703.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('recognize_entities_dl', lang = 'en')
+annotations =  pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("recognize_entities_dl", lang = "en")
+val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Hello from John Snow Labs ! ""]
+result_df = nlu.load('en.ner').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('recognize_entities_dl', lang = 'en')
+annotations =  pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("recognize_entities_dl", lang = "en")
+val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Hello from John Snow Labs ! ""]
+result_df = nlu.load('en.ner').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                         | sentence                        | token                                          | embeddings                   | ner                                                | entities                      |
+|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:---------------------------------------------------|:------------------------------|
+|  0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[0.2668800055980682,.,...]] | ['B-ORG', 'I-ORG', 'I-ORG', 'I-ORG', 'I-ORG', 'O'] | ['Hello from John Snow Labs'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|recognize_entities_dl|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|166.7 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-25-text_cleaning_en.md b/docs/_posts/ahmedlone127/2023-05-25-text_cleaning_en.md
new file mode 100644
index 00000000000000..52c450ba6f87c4
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-25-text_cleaning_en.md
@@ -0,0 +1,80 @@
+---
+layout: model
+title: Text Cleaning
+author: John Snow Labs
+name: text_cleaning
+date: 2023-05-25
+tags: [en, open_source]
+task: Text Classification
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The text_cleaning is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and cleans text.
+         It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/text_cleaning_en_4.4.2_3.4_1685046282536.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/text_cleaning_en_4.4.2_3.4_1685046282536.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline("text_cleaning", "en")
+
+result = pipeline.annotate("""I love johnsnowlabs!  """)
+```
+
+</div>
+
+{:.model-param}
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline("text_cleaning", "en")
+
+result = pipeline.annotate("""I love johnsnowlabs!  """)
+```
+
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|text_cleaning|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|944.5 KB|
+
+## Included Models
+
+- DocumentAssembler
+- TokenizerModel
+- NormalizerModel
+- StopWordsCleaner
+- LemmatizerModel
+- TokenAssembler
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-26-albert_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-albert_base_token_classifier_conll03_pipeline_en.md
new file mode 100644
index 00000000000000..78c671f91f89da
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-26-albert_base_token_classifier_conll03_pipeline_en.md
@@ -0,0 +1,106 @@
+---
+layout: model
+title: ALBERT Base CoNNL-03 NER Pipeline
+author: John Snow Labs
+name: albert_base_token_classifier_conll03_pipeline
+date: 2023-05-26
+tags: [open_source, ner, token_classifier, albert, conll03, en]
+task: Named Entity Recognition
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline is built on the top of [albert_base_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/26/albert_base_token_classifier_conll03_en.html) model.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685066409500.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685066409500.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+
+pipeline = PretrainedPipeline("albert_base_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I work at John Snow Labs.")
+```
+```scala
+
+
+val pipeline = new PretrainedPipeline("albert_base_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I work at John Snow Labs."))
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+pipeline = PretrainedPipeline("albert_base_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I work at John Snow Labs.")
+```
+```scala
+val pipeline = new PretrainedPipeline("albert_base_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I work at John Snow Labs."))
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
+
++--------------+---------+
+|chunk         |ner_label|
++--------------+---------+
+|John          |PER      |
+|John Snow Labs|ORG      |
++--------------+---------+
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|albert_base_token_classifier_conll03_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|43.1 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetectorDLModel
+- TokenizerModel
+- AlbertForTokenClassification
+- NerConverter
+- Finisher
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-26-albert_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-albert_large_token_classifier_conll03_pipeline_en.md
new file mode 100644
index 00000000000000..4f4443a9414ff3
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-26-albert_large_token_classifier_conll03_pipeline_en.md
@@ -0,0 +1,106 @@
+---
+layout: model
+title: ALBERT Large CoNNL-03 NER Pipeline
+author: John Snow Labs
+name: albert_large_token_classifier_conll03_pipeline
+date: 2023-05-26
+tags: [open_source, ner, token_classifier, albert, conll03, en]
+task: Named Entity Recognition
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline is built on the top of [albert_large_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/26/albert_large_token_classifier_conll03_en.html) model.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_large_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685066487956.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_large_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685066487956.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+
+pipeline = PretrainedPipeline("albert_large_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I work at John Snow Labs.")
+```
+```scala
+
+
+val pipeline = new PretrainedPipeline("albert_large_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I work at John Snow Labs."))
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+pipeline = PretrainedPipeline("albert_large_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I work at John Snow Labs.")
+```
+```scala
+val pipeline = new PretrainedPipeline("albert_large_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I work at John Snow Labs."))
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
+
++--------------+---------+
+|chunk         |ner_label|
++--------------+---------+
+|John          |PER      |
+|John Snow Labs|ORG      |
++--------------+---------+
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|albert_large_token_classifier_conll03_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|64.5 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetectorDLModel
+- TokenizerModel
+- AlbertForTokenClassification
+- NerConverter
+- Finisher
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-26-albert_xlarge_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-albert_xlarge_token_classifier_conll03_pipeline_en.md
new file mode 100644
index 00000000000000..1cfd1f3b1eea22
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-26-albert_xlarge_token_classifier_conll03_pipeline_en.md
@@ -0,0 +1,106 @@
+---
+layout: model
+title: ALBERT XLarge CoNNL-03 NER Pipeline
+author: John Snow Labs
+name: albert_xlarge_token_classifier_conll03_pipeline
+date: 2023-05-26
+tags: [open_source, ner, token_classifier, albert, conll03, en]
+task: Named Entity Recognition
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline is built on the top of [albert_xlarge_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/26/albert_xlarge_token_classifier_conll03_en.html) model.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_xlarge_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685066592482.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_xlarge_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685066592482.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+
+pipeline = PretrainedPipeline("albert_xlarge_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I work at John Snow Labs.")
+```
+```scala
+
+
+val pipeline = new PretrainedPipeline("albert_xlarge_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I work at John Snow Labs."))
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+pipeline = PretrainedPipeline("albert_xlarge_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I work at John Snow Labs.")
+```
+```scala
+val pipeline = new PretrainedPipeline("albert_xlarge_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I work at John Snow Labs."))
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
+
++--------------+---------+
+|chunk         |ner_label|
++--------------+---------+
+|John          |PER      |
+|John Snow Labs|ORG      |
++--------------+---------+
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|albert_xlarge_token_classifier_conll03_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|206.5 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetectorDLModel
+- TokenizerModel
+- AlbertForTokenClassification
+- NerConverter
+- Finisher
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-26-bert_sequence_classifier_age_news_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-bert_sequence_classifier_age_news_pipeline_en.md
new file mode 100644
index 00000000000000..7f4939e8c4dcf9
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-26-bert_sequence_classifier_age_news_pipeline_en.md
@@ -0,0 +1,98 @@
+---
+layout: model
+title: Pipeline to Classify Texts into 4 News Categories
+author: John Snow Labs
+name: bert_sequence_classifier_age_news_pipeline
+date: 2023-05-26
+tags: [ag_news, news, bert, bert_sequence, classification, en, open_source]
+task: Text Classification
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline is built on the top of [bert_sequence_classifier_age_news_en](https://nlp.johnsnowlabs.com/2021/11/07/bert_sequence_classifier_age_news_en.html) which is imported from `HuggingFace`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_age_news_pipeline_en_4.4.2_3.4_1685062332212.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_age_news_pipeline_en_4.4.2_3.4_1685062332212.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+
+news_pipeline = PretrainedPipeline("bert_sequence_classifier_age_news_pipeline", lang = "en")
+
+news_pipeline.annotate("Microsoft has taken its first step into the metaverse.")
+```
+```scala
+
+
+val news_pipeline = new PretrainedPipeline("bert_sequence_classifier_age_news_pipeline", lang = "en")
+
+news_pipeline.annotate("Microsoft has taken its first step into the metaverse.")
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+news_pipeline = PretrainedPipeline("bert_sequence_classifier_age_news_pipeline", lang = "en")
+
+news_pipeline.annotate("Microsoft has taken its first step into the metaverse.")
+```
+```scala
+val news_pipeline = new PretrainedPipeline("bert_sequence_classifier_age_news_pipeline", lang = "en")
+
+news_pipeline.annotate("Microsoft has taken its first step into the metaverse.")
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
+
+['Sci/Tech']
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|bert_sequence_classifier_age_news_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|42.4 MB|
+
+## Included Models
+
+- DocumentAssembler
+- TokenizerModel
+- BertForSequenceClassification
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-26-bert_sequence_classifier_trec_coarse_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-bert_sequence_classifier_trec_coarse_pipeline_en.md
new file mode 100644
index 00000000000000..3990534aee8c8b
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-26-bert_sequence_classifier_trec_coarse_pipeline_en.md
@@ -0,0 +1,100 @@
+---
+layout: model
+title: Pipeline to Classify Texts into TREC-6 Classes
+author: John Snow Labs
+name: bert_sequence_classifier_trec_coarse_pipeline
+date: 2023-05-26
+tags: [bert_sequence, trec, coarse, bert, en, open_source]
+task: Text Classification
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline is built on the top of [bert_sequence_classifier_trec_coarse_en](https://nlp.johnsnowlabs.com/2021/11/06/bert_sequence_classifier_trec_coarse_en.html).
+
+The TREC dataset for question classification consists of open-domain, fact-based questions divided into broad semantic categories. You can check the official documentation of the dataset, entities, etc. [here](https://search.r-project.org/CRAN/refmans/textdata/html/dataset_trec.html).
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_trec_coarse_pipeline_en_4.4.2_3.4_1685062251815.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_trec_coarse_pipeline_en_4.4.2_3.4_1685062251815.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+
+trec_pipeline = PretrainedPipeline("bert_sequence_classifier_trec_coarse_pipeline", lang = "en")
+
+trec_pipeline.annotate("Germany is the largest country in Europe economically.")
+```
+```scala
+
+
+val trec_pipeline = new PretrainedPipeline("bert_sequence_classifier_trec_coarse_pipeline", lang = "en")
+
+trec_pipeline.annotate("Germany is the largest country in Europe economically.")
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+trec_pipeline = PretrainedPipeline("bert_sequence_classifier_trec_coarse_pipeline", lang = "en")
+
+trec_pipeline.annotate("Germany is the largest country in Europe economically.")
+```
+```scala
+val trec_pipeline = new PretrainedPipeline("bert_sequence_classifier_trec_coarse_pipeline", lang = "en")
+
+trec_pipeline.annotate("Germany is the largest country in Europe economically.")
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
+
+['LOC']
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|bert_sequence_classifier_trec_coarse_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|406.6 MB|
+
+## Included Models
+
+- DocumentAssembler
+- TokenizerModel
+- BertForSequenceClassification
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-26-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md b/docs/_posts/ahmedlone127/2023-05-26-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md
new file mode 100644
index 00000000000000..76147872e1abee
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-26-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md
@@ -0,0 +1,106 @@
+---
+layout: model
+title: Dutch NER Pipeline
+author: John Snow Labs
+name: bert_token_classifier_dutch_udlassy_ner_pipeline
+date: 2023-05-26
+tags: [open_source, ner, dutch, token_classifier, bert, treatment, nl]
+task: Named Entity Recognition
+language: nl
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline is built on the top of [bert_token_classifier_dutch_udlassy_ner](https://nlp.johnsnowlabs.com/2021/12/08/bert_token_classifier_dutch_udlassy_ner_nl.html) model.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_token_classifier_dutch_udlassy_ner_pipeline_nl_4.4.2_3.4_1685064033791.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_token_classifier_dutch_udlassy_ner_pipeline_nl_4.4.2_3.4_1685064033791.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+pipeline = PretrainedPipeline("bert_token_classifier_dutch_udlassy_ner_pipeline", lang = "nl")
+
+pipeline.annotate("Mijn naam is Peter Fergusson. Ik woon sinds oktober 2011 in New York en werk 5 jaar bij Tesla Motor.")
+```
+```scala
+
+val pipeline = new PretrainedPipeline("bert_token_classifier_dutch_udlassy_ner_pipeline", lang = "nl")
+
+pipeline.annotate("Mijn naam is Peter Fergusson. Ik woon sinds oktober 2011 in New York en werk 5 jaar bij Tesla Motor.")
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+pipeline = PretrainedPipeline("bert_token_classifier_dutch_udlassy_ner_pipeline", lang = "nl")
+
+pipeline.annotate("Mijn naam is Peter Fergusson. Ik woon sinds oktober 2011 in New York en werk 5 jaar bij Tesla Motor.")
+```
+```scala
+val pipeline = new PretrainedPipeline("bert_token_classifier_dutch_udlassy_ner_pipeline", lang = "nl")
+
+pipeline.annotate("Mijn naam is Peter Fergusson. Ik woon sinds oktober 2011 in New York en werk 5 jaar bij Tesla Motor.")
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
++---------------+---------+
+|chunk          |ner_label|
++---------------+---------+
+|Peter Fergusson|PERSON   |
+|oktober 2011   |DATE     |
+|New York       |GPE      |
+|5 jaar         |DATE     |
+|Tesla Motor    |ORG      |
++---------------+---------+
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|bert_token_classifier_dutch_udlassy_ner_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|nl|
+|Size:|408.0 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetectorDLModel
+- TokenizerModel
+- BertForTokenClassification
+- NerConverter
+- Finisher
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-26-bert_token_classifier_hi_en_ner_pipeline_hi.md b/docs/_posts/ahmedlone127/2023-05-26-bert_token_classifier_hi_en_ner_pipeline_hi.md
new file mode 100644
index 00000000000000..bc5223db30914c
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-26-bert_token_classifier_hi_en_ner_pipeline_hi.md
@@ -0,0 +1,113 @@
+---
+layout: model
+title: NER Pipeline for Hindi+English
+author: John Snow Labs
+name: bert_token_classifier_hi_en_ner_pipeline
+date: 2023-05-26
+tags: [hindi, bert_token, hi, open_source]
+task: Named Entity Recognition
+language: hi
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline is built on [bert_token_classifier_hi_en_ner](https://nlp.johnsnowlabs.com/2021/12/27/bert_token_classifier_hi_en_ner_hi.html).
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_token_classifier_hi_en_ner_pipeline_hi_4.4.2_3.4_1685063082391.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_token_classifier_hi_en_ner_pipeline_hi_4.4.2_3.4_1685063082391.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+pipeline = PretrainedPipeline("bert_token_classifier_hi_en_ner_pipeline", lang = "hi")
+
+pipeline.annotate("रिलायंस इंडस्ट्रीज़ लिमिटेड (Reliance Industries Limited) एक भारतीय संगुटिका नियंत्रक कंपनी है, जिसका मुख्यालय मुंबई, महाराष्ट्र (Maharashtra) में स्थित है।रतन नवल टाटा (28 दिसंबर 1937, को मुम्बई (Mumbai), में जन्मे) टाटा समुह के वर्तमान अध्यक्ष, जो भारत की सबसे बड़ी व्यापारिक समूह है, जिसकी स्थापना जमशेदजी टाटा ने की और उनके परिवार की पीढियों ने इसका विस्तार किया और इसे दृढ़ बनाया।")
+```
+```scala
+
+val pipeline = new PretrainedPipeline("bert_token_classifier_hi_en_ner_pipeline", lang = "hi")
+
+val pipeline.annotate("रिलायंस इंडस्ट्रीज़ लिमिटेड (Reliance Industries Limited) एक भारतीय संगुटिका नियंत्रक कंपनी है, जिसका मुख्यालय मुंबई, महाराष्ट्र (Maharashtra) में स्थित है।रतन नवल टाटा (28 दिसंबर 1937, को मुम्बई (Mumbai), में जन्मे) टाटा समुह के वर्तमान अध्यक्ष, जो भारत की सबसे बड़ी व्यापारिक समूह है, जिसकी स्थापना जमशेदजी टाटा ने की और उनके परिवार की पीढियों ने इसका विस्तार किया और इसे दृढ़ बनाया।")
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+pipeline = PretrainedPipeline("bert_token_classifier_hi_en_ner_pipeline", lang = "hi")
+
+pipeline.annotate("रिलायंस इंडस्ट्रीज़ लिमिटेड (Reliance Industries Limited) एक भारतीय संगुटिका नियंत्रक कंपनी है, जिसका मुख्यालय मुंबई, महाराष्ट्र (Maharashtra) में स्थित है।रतन नवल टाटा (28 दिसंबर 1937, को मुम्बई (Mumbai), में जन्मे) टाटा समुह के वर्तमान अध्यक्ष, जो भारत की सबसे बड़ी व्यापारिक समूह है, जिसकी स्थापना जमशेदजी टाटा ने की और उनके परिवार की पीढियों ने इसका विस्तार किया और इसे दृढ़ बनाया।")
+```
+```scala
+val pipeline = new PretrainedPipeline("bert_token_classifier_hi_en_ner_pipeline", lang = "hi")
+
+val pipeline.annotate("रिलायंस इंडस्ट्रीज़ लिमिटेड (Reliance Industries Limited) एक भारतीय संगुटिका नियंत्रक कंपनी है, जिसका मुख्यालय मुंबई, महाराष्ट्र (Maharashtra) में स्थित है।रतन नवल टाटा (28 दिसंबर 1937, को मुम्बई (Mumbai), में जन्मे) टाटा समुह के वर्तमान अध्यक्ष, जो भारत की सबसे बड़ी व्यापारिक समूह है, जिसकी स्थापना जमशेदजी टाटा ने की और उनके परिवार की पीढियों ने इसका विस्तार किया और इसे दृढ़ बनाया।")
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
++---------------------------+------------+
+|chunk                      |ner_label   |
++---------------------------+------------+
+|रिलायंस इंडस्ट्रीज़ लिमिटेड          |ORGANISATION|
+|Reliance Industries Limited|ORGANISATION|
+|भारतीय                      |PLACE       |
+|मुंबई                        |PLACE       |
+|महाराष्ट्र                      |PLACE       |
+|Maharashtra)               |PLACE       |
+|नवल टाटा                    |PERSON      |
+|मुम्बई                       |PLACE       |
+|Mumbai                     |PLACE       |
+|टाटा समुह                    |ORGANISATION|
+|भारत                       |PLACE       |
+|जमशेदजी टाटा                 |PERSON      |
++---------------------------+------------+
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|bert_token_classifier_hi_en_ner_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|hi|
+|Size:|665.8 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- BertForTokenClassification
+- NerConverter
+- Finisher
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-26-bert_token_classifier_scandi_ner_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-26-bert_token_classifier_scandi_ner_pipeline_xx.md
new file mode 100644
index 00000000000000..e2f6ac52e6214b
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-26-bert_token_classifier_scandi_ner_pipeline_xx.md
@@ -0,0 +1,103 @@
+---
+layout: model
+title: NER Pipeline for 6 Scandinavian Languages
+author: John Snow Labs
+name: bert_token_classifier_scandi_ner_pipeline
+date: 2023-05-26
+tags: [danish, norwegian, swedish, icelandic, faroese, bert, xx, open_source]
+task: Named Entity Recognition
+language: xx
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline is built on [bert_token_classifier_scandi_ner](https://nlp.johnsnowlabs.com/2021/12/09/bert_token_classifier_scandi_ner_xx.html) model which is imported from `HuggingFace`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_token_classifier_scandi_ner_pipeline_xx_4.4.2_3.4_1685062102199.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_token_classifier_scandi_ner_pipeline_xx_4.4.2_3.4_1685062102199.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+scandiner_pipeline = PretrainedPipeline("bert_token_classifier_scandi_ner_pipeline", lang = "xx")
+scandiner_pipeline.annotate("Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner.")
+```
+```scala
+
+val scandiner_pipeline = new PretrainedPipeline("bert_token_classifier_scandi_ner_pipeline", lang = "xx")
+
+val scandiner_pipeline.annotate("Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner.")
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+scandiner_pipeline = PretrainedPipeline("bert_token_classifier_scandi_ner_pipeline", lang = "xx")
+scandiner_pipeline.annotate("Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner.")
+```
+```scala
+val scandiner_pipeline = new PretrainedPipeline("bert_token_classifier_scandi_ner_pipeline", lang = "xx")
+
+val scandiner_pipeline.annotate("Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner.")
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
++-------------------+---------+
+|chunk              |ner_label|
++-------------------+---------+
+|Hans               |PER      |
+|Statens Universitet|ORG      |
+|København          |LOC      |
+|københavner        |MISC     |
++-------------------+---------+
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|bert_token_classifier_scandi_ner_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|xx|
+|Size:|666.9 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- BertForTokenClassification
+- NerConverter
+- Finisher
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-26-distilbert_base_token_classifier_masakhaner_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-26-distilbert_base_token_classifier_masakhaner_pipeline_xx.md
new file mode 100644
index 00000000000000..9fb584b272e041
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-26-distilbert_base_token_classifier_masakhaner_pipeline_xx.md
@@ -0,0 +1,108 @@
+---
+layout: model
+title: NER Pipeline for 9 African Languages
+author: John Snow Labs
+name: distilbert_base_token_classifier_masakhaner_pipeline
+date: 2023-05-26
+tags: [hausa, igbo, kinyarwanda, luganda, nigerian, pidgin, swahilu, wolof, yoruba, xx, open_source]
+task: Named Entity Recognition
+language: xx
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline is built on the top of [distilbert_base_token_classifier_masakhaner](https://nlp.johnsnowlabs.com/2022/01/18/distilbert_base_token_classifier_masakhaner_xx.html) model.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_token_classifier_masakhaner_pipeline_xx_4.4.2_3.4_1685062637711.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_token_classifier_masakhaner_pipeline_xx_4.4.2_3.4_1685062637711.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+masakhaner_pipeline = PretrainedPipeline("distilbert_base_token_classifier_masakhaner_pipeline", lang = "xx")
+
+masakhaner_pipeline.annotate("Ilé-iṣẹ́ẹ Mohammed Sani Musa, Activate Technologies Limited, ni ó kó ẹ̀rọ Ìwé-pélébé Ìdìbò Alálòpẹ́ (PVCs) tí a lò fún ìbò ọdún-un 2019, ígbà tí ó jẹ́ òǹdíjedupò lábẹ́ ẹgbẹ́ olóṣèlúu tí ó ń tukọ̀ ètò ìṣèlú lọ́wọ́ All rogressives Congress (APC) fún Aṣojú Ìlà-Oòrùn Niger, ìyẹn gẹ́gẹ́ bí ilé iṣẹ́ aṣèwádìí, Premium Times ṣe tẹ̀ ẹ́ jáde.")
+```
+```scala
+
+val masakhaner_pipeline = new PretrainedPipeline("distilbert_base_token_classifier_masakhaner_pipeline", lang = "xx")
+
+masakhaner_pipeline.annotate("Ilé-iṣẹ́ẹ Mohammed Sani Musa, Activate Technologies Limited, ni ó kó ẹ̀rọ Ìwé-pélébé Ìdìbò Alálòpẹ́ (PVCs) tí a lò fún ìbò ọdún-un 2019, ígbà tí ó jẹ́ òǹdíjedupò lábẹ́ ẹgbẹ́ olóṣèlúu tí ó ń tukọ̀ ètò ìṣèlú lọ́wọ́ All rogressives Congress (APC) fún Aṣojú Ìlà-Oòrùn Niger, ìyẹn gẹ́gẹ́ bí ilé iṣẹ́ aṣèwádìí, Premium Times ṣe tẹ̀ ẹ́ jáde.")
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+masakhaner_pipeline = PretrainedPipeline("distilbert_base_token_classifier_masakhaner_pipeline", lang = "xx")
+
+masakhaner_pipeline.annotate("Ilé-iṣẹ́ẹ Mohammed Sani Musa, Activate Technologies Limited, ni ó kó ẹ̀rọ Ìwé-pélébé Ìdìbò Alálòpẹ́ (PVCs) tí a lò fún ìbò ọdún-un 2019, ígbà tí ó jẹ́ òǹdíjedupò lábẹ́ ẹgbẹ́ olóṣèlúu tí ó ń tukọ̀ ètò ìṣèlú lọ́wọ́ All rogressives Congress (APC) fún Aṣojú Ìlà-Oòrùn Niger, ìyẹn gẹ́gẹ́ bí ilé iṣẹ́ aṣèwádìí, Premium Times ṣe tẹ̀ ẹ́ jáde.")
+```
+```scala
+val masakhaner_pipeline = new PretrainedPipeline("distilbert_base_token_classifier_masakhaner_pipeline", lang = "xx")
+
+masakhaner_pipeline.annotate("Ilé-iṣẹ́ẹ Mohammed Sani Musa, Activate Technologies Limited, ni ó kó ẹ̀rọ Ìwé-pélébé Ìdìbò Alálòpẹ́ (PVCs) tí a lò fún ìbò ọdún-un 2019, ígbà tí ó jẹ́ òǹdíjedupò lábẹ́ ẹgbẹ́ olóṣèlúu tí ó ń tukọ̀ ètò ìṣèlú lọ́wọ́ All rogressives Congress (APC) fún Aṣojú Ìlà-Oòrùn Niger, ìyẹn gẹ́gẹ́ bí ilé iṣẹ́ aṣèwádìí, Premium Times ṣe tẹ̀ ẹ́ jáde.")
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
++-----------------------------+---------+
+|chunk                        |ner_label|
++-----------------------------+---------+
+|Mohammed Sani Musa           |PER      |
+|Activate Technologies Limited|ORG      |
+|ọdún-un 2019                 |DATE     |
+|All rogressives Congress     |ORG      |
+|APC                          |ORG      |
+|Aṣojú Ìlà-Oòrùn Niger        |LOC      |
+|Premium Times                |ORG      |
++-----------------------------+---------+
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|distilbert_base_token_classifier_masakhaner_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|xx|
+|Size:|505.8 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- DistilBertForTokenClassification
+- NerConverter
+- Finisher
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-26-distilbert_token_classifier_typo_detector_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-distilbert_token_classifier_typo_detector_pipeline_en.md
new file mode 100644
index 00000000000000..2892cf85cbea2a
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-26-distilbert_token_classifier_typo_detector_pipeline_en.md
@@ -0,0 +1,105 @@
+---
+layout: model
+title: Typo Detector Pipeline for English
+author: John Snow Labs
+name: distilbert_token_classifier_typo_detector_pipeline
+date: 2023-05-26
+tags: [ner, bert, bert_for_token, typo, en, open_source]
+task: Named Entity Recognition
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline is built on the top of [distilbert_token_classifier_typo_detector](https://nlp.johnsnowlabs.com/2022/01/19/distilbert_token_classifier_typo_detector_en.html).
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_token_classifier_typo_detector_pipeline_en_4.4.2_3.4_1685067324460.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_token_classifier_typo_detector_pipeline_en_4.4.2_3.4_1685067324460.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+
+typo_pipeline = PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "en")
+
+typo_pipeline.annotate("He had also stgruggled with addiction during his tine in Congress.")
+```
+```scala
+
+
+val typo_pipeline = new PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "en")
+
+typo_pipeline.annotate("He had also stgruggled with addiction during his tine in Congress.")
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+typo_pipeline = PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "en")
+
+typo_pipeline.annotate("He had also stgruggled with addiction during his tine in Congress.")
+```
+```scala
+val typo_pipeline = new PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "en")
+
+typo_pipeline.annotate("He had also stgruggled with addiction during his tine in Congress.")
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
+
++----------+---------+
+|chunk     |ner_label|
++----------+---------+
+|stgruggled|PO       |
+|tine      |PO       |
++----------+---------+
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|distilbert_token_classifier_typo_detector_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|244.1 MB|
+
+## Included Models
+
+- DocumentAssembler
+- TokenizerModel
+- DistilBertForTokenClassification
+- NerConverter
+- Finisher
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-26-distilbert_token_classifier_typo_detector_pipeline_is.md b/docs/_posts/ahmedlone127/2023-05-26-distilbert_token_classifier_typo_detector_pipeline_is.md
new file mode 100644
index 00000000000000..050826db384303
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-26-distilbert_token_classifier_typo_detector_pipeline_is.md
@@ -0,0 +1,102 @@
+---
+layout: model
+title: Typo Detector Pipeline for Icelandic
+author: John Snow Labs
+name: distilbert_token_classifier_typo_detector_pipeline
+date: 2023-05-26
+tags: [icelandic, typo, ner, distilbert, is, open_source]
+task: Named Entity Recognition
+language: is
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline is built on the top of [distilbert_token_classifier_typo_detector_is](https://nlp.johnsnowlabs.com/2022/01/19/distilbert_token_classifier_typo_detector_is.html).
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_token_classifier_typo_detector_pipeline_is_4.4.2_3.4_1685062477110.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_token_classifier_typo_detector_pipeline_is_4.4.2_3.4_1685062477110.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+typo_pipeline = PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "is")
+
+typo_pipeline.annotate("Það er miög auðvelt að draga marktækar álykanir af texta með Spark NLP.")
+```
+```scala
+
+val typo_pipeline = new PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "is")
+
+typo_pipeline.annotate("Það er miög auðvelt að draga marktækar álykanir af texta með Spark NLP.")
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+typo_pipeline = PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "is")
+
+typo_pipeline.annotate("Það er miög auðvelt að draga marktækar álykanir af texta með Spark NLP.")
+```
+```scala
+val typo_pipeline = new PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "is")
+
+typo_pipeline.annotate("Það er miög auðvelt að draga marktækar álykanir af texta með Spark NLP.")
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
++--------+---------+
+|chunk   |ner_label|
++--------+---------+
+|miög    |PO       |
+|álykanir|PO       |
++--------+---------+
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|distilbert_token_classifier_typo_detector_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|is|
+|Size:|505.8 MB|
+
+## Included Models
+
+- DocumentAssembler
+- TokenizerModel
+- DistilBertForTokenClassification
+- NerConverter
+- Finisher
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-26-distilroberta_base_token_classifier_ontonotes_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-distilroberta_base_token_classifier_ontonotes_pipeline_en.md
new file mode 100644
index 00000000000000..61a39c2b402af3
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-26-distilroberta_base_token_classifier_ontonotes_pipeline_en.md
@@ -0,0 +1,107 @@
+---
+layout: model
+title: DistilRoBERTa Base Ontonotes NER Pipeline
+author: John Snow Labs
+name: distilroberta_base_token_classifier_ontonotes_pipeline
+date: 2023-05-26
+tags: [open_source, ner, token_classifier, distilroberta, ontonotes, en]
+task: Named Entity Recognition
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline is built on the top of [distilroberta_base_token_classifier_ontonotes](https://nlp.johnsnowlabs.com/2021/09/26/distilroberta_base_token_classifier_ontonotes_en.html) model.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilroberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.4_1685066716004.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilroberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.4_1685066716004.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+
+pipeline = PretrainedPipeline("distilroberta_base_token_classifier_ontonotes_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")
+```
+```scala
+
+
+val pipeline = new PretrainedPipeline("distilroberta_base_token_classifier_ontonotes_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020."))
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+pipeline = PretrainedPipeline("distilroberta_base_token_classifier_ontonotes_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")
+```
+```scala
+val pipeline = new PretrainedPipeline("distilroberta_base_token_classifier_ontonotes_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020."))
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
+
++--------------+---------+
+|chunk         |ner_label|
++--------------+---------+
+|John          |PERSON   |
+|John Snow Labs|ORG      |
+|November 2020 |DATE     |
++--------------+---------+
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|distilroberta_base_token_classifier_ontonotes_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|307.5 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetectorDLModel
+- TokenizerModel
+- RoBertaForTokenClassification
+- NerConverter
+- Finisher
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-26-longformer_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-longformer_base_token_classifier_conll03_pipeline_en.md
new file mode 100644
index 00000000000000..8d3a12f5bdae5a
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-26-longformer_base_token_classifier_conll03_pipeline_en.md
@@ -0,0 +1,106 @@
+---
+layout: model
+title: Longformer Base NER Pipeline
+author: John Snow Labs
+name: longformer_base_token_classifier_conll03_pipeline
+date: 2023-05-26
+tags: [ner, longformer, pipeline, conll, token_classification, en, open_source]
+task: Named Entity Recognition
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline is built on the top of [longformer_base_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/10/09/longformer_base_token_classifier_conll03_en.html) model.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/longformer_base_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685065123047.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/longformer_base_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685065123047.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+
+pipeline = PretrainedPipeline("longformer_base_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I am working at John Snow Labs.")
+```
+```scala
+
+
+val pipeline = new PretrainedPipeline("longformer_base_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I am working at John Snow Labs.")
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+pipeline = PretrainedPipeline("longformer_base_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I am working at John Snow Labs.")
+```
+```scala
+val pipeline = new PretrainedPipeline("longformer_base_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I am working at John Snow Labs.")
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
+
++--------------+---------+
+|chunk         |ner_label|
++--------------+---------+
+|John          |PER      |
+|John Snow Labs|ORG      |
++--------------+---------+
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|longformer_base_token_classifier_conll03_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|516.1 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetectorDLModel
+- TokenizerModel
+- LongformerForTokenClassification
+- NerConverter
+- Finisher
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-26-longformer_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-longformer_large_token_classifier_conll03_pipeline_en.md
new file mode 100644
index 00000000000000..bb275a83e66ad3
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-26-longformer_large_token_classifier_conll03_pipeline_en.md
@@ -0,0 +1,106 @@
+---
+layout: model
+title: Longformer Large NER Pipeline
+author: John Snow Labs
+name: longformer_large_token_classifier_conll03_pipeline
+date: 2023-05-26
+tags: [open_source, ner, token_classifier, longformer, conll, en]
+task: Named Entity Recognition
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline is built on the top of [longformer_large_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/10/09/longformer_large_token_classifier_conll03_en.html) model.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/longformer_large_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685065544572.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/longformer_large_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685065544572.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+
+pipeline = PretrainedPipeline("longformer_large_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I am working at John Snow Labs.")
+```
+```scala
+
+
+val pipeline = new PretrainedPipeline("longformer_large_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I am working at John Snow Labs.")
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+pipeline = PretrainedPipeline("longformer_large_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I am working at John Snow Labs.")
+```
+```scala
+val pipeline = new PretrainedPipeline("longformer_large_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I am working at John Snow Labs.")
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
+
++--------------+---------+
+|chunk         |ner_label|
++--------------+---------+
+|John          |PER      |
+|John Snow Labs|ORG      |
++--------------+---------+
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|longformer_large_token_classifier_conll03_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|1.5 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetectorDLModel
+- TokenizerModel
+- LongformerForTokenClassification
+- NerConverter
+- Finisher
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-26-nerdl_fewnerd_100d_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-nerdl_fewnerd_100d_pipeline_en.md
new file mode 100644
index 00000000000000..27eca880193ab7
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-26-nerdl_fewnerd_100d_pipeline_en.md
@@ -0,0 +1,109 @@
+---
+layout: model
+title: Pretrained Pipeline for Few-NERD-General NER Model
+author: John Snow Labs
+name: nerdl_fewnerd_100d_pipeline
+date: 2023-05-26
+tags: [fewnerd, nerdl, en, open_source]
+task: Named Entity Recognition
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline is built on Few-NERD model and it detects :
+
+`PERSON`, `ORGANIZATION`, `LOCATION`, `ART`, `BUILDING`, `PRODUCT`, `EVENT`, `OTHER`
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nerdl_fewnerd_100d_pipeline_en_4.4.2_3.4_1685061095892.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nerdl_fewnerd_100d_pipeline_en_4.4.2_3.4_1685061095892.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+fewnerd_pipeline = PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en")
+
+fewnerd_pipeline.annotate("""The Double Down is a sandwich offered by Kentucky Fried Chicken restaurants. He did not see active service again until 1882, when he took part in the Anglo-Egyptian War, and was present at the battle of Tell El Kebir (September 1882), for which he was mentioned in dispatches, received the Egypt Medal with clasp and the 3rd class of the Order of Medjidie, and was appointed a Companion of the Order of the Bath (CB).""")
+```
+```scala
+
+val pipeline = new PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en")
+
+val result = pipeline.fullAnnotate("The Double Down is a sandwich offered by Kentucky Fried Chicken restaurants. He did not see active service again until 1882, when he took part in the Anglo-Egyptian War, and was present at the battle of Tell El Kebir (September 1882), for which he was mentioned in dispatches, received the Egypt Medal with clasp and the 3rd class of the Order of Medjidie, and was appointed a Companion of the Order of the Bath (CB).")(0)
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+fewnerd_pipeline = PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en")
+
+fewnerd_pipeline.annotate("""The Double Down is a sandwich offered by Kentucky Fried Chicken restaurants. He did not see active service again until 1882, when he took part in the Anglo-Egyptian War, and was present at the battle of Tell El Kebir (September 1882), for which he was mentioned in dispatches, received the Egypt Medal with clasp and the 3rd class of the Order of Medjidie, and was appointed a Companion of the Order of the Bath (CB).""")
+```
+```scala
+val pipeline = new PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en")
+
+val result = pipeline.fullAnnotate("The Double Down is a sandwich offered by Kentucky Fried Chicken restaurants. He did not see active service again until 1882, when he took part in the Anglo-Egyptian War, and was present at the battle of Tell El Kebir (September 1882), for which he was mentioned in dispatches, received the Egypt Medal with clasp and the 3rd class of the Order of Medjidie, and was appointed a Companion of the Order of the Bath (CB).")(0)
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
++-----------------------+------------+
+|chunk                  |ner_label   |
++-----------------------+------------+
+|Kentucky Fried Chicken |ORGANIZATION|
+|Anglo-Egyptian War     |EVENT       |
+|battle of Tell El Kebir|EVENT       |
+|Egypt Medal            |OTHER       |
+|Order of Medjidie      |OTHER       |
++-----------------------+------------+
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|nerdl_fewnerd_100d_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|167.3 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
+- Finisher
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-26-nerdl_fewnerd_subentity_100d_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-nerdl_fewnerd_subentity_100d_pipeline_en.md
new file mode 100644
index 00000000000000..5275f8a0281ee1
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-26-nerdl_fewnerd_subentity_100d_pipeline_en.md
@@ -0,0 +1,110 @@
+---
+layout: model
+title: Pre-trained Pipeline for Few-NERD NER Model
+author: John Snow Labs
+name: nerdl_fewnerd_subentity_100d_pipeline
+date: 2023-05-26
+tags: [fewnerd, ner, en, open_source]
+task: Named Entity Recognition
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline is built on Few-NERD/inter public dataset and it extracts 66 entities that are in general scope.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nerdl_fewnerd_subentity_100d_pipeline_en_4.4.2_3.4_1685060170575.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nerdl_fewnerd_subentity_100d_pipeline_en_4.4.2_3.4_1685060170575.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+fewnerd_pipeline = PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en")
+
+fewnerd_pipeline.annotate("""12 Corazones ('12 Hearts') is Spanish-language dating game show produced in the United States for the television network Telemundo since January 2005, based on its namesake Argentine TV show format. The show is filmed in Los Angeles and revolves around the twelve Zodiac signs that identify each contestant. In 2008, Ho filmed a cameo in the Steven Spielberg feature film The Cloverfield Paradox, as a news pundit.""")
+```
+```scala
+
+val pipeline = new PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en")
+
+val result = pipeline.fullAnnotate("12 Corazones ('12 Hearts') is Spanish-language dating game show produced in the United States for the television network Telemundo since January 2005, based on its namesake Argentine TV show format. The show is filmed in Los Angeles and revolves around the twelve Zodiac signs that identify each contestant. In 2008, Ho filmed a cameo in the Steven Spielberg feature film The Cloverfield Paradox, as a news pundit.")(0)
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+fewnerd_pipeline = PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en")
+
+fewnerd_pipeline.annotate("""12 Corazones ('12 Hearts') is Spanish-language dating game show produced in the United States for the television network Telemundo since January 2005, based on its namesake Argentine TV show format. The show is filmed in Los Angeles and revolves around the twelve Zodiac signs that identify each contestant. In 2008, Ho filmed a cameo in the Steven Spielberg feature film The Cloverfield Paradox, as a news pundit.""")
+```
+```scala
+val pipeline = new PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en")
+
+val result = pipeline.fullAnnotate("12 Corazones ('12 Hearts') is Spanish-language dating game show produced in the United States for the television network Telemundo since January 2005, based on its namesake Argentine TV show format. The show is filmed in Los Angeles and revolves around the twelve Zodiac signs that identify each contestant. In 2008, Ho filmed a cameo in the Steven Spielberg feature film The Cloverfield Paradox, as a news pundit.")(0)
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
++-----------------------+----------------------------+
+|chunk                  |ner_label                   |
++-----------------------+----------------------------+
+|Corazones ('12 Hearts')|art-broadcastprogram        |
+|Spanish-language       |other-language              |
+|United States          |location-GPE                |
+|Telemundo              |organization-media/newspaper|
+|Argentine TV           |organization-media/newspaper|
+|Los Angeles            |location-GPE                |
+|Steven Spielberg       |person-director             |
+|Cloverfield Paradox    |art-film                    |
++-----------------------+----------------------------+
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|nerdl_fewnerd_subentity_100d_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|167.9 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
+- Finisher
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-26-nerdl_restaurant_100d_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-nerdl_restaurant_100d_pipeline_en.md
new file mode 100644
index 00000000000000..1cd9bd857b7dcb
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-26-nerdl_restaurant_100d_pipeline_en.md
@@ -0,0 +1,119 @@
+---
+layout: model
+title: Pipeline to Detect Restaurant-related Terminology
+author: John Snow Labs
+name: nerdl_restaurant_100d_pipeline
+date: 2023-05-26
+tags: [restaurant, ner, en, open_source]
+task: Named Entity Recognition
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline is built on the top of [nerdl_restaurant_100d](https://nlp.johnsnowlabs.com/2021/12/31/nerdl_restaurant_100d_en.html) model.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nerdl_restaurant_100d_pipeline_en_4.4.2_3.4_1685062743991.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nerdl_restaurant_100d_pipeline_en_4.4.2_3.4_1685062743991.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+restaurant_pipeline = PretrainedPipeline("nerdl_restaurant_100d_pipeline", lang = "en")
+
+restaurant_pipeline.annotate("Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.")
+```
+```scala
+
+val restaurant_pipeline = new PretrainedPipeline("nerdl_restaurant_100d_pipeline", lang = "en")
+
+restaurant_pipeline.annotate("Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.")
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+restaurant_pipeline = PretrainedPipeline("nerdl_restaurant_100d_pipeline", lang = "en")
+
+restaurant_pipeline.annotate("Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.")
+```
+```scala
+val restaurant_pipeline = new PretrainedPipeline("nerdl_restaurant_100d_pipeline", lang = "en")
+
+restaurant_pipeline.annotate("Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.")
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
++---------------------------+---------------+
+|chunk                      |ner_label      |
++---------------------------+---------------+
+|Hong Kong’s                |Restaurant_Name|
+|favourite                  |Rating         |
+|pasta bar                  |Dish           |
+|most reasonably            |Price          |
+|lunch                      |Hours          |
+|in town!                   |Location       |
+|Sha Tin – Pici’s           |Restaurant_Name|
+|burrata                    |Dish           |
+|arugula salad              |Dish           |
+|freshly tossed tuna tartare|Dish           |
+|reliable                   |Price          |
+|handmade pasta             |Dish           |
+|pappardelle                |Dish           |
+|effortless                 |Amenity        |
+|Italian                    |Cuisine        |
+|tidy one-pot               |Amenity        |
+|espresso                   |Dish           |
++---------------------------+---------------+
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|nerdl_restaurant_100d_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|166.7 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
+- Finisher
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-26-pos_ud_bokmaal_nb.md b/docs/_posts/ahmedlone127/2023-05-26-pos_ud_bokmaal_nb.md
new file mode 100644
index 00000000000000..5fef7a9e1b2912
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-26-pos_ud_bokmaal_nb.md
@@ -0,0 +1,138 @@
+---
+layout: model
+title: Part of Speech for Norwegian
+author: John Snow Labs
+name: pos_ud_bokmaal
+date: 2023-05-26
+tags: [pos, norwegian, nb, open_source]
+task: Part of Speech Tagging
+language: nb
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This model annotates the part of speech of tokens in a text. The parts of speech annotated include PRON (pronoun), CCONJ (coordinating conjunction), and 15 others. The part of speech model is useful for extracting the grammatical structure of a piece of text automatically.
+
+This model was trained using the dataset available at https://universaldependencies.org
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/pos_ud_bokmaal_nb_4.4.2_3.4_1685061155019.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/pos_ud_bokmaal_nb_4.4.2_3.4_1685061155019.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+pos = PerceptronModel.pretrained("pos_ud_bokmaal", "nb") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("pos")
+
+nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, pos])
+light_pipeline = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([['']]).toDF("text")))
+results = light_pipeline.fullAnnotate("Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene.")
+```
+```scala
+
+val pos = PerceptronModel.pretrained("pos_ud_bokmaal", "nb")
+    .setInputCols(Array("document", "token"))
+    .setOutputCol("pos")
+
+val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, pos))
+val data = Seq("Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene.").toDF("text")
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+
+text = ["""Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene."""]
+pos_df = nlu.load('nb.pos.ud_bokmaal').predict(text)
+pos_df
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+pos = PerceptronModel.pretrained("pos_ud_bokmaal", "nb") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("pos")
+
+nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, pos])
+light_pipeline = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([['']]).toDF("text")))
+results = light_pipeline.fullAnnotate("Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene.")
+```
+```scala
+val pos = PerceptronModel.pretrained("pos_ud_bokmaal", "nb")
+    .setInputCols(Array("document", "token"))
+    .setOutputCol("pos")
+
+val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, pos))
+val data = Seq("Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene.").toDF("text")
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+
+text = ["""Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene."""]
+pos_df = nlu.load('nb.pos.ud_bokmaal').predict(text)
+pos_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
+[Row(annotatorType='pos', begin=0, end=4, result='DET', metadata={'word': 'Annet'}),
+Row(annotatorType='pos', begin=6, end=8, result='SCONJ', metadata={'word': 'enn'}),
+Row(annotatorType='pos', begin=10, end=10, result='PART', metadata={'word': 'å'}),
+Row(annotatorType='pos', begin=12, end=15, result='AUX', metadata={'word': 'være'}),
+Row(annotatorType='pos', begin=17, end=22, result='NOUN', metadata={'word': 'kongen'}),
+...]
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|pos_ud_bokmaal|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|nb|
+|Size:|17.7 KB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- PerceptronModel
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-26-recognize_entities_dl_fa.md b/docs/_posts/ahmedlone127/2023-05-26-recognize_entities_dl_fa.md
new file mode 100644
index 00000000000000..f3050a29f18a85
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-26-recognize_entities_dl_fa.md
@@ -0,0 +1,169 @@
+---
+layout: model
+title: Explain Document DL Pipeline for Farsi/Persian
+author: John Snow Labs
+name: recognize_entities_dl
+date: 2023-05-26
+tags: [pipeline, ner, fa, open_source]
+task: Named Entity Recognition
+language: fa
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The explain_document_dl is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities . It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/recognize_entities_dl_fa_4.4.2_3.4_1685059282514.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/recognize_entities_dl_fa_4.4.2_3.4_1685059282514.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipeline
+
+pipeline = PretrainedPipeline('recognize_entities_dl', lang = 'fa')
+
+annotations =  pipeline.fullAnnotate("""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند""")[0]
+
+annotations.keys()
+```
+```scala
+
+val pipeline = new PretrainedPipeline("recognize_entities_dl", lang = "fa")
+
+val result = pipeline.fullAnnotate("""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند""")(0)
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+
+text = ["""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند"""]
+
+result_df = nlu.load('fa.recognize_entities_dl').predict(text)
+
+result_df
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+
+pipeline = PretrainedPipeline('recognize_entities_dl', lang = 'fa')
+
+annotations =  pipeline.fullAnnotate("""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند""")[0]
+
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("recognize_entities_dl", lang = "fa")
+
+val result = pipeline.fullAnnotate("""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند""")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+
+text = ["""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند"""]
+
+result_df = nlu.load('fa.recognize_entities_dl').predict(text)
+
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
+|    | document                                                                                                                                                                                  | sentence                                                                                                                                                                                  | token     | clean_tokens   | lemma    | pos   | embeddings   | ner   | entities             |
+|---:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------|:---------------|:---------|:------|:-------------|:------|:---------------------|
+|  0 | "به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند | "به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند | "         | "              | "        | PUNCT | "            | O     | خبرنگار ایرنا        |
+|  1 |                                                                                                                                                                                           |                                                                                                                                                                                           | به        | گزارش          | به       | ADP   | به           | O     | محمد قمی             |
+|  2 |                                                                                                                                                                                           |                                                                                                                                                                                           | گزارش     | خبرنگار        | گزارش    | NOUN  | گزارش        | O     | پاکدشت               |
+|  3 |                                                                                                                                                                                           |                                                                                                                                                                                           | خبرنگار   | ایرنا          | خبرنگار  | NOUN  | خبرنگار      | B-ORG | علی‌اکبر موسوی خوئینی |
+|  4 |                                                                                                                                                                                           |                                                                                                                                                                                           | ایرنا     | ،              | ایرنا    | PROPN | ایرنا        | I-ORG | شمس‌الدین وهابی       |
+|  5 |                                                                                                                                                                                           |                                                                                                                                                                                           | ،         | اساس           | ؛        | PUNCT | ،            | O     | تهران                |
+|  6 |                                                                                                                                                                                           |                                                                                                                                                                                           | بر        | تصمیم          | بر       | ADP   | بر           | O     |                      |
+|  7 |                                                                                                                                                                                           |                                                                                                                                                                                           | اساس      | این            | اساس     | NOUN  | اساس         | O     |                      |
+|  8 |                                                                                                                                                                                           |                                                                                                                                                                                           | تصمیم     | مجمع           | تصمیم    | NOUN  | تصمیم        | O     |                      |
+|  9 |                                                                                                                                                                                           |                                                                                                                                                                                           | این       | ،              | این      | DET   | این          | O     |                      |
+| 10 |                                                                                                                                                                                           |                                                                                                                                                                                           | مجمع      | محمد           | مجمع     | NOUN  | مجمع         | O     |                      |
+| 11 |                                                                                                                                                                                           |                                                                                                                                                                                           | ،         | قمی            | ؛        | PUNCT | ،            | O     |                      |
+| 12 |                                                                                                                                                                                           |                                                                                                                                                                                           | محمد      | نماینده        | محمد     | PROPN | محمد         | B-PER |                      |
+| 13 |                                                                                                                                                                                           |                                                                                                                                                                                           | قمی       | پاکدشت         | قمی      | PROPN | قمی          | I-PER |                      |
+| 14 |                                                                                                                                                                                           |                                                                                                                                                                                           | نماینده   | عنوان          | نماینده  | NOUN  | نماینده      | O     |                      |
+| 15 |                                                                                                                                                                                           |                                                                                                                                                                                           | مردم      | رئیس           | مردم     | NOUN  | مردم         | O     |                      |
+| 16 |                                                                                                                                                                                           |                                                                                                                                                                                           | پاکدشت    | علی‌اکبر        | پاکدشت   | PROPN | پاکدشت       | B-LOC |                      |
+| 17 |                                                                                                                                                                                           |                                                                                                                                                                                           | به        | موسوی          | به       | ADP   | به           | O     |                      |
+| 18 |                                                                                                                                                                                           |                                                                                                                                                                                           | عنوان     | خوئینی         | عنوان    | NOUN  | عنوان        | O     |                      |
+| 19 |                                                                                                                                                                                           |                                                                                                                                                                                           | رئیس      | شمس‌الدین       | رئیس     | NOUN  | رئیس         | O     |                      |
+| 20 |                                                                                                                                                                                           |                                                                                                                                                                                           | و         | وهابی          | او       | CCONJ | و            | O     |                      |
+| 21 |                                                                                                                                                                                           |                                                                                                                                                                                           | علی‌اکبر   | نمایندگان      | علی‌اکبر  | PROPN | علی‌اکبر      | B-PER |                      |
+| 22 |                                                                                                                                                                                           |                                                                                                                                                                                           | موسوی     | تهران          | موسوی    | PROPN | موسوی        | I-PER |                      |
+| 23 |                                                                                                                                                                                           |                                                                                                                                                                                           | خوئینی    | عنوان          | خوئینی   | PROPN | خوئینی       | I-PER |                      |
+| 24 |                                                                                                                                                                                           |                                                                                                                                                                                           | و         | نواب           | او       | CCONJ | و            | O     |                      |
+| 25 |                                                                                                                                                                                           |                                                                                                                                                                                           | شمس‌الدین  | رئیس           | شمس‌الدین | PROPN | شمس‌الدین     | B-PER |                      |
+| 26 |                                                                                                                                                                                           |                                                                                                                                                                                           | وهابی     | انتخاب         | وهابی    | PROPN | وهابی        | I-PER |                      |
+| 27 |                                                                                                                                                                                           |                                                                                                                                                                                           | نمایندگان |                | نماینده  | NOUN  | نمایندگان    | O     |                      |
+| 28 |                                                                                                                                                                                           |                                                                                                                                                                                           | مردم      |                | مردم     | NOUN  | مردم         | O     |                      |
+| 29 |                                                                                                                                                                                           |                                                                                                                                                                                           | تهران     |                | تهران    | PROPN | تهران        | B-LOC |                      |
+| 30 |                                                                                                                                                                                           |                                                                                                                                                                                           | به        |                | به       | ADP   | به           | O     |                      |
+| 31 |                                                                                                                                                                                           |                                                                                                                                                                                           | عنوان     |                | عنوان    | NOUN  | عنوان        | O     |                      |
+| 32 |                                                                                                                                                                                           |                                                                                                                                                                                           | نواب      |                | نواب     | NOUN  | نواب         | O     |                      |
+| 33 |                                                                                                                                                                                           |                                                                                                                                                                                           | رئیس      |                | رئیس     | NOUN  | رئیس         | O     |                      |
+| 34 |                                                                                                                                                                                           |                                                                                                                                                                                           | انتخاب    |                | انتخاب   | NOUN  | انتخاب       | O     |                      |
+| 35 |                                                                                                                                                                                           |                                                                                                                                                                                           | شدند      |                | کرد#کن   | VERB  | شدند         | O     |                      |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|recognize_entities_dl|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|fa|
+|Size:|1.2 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- StopWordsCleaner
+- LemmatizerModel
+- PerceptronModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-26-roberta_base_token_classifier_ontonotes_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-roberta_base_token_classifier_ontonotes_pipeline_en.md
new file mode 100644
index 00000000000000..52cb3c799c48dc
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-26-roberta_base_token_classifier_ontonotes_pipeline_en.md
@@ -0,0 +1,104 @@
+---
+layout: model
+title: RoBERTa Base Ontonotes NER Pipeline
+author: John Snow Labs
+name: roberta_base_token_classifier_ontonotes_pipeline
+date: 2023-05-26
+tags: [open_source, ner, token_classifier, roberta, ontonotes, en]
+task: Named Entity Recognition
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline is built on the top of [roberta_base_token_classifier_ontonotes](https://nlp.johnsnowlabs.com/2021/09/26/roberta_base_token_classifier_ontonotes_en.html) model.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.4_1685066871009.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.4_1685066871009.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+pipeline = PretrainedPipeline("roberta_base_token_classifier_ontonotes_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")
+```
+```scala
+
+val pipeline = new PretrainedPipeline("roberta_base_token_classifier_ontonotes_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020."))
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+pipeline = PretrainedPipeline("roberta_base_token_classifier_ontonotes_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")
+```
+```scala
+val pipeline = new PretrainedPipeline("roberta_base_token_classifier_ontonotes_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020."))
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
++--------------+---------+
+|chunk         |ner_label|
++--------------+---------+
+|John          |PERSON   |
+|John Snow Labs|ORG      |
+|November 2020 |DATE     |
++--------------+---------+
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|roberta_base_token_classifier_ontonotes_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|456.5 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetectorDLModel
+- TokenizerModel
+- RoBertaForTokenClassification
+- NerConverter
+- Finisher
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-26-roberta_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-roberta_large_token_classifier_conll03_pipeline_en.md
new file mode 100644
index 00000000000000..617edc160336e2
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-26-roberta_large_token_classifier_conll03_pipeline_en.md
@@ -0,0 +1,106 @@
+---
+layout: model
+title: RoBERTa Large CoNLL-03 NER Pipeline
+author: John Snow Labs
+name: roberta_large_token_classifier_conll03_pipeline
+date: 2023-05-26
+tags: [open_source, ner, token_classifier, roberta, conll03, en]
+task: Named Entity Recognition
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline is built on the top of [roberta_large_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/26/roberta_large_token_classifier_conll03_en.html) model.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_large_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685067174083.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_large_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685067174083.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+
+pipeline = PretrainedPipeline("roberta_large_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I work at John Snow Labs.")
+```
+```scala
+
+
+val pipeline = new PretrainedPipeline("roberta_large_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I work at John Snow Labs."))
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+pipeline = PretrainedPipeline("roberta_large_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I work at John Snow Labs.")
+```
+```scala
+val pipeline = new PretrainedPipeline("roberta_large_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I work at John Snow Labs."))
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
+
++--------------+---------+
+|chunk         |ner_label|
++--------------+---------+
+|John          |PERSON   |
+|John Snow Labs|ORG      |
++--------------+---------+
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|roberta_large_token_classifier_conll03_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|1.3 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetectorDLModel
+- TokenizerModel
+- RoBertaForTokenClassification
+- NerConverter
+- Finisher
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-26-roberta_token_classifier_bne_capitel_ner_pipeline_es.md b/docs/_posts/ahmedlone127/2023-05-26-roberta_token_classifier_bne_capitel_ner_pipeline_es.md
new file mode 100644
index 00000000000000..8ac615bb296f92
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-26-roberta_token_classifier_bne_capitel_ner_pipeline_es.md
@@ -0,0 +1,104 @@
+---
+layout: model
+title: Spanish NER Pipeline
+author: John Snow Labs
+name: roberta_token_classifier_bne_capitel_ner_pipeline
+date: 2023-05-26
+tags: [roberta, token_classifier, spanish, ner, es, open_source]
+task: Named Entity Recognition
+language: es
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline is built on the top of [roberta_token_classifier_bne_capitel_ner_es](https://nlp.johnsnowlabs.com/2021/12/07/roberta_token_classifier_bne_capitel_ner_es.html) model.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_bne_capitel_ner_pipeline_es_4.4.2_3.4_1685064748634.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_bne_capitel_ner_pipeline_es_4.4.2_3.4_1685064748634.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+pipeline = PretrainedPipeline("roberta_token_classifier_bne_capitel_ner_pipeline", lang = "es")
+
+pipeline.annotate("Me llamo Antonio y trabajo en la fábrica de Mercedes-Benz en Madrid.")
+```
+```scala
+
+val pipeline = new PretrainedPipeline("roberta_token_classifier_bne_capitel_ner_pipeline", lang = "es")
+
+pipeline.annotate("Me llamo Antonio y trabajo en la fábrica de Mercedes-Benz en Madrid.")
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+pipeline = PretrainedPipeline("roberta_token_classifier_bne_capitel_ner_pipeline", lang = "es")
+
+pipeline.annotate("Me llamo Antonio y trabajo en la fábrica de Mercedes-Benz en Madrid.")
+```
+```scala
+val pipeline = new PretrainedPipeline("roberta_token_classifier_bne_capitel_ner_pipeline", lang = "es")
+
+pipeline.annotate("Me llamo Antonio y trabajo en la fábrica de Mercedes-Benz en Madrid.")
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
++------------------------+---------+
+|chunk                   |ner_label|
++------------------------+---------+
+|Antonio                 |PER      |
+|fábrica de Mercedes-Benz|ORG      |
+|Madrid                  |LOC      |
++------------------------+---------+
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|roberta_token_classifier_bne_capitel_ner_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|es|
+|Size:|459.4 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetectorDLModel
+- TokenizerModel
+- RoBertaForTokenClassification
+- NerConverter
+- Finisher
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-26-roberta_token_classifier_icelandic_ner_pipeline_is.md b/docs/_posts/ahmedlone127/2023-05-26-roberta_token_classifier_icelandic_ner_pipeline_is.md
new file mode 100644
index 00000000000000..b128a453a67fd2
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-26-roberta_token_classifier_icelandic_ner_pipeline_is.md
@@ -0,0 +1,106 @@
+---
+layout: model
+title: Icelandic NER Pipeline
+author: John Snow Labs
+name: roberta_token_classifier_icelandic_ner_pipeline
+date: 2023-05-26
+tags: [open_source, ner, token_classifier, roberta, icelandic, is]
+task: Named Entity Recognition
+language: is
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline is built on the top of [roberta_token_classifier_icelandic_ner](https://nlp.johnsnowlabs.com/2021/12/06/roberta_token_classifier_icelandic_ner_is.html) model.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_icelandic_ner_pipeline_is_4.4.2_3.4_1685064909820.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_icelandic_ner_pipeline_is_4.4.2_3.4_1685064909820.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+pipeline = PretrainedPipeline("roberta_token_classifier_icelandic_ner_pipeline", lang = "is")
+
+pipeline.annotate("Ég heiti Peter Fergusson. Ég hef búið í New York síðan í október 2011 og unnið hjá Tesla Motor og þénað 100K $ á ári.")
+```
+```scala
+
+val pipeline = new PretrainedPipeline("roberta_token_classifier_icelandic_ner_pipeline", lang = "is")
+
+pipeline.annotate("Ég heiti Peter Fergusson. Ég hef búið í New York síðan í október 2011 og unnið hjá Tesla Motor og þénað 100K $ á ári.")
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+pipeline = PretrainedPipeline("roberta_token_classifier_icelandic_ner_pipeline", lang = "is")
+
+pipeline.annotate("Ég heiti Peter Fergusson. Ég hef búið í New York síðan í október 2011 og unnið hjá Tesla Motor og þénað 100K $ á ári.")
+```
+```scala
+val pipeline = new PretrainedPipeline("roberta_token_classifier_icelandic_ner_pipeline", lang = "is")
+
+pipeline.annotate("Ég heiti Peter Fergusson. Ég hef búið í New York síðan í október 2011 og unnið hjá Tesla Motor og þénað 100K $ á ári.")
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
++----------------+------------+
+|chunk           |ner_label   |
++----------------+------------+
+|Peter Fergusson |Person      |
+|New York        |Location    |
+|október 2011    |Date        |
+|Tesla Motor     |Organization|
+|100K $          |Money       |
++----------------+------------+
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|roberta_token_classifier_icelandic_ner_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|is|
+|Size:|457.5 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetectorDLModel
+- TokenizerModel
+- RoBertaForTokenClassification
+- NerConverter
+- Finisher
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-26-roberta_token_classifier_timex_semeval_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-roberta_token_classifier_timex_semeval_pipeline_en.md
new file mode 100644
index 00000000000000..973b35a190fd97
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-26-roberta_token_classifier_timex_semeval_pipeline_en.md
@@ -0,0 +1,110 @@
+---
+layout: model
+title: Pipeline to Detect Time-related Terminology
+author: John Snow Labs
+name: roberta_token_classifier_timex_semeval_pipeline
+date: 2023-05-26
+tags: [timex, semeval, ner, en, open_source]
+task: Named Entity Recognition
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline is built on the top of [roberta_token_classifier_timex_semeval](https://nlp.johnsnowlabs.com/2021/12/28/roberta_token_classifier_timex_semeval_en.html) model.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_timex_semeval_pipeline_en_4.4.2_3.4_1685062894274.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_timex_semeval_pipeline_en_4.4.2_3.4_1685062894274.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+
+timex_pipeline = PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en")
+
+timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.")
+```
+```scala
+
+
+val timex_pipeline = new PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en")
+
+timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.")
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+timex_pipeline = PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en")
+
+timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.")
+```
+```scala
+val timex_pipeline = new PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en")
+
+timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.")
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
+
++-------+-----------------+
+|chunk  |ner_label        |
++-------+-----------------+
+|22:12C |Period           |
+|3      |Number           |
+|days   |Calendar-Interval|
+|Tuesday|Day-Of-Week      |
+|to     |Between          |
+|Friday |Day-Of-Week      |
++-------+-----------------+
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|roberta_token_classifier_timex_semeval_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|439.5 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- RoBertaForTokenClassification
+- NerConverter
+- Finisher
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-26-spellcheck_dl_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-spellcheck_dl_pipeline_en.md
new file mode 100644
index 00000000000000..fa564958af1b5c
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-26-spellcheck_dl_pipeline_en.md
@@ -0,0 +1,112 @@
+---
+layout: model
+title: Context Spell Checker Pipeline for English
+author: John Snow Labs
+name: spellcheck_dl_pipeline
+date: 2023-05-26
+tags: [spellcheck, spell, spellcheck_pipeline, spelling_corrector, en, open_source]
+task: Spell Check
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained spellchecker pipeline is built on the top of [spellcheck_dl](https://nlp.johnsnowlabs.com/2022/04/02/spellcheck_dl_en_2_4.html) model. This pipeline is for PySpark 2.4.x users with SparkNLP 3.4.2 and above.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/spellcheck_dl_pipeline_en_4.4.2_3.4_1685063898608.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/spellcheck_dl_pipeline_en_4.4.2_3.4_1685063898608.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+
+pipeline = PretrainedPipeline("spellcheck_dl_pipeline", lang = "en")
+
+text = ["During the summer we have the best ueather.", "I have a black ueather jacket, so nice."]
+
+pipeline.annotate(text)
+```
+```scala
+
+
+val pipeline = new PretrainedPipeline("spellcheck_dl_pipeline", lang = "en")
+
+val example = Array("During the summer we have the best ueather.", "I have a black ueather jacket, so nice.")
+
+pipeline.annotate(example)
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+pipeline = PretrainedPipeline("spellcheck_dl_pipeline", lang = "en")
+
+text = ["During the summer we have the best ueather.", "I have a black ueather jacket, so nice."]
+
+pipeline.annotate(text)
+```
+```scala
+val pipeline = new PretrainedPipeline("spellcheck_dl_pipeline", lang = "en")
+
+val example = Array("During the summer we have the best ueather.", "I have a black ueather jacket, so nice.")
+
+pipeline.annotate(example)
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
+
+[{'checked': ['During', 'the', 'summer', 'we', 'have', 'the', 'best', 'weather', '.'],
+  'document': ['During the summer we have the best ueather.'],
+  'token': ['During', 'the', 'summer', 'we', 'have', 'the', 'best', 'ueather', '.']},
+
+ {'checked': ['I', 'have', 'a', 'black', 'leather', 'jacket', ',', 'so', 'nice',  '.'],
+  'document': ['I have a black ueather jacket, so nice.'],
+  'token': ['I', 'have', 'a', 'black', 'ueather', 'jacket', ',', 'so', 'nice', '.']}]
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|spellcheck_dl_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|99.7 MB|
+
+## Included Models
+
+- DocumentAssembler
+- TokenizerModel
+- ContextSpellCheckerModel
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_base_token_classifier_conll03_pipeline_en.md
new file mode 100644
index 00000000000000..83c7e0e7dc12cd
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_base_token_classifier_conll03_pipeline_en.md
@@ -0,0 +1,106 @@
+---
+layout: model
+title: XLM-RoBERTa Base, CoNLL-03 NER Pipeline
+author: John Snow Labs
+name: xlm_roberta_base_token_classifier_conll03_pipeline
+date: 2023-05-26
+tags: [open_source, ner, token_classifier, xlm_roberta, conll03, xlm, base, en]
+task: Named Entity Recognition
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline is built on the top of [xlm_roberta_base_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/10/03/xlm_roberta_base_token_classifier_conll03_en.html) model.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlm_roberta_base_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685066235304.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_roberta_base_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685066235304.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+
+pipeline = PretrainedPipeline("xlm_roberta_base_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I work at John Snow Labs.")
+```
+```scala
+
+
+val pipeline = new PretrainedPipeline("xlm_roberta_base_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I work at John Snow Labs.")
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+pipeline = PretrainedPipeline("xlm_roberta_base_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I work at John Snow Labs.")
+```
+```scala
+val pipeline = new PretrainedPipeline("xlm_roberta_base_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I work at John Snow Labs.")
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
+
++--------------+---------+
+|chunk         |ner_label|
++--------------+---------+
+|John          |PERSON   |
+|John Snow Labs|ORG      |
++--------------+---------+
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|xlm_roberta_base_token_classifier_conll03_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|851.9 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetectorDLModel
+- TokenizerModel
+- XlmRoBertaForTokenClassification
+- NerConverter
+- Finisher
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md
new file mode 100644
index 00000000000000..bfc143f8889183
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md
@@ -0,0 +1,107 @@
+---
+layout: model
+title: XLM-RoBERTa Base NER Pipeline
+author: John Snow Labs
+name: xlm_roberta_base_token_classifier_ontonotes_pipeline
+date: 2023-05-26
+tags: [open_source, ner, token_classifier, xlm_roberta, ontonotes, xlm, en]
+task: Named Entity Recognition
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline is built on the top of [xlm_roberta_base_token_classifier_ontonotes](https://nlp.johnsnowlabs.com/2021/10/03/xlm_roberta_base_token_classifier_ontonotes_en.html) model.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlm_roberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.4_1685065973037.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_roberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.4_1685065973037.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+
+pipeline = PretrainedPipeline("xlm_roberta_base_token_classifier_ontonotes_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")
+```
+```scala
+
+
+val pipeline = new PretrainedPipeline("xlm_roberta_base_token_classifier_ontonotes_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+pipeline = PretrainedPipeline("xlm_roberta_base_token_classifier_ontonotes_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")
+```
+```scala
+val pipeline = new PretrainedPipeline("xlm_roberta_base_token_classifier_ontonotes_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
+
++--------------+---------+
+|chunk         |ner_label|
++--------------+---------+
+|John          |PERSON   |
+|John Snow Labs|ORG      |
+|November 2020 |DATE     |
++--------------+---------+
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|xlm_roberta_base_token_classifier_ontonotes_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|858.4 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetectorDLModel
+- TokenizerModel
+- XlmRoBertaForTokenClassification
+- NerConverter
+- Finisher
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_large_token_classifier_conll03_pipeline_de.md b/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_large_token_classifier_conll03_pipeline_de.md
new file mode 100644
index 00000000000000..d7b6d8ec978ee5
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_large_token_classifier_conll03_pipeline_de.md
@@ -0,0 +1,104 @@
+---
+layout: model
+title: NER Pipeline for German
+author: John Snow Labs
+name: xlm_roberta_large_token_classifier_conll03_pipeline
+date: 2023-05-26
+tags: [german, roberta, xlm, ner, conll03, de, open_source]
+task: Named Entity Recognition
+language: de
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline is built on the top of [xlm_roberta_large_token_classifier_conll03_de](https://nlp.johnsnowlabs.com/2021/12/25/xlm_roberta_large_token_classifier_conll03_de.html) model.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_conll03_pipeline_de_4.4.2_3.4_1685064550521.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_conll03_pipeline_de_4.4.2_3.4_1685064550521.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_conll03_pipeline", lang = "de")
+
+pipeline.annotate("Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte.")
+```
+```scala
+
+val pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_conll03_pipeline", lang = "de")
+
+pipeline.annotate("Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte.")
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_conll03_pipeline", lang = "de")
+
+pipeline.annotate("Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte.")
+```
+```scala
+val pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_conll03_pipeline", lang = "de")
+
+pipeline.annotate("Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte.")
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
++----------------------+---------+
+|chunk                 |ner_label|
++----------------------+---------+
+|Ibser                 |PER      |
+|ASK Ebreichsdorf      |ORG      |
+|Admira Wacker Mödling |ORG      |
++----------------------+---------+
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|xlm_roberta_large_token_classifier_conll03_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|de|
+|Size:|1.8 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetectorDLModel
+- TokenizerModel
+- XlmRoBertaForTokenClassification
+- NerConverter
+- Finisher
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md
new file mode 100644
index 00000000000000..32a4f08022b69b
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md
@@ -0,0 +1,104 @@
+---
+layout: model
+title: NER Pipeline for 10 High Resourced Languages
+author: John Snow Labs
+name: xlm_roberta_large_token_classifier_hrl_pipeline
+date: 2023-05-26
+tags: [arabic, german, english, spanish, french, italian, latvian, dutch, portuguese, chinese, xlm, roberta, ner, xx, open_source]
+task: Named Entity Recognition
+language: xx
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline is built on the top of [xlm_roberta_large_token_classifier_hrl](https://nlp.johnsnowlabs.com/2021/12/26/xlm_roberta_large_token_classifier_hrl_xx.html) model.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_hrl_pipeline_xx_4.4.2_3.4_1685063715742.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_hrl_pipeline_xx_4.4.2_3.4_1685063715742.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_hrl_pipeline", lang = "xx")
+
+pipeline.annotate("يمكنكم مشاهدة أمير منطقة الرياض الأمير فيصل بن بندر بن عبد العزيز في كل مناسبة وافتتاح تتعلق بمشاريع التعليم والصحة وخدمة الطرق والمشاريع الثقافية في منطقة الرياض.")
+```
+```scala
+
+val pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_hrl_pipeline", lang = "xx")
+
+pipeline.annotate("يمكنكم مشاهدة أمير منطقة الرياض الأمير فيصل بن بندر بن عبد العزيز في كل مناسبة وافتتاح تتعلق بمشاريع التعليم والصحة وخدمة الطرق والمشاريع الثقافية في منطقة الرياض.")
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_hrl_pipeline", lang = "xx")
+
+pipeline.annotate("يمكنكم مشاهدة أمير منطقة الرياض الأمير فيصل بن بندر بن عبد العزيز في كل مناسبة وافتتاح تتعلق بمشاريع التعليم والصحة وخدمة الطرق والمشاريع الثقافية في منطقة الرياض.")
+```
+```scala
+val pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_hrl_pipeline", lang = "xx")
+
+pipeline.annotate("يمكنكم مشاهدة أمير منطقة الرياض الأمير فيصل بن بندر بن عبد العزيز في كل مناسبة وافتتاح تتعلق بمشاريع التعليم والصحة وخدمة الطرق والمشاريع الثقافية في منطقة الرياض.")
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
++---------------------------+---------+
+|chunk                      |ner_label|
++---------------------------+---------+
+|الرياض                     |LOC      |
+|فيصل بن بندر بن عبد العزيز |PER      |
+|الرياض                     |LOC      |
++---------------------------+---------+
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|xlm_roberta_large_token_classifier_hrl_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|xx|
+|Size:|1.8 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- XlmRoBertaForTokenClassification
+- NerConverter
+- Finisher
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md
new file mode 100644
index 00000000000000..effb202a3b61c5
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md
@@ -0,0 +1,104 @@
+---
+layout: model
+title: NER Pipeline for 10 African Languages
+author: John Snow Labs
+name: xlm_roberta_large_token_classifier_masakhaner_pipeline
+date: 2023-05-26
+tags: [masakhaner, african, xlm_roberta, multilingual, pipeline, amharic, hausa, igbo, kinyarwanda, luganda, swahilu, wolof, yoruba, nigerian, pidgin, xx, open_source]
+task: Named Entity Recognition
+language: xx
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline is built on [xlm_roberta_large_token_classifier_masakhaner](https://nlp.johnsnowlabs.com/2021/12/06/xlm_roberta_large_token_classifier_masakhaner_xx.html) ner model which is imported from `HuggingFace`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_masakhaner_pipeline_xx_4.4.2_3.4_1685061875856.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_masakhaner_pipeline_xx_4.4.2_3.4_1685061875856.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+masakhaner_pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_masakhaner_pipeline", lang = "xx")
+
+masakhaner_pipeline.annotate("አህመድ ቫንዳ ከ3-10-2000 ጀምሮ በአዲስ አበባ ኖሯል።")
+```
+```scala
+
+val masakhaner_pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_masakhaner_pipeline", lang = "xx")
+
+val masakhaner_pipeline.annotate("አህመድ ቫንዳ ከ3-10-2000 ጀምሮ በአዲስ አበባ ኖሯል።")
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+masakhaner_pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_masakhaner_pipeline", lang = "xx")
+
+masakhaner_pipeline.annotate("አህመድ ቫንዳ ከ3-10-2000 ጀምሮ በአዲስ አበባ ኖሯል።")
+```
+```scala
+val masakhaner_pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_masakhaner_pipeline", lang = "xx")
+
+val masakhaner_pipeline.annotate("አህመድ ቫንዳ ከ3-10-2000 ጀምሮ በአዲስ አበባ ኖሯል።")
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
++----------------+---------+
+|chunk           |ner_label|
++----------------+---------+
+|አህመድ ቫንዳ      |PER      |
+|ከ3-10-2000 ጀምሮ|DATE      |
+|በአዲስ አበባ       |LOC      |
++----------------+---------+
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|xlm_roberta_large_token_classifier_masakhaner_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|xx|
+|Size:|1.8 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- XlmRoBertaForTokenClassification
+- NerConverter
+- Finisher
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-26-xlnet_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-xlnet_base_token_classifier_conll03_pipeline_en.md
new file mode 100644
index 00000000000000..aa40cf3ff9bbcc
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-26-xlnet_base_token_classifier_conll03_pipeline_en.md
@@ -0,0 +1,106 @@
+---
+layout: model
+title: XLNet Base CoNLL-03 NER Pipeline
+author: John Snow Labs
+name: xlnet_base_token_classifier_conll03_pipeline
+date: 2023-05-26
+tags: [ner, english, xlnet, base, token_classification, en, open_source]
+task: Named Entity Recognition
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline is built on the top of [xlnet_base_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/28/xlnet_base_token_classifier_conll03_en.html) model.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlnet_base_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685065733321.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlnet_base_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685065733321.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+
+pipeline = PretrainedPipeline("xlnet_base_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I work at John Snow Labs.")
+```
+```scala
+
+
+val pipeline = new PretrainedPipeline("xlnet_base_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I work at John Snow Labs.")
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+pipeline = PretrainedPipeline("xlnet_base_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I work at John Snow Labs.")
+```
+```scala
+val pipeline = new PretrainedPipeline("xlnet_base_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I work at John Snow Labs.")
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
+
++--------------+---------+
+|chunk         |ner_label|
++--------------+---------+
+|John          |PER      |
+|John Snow Labs|ORG      |
++--------------+---------+
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|xlnet_base_token_classifier_conll03_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|438.6 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetectorDLModel
+- TokenizerModel
+- XlnetForTokenClassification
+- NerConverter
+- Finisher
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-05-26-xlnet_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-xlnet_large_token_classifier_conll03_pipeline_en.md
new file mode 100644
index 00000000000000..ed3e5055d40756
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-05-26-xlnet_large_token_classifier_conll03_pipeline_en.md
@@ -0,0 +1,103 @@
+---
+layout: model
+title: XLNet Large CoNLL-03 NER Pipeline
+author: John Snow Labs
+name: xlnet_large_token_classifier_conll03_pipeline
+date: 2023-05-26
+tags: [open_source, ner, token_classifier, xlnet, conll03, large, en]
+task: Named Entity Recognition
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline is built on the top of [xlnet_large_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/28/xlnet_large_token_classifier_conll03_en.html) model.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlnet_large_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685066312653.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlnet_large_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685066312653.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+
+pipeline = PretrainedPipeline("xlnet_large_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I work at John Snow Labs.")
+```
+```scala
+
+
+val pipeline = new PretrainedPipeline("xlnet_large_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I work at John Snow Labs.")
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+pipeline = PretrainedPipeline("xlnet_large_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I work at John Snow Labs.")
+```
+```scala
+val pipeline = new PretrainedPipeline("xlnet_large_token_classifier_conll03_pipeline", lang = "en")
+
+pipeline.annotate("My name is John and I work at John Snow Labs.")
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+
+
++--------------+---------+
+|chunk         |ner_label|
++--------------+---------+
+|John          |PERSON   |
+|John Snow Labs|ORG      |
++--------------+---------+
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|xlnet_large_token_classifier_conll03_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|19.0 KB|
+
+## Included Models
+
+- DocumentAssembler
+- TokenizerModel
+- NormalizerModel
\ No newline at end of file