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:doc:`NER component </components/ner>`

Based on neural Named Entity Recognition network. The NER component reproduces architecture from the paper Application of a Hybrid Bi-LSTM-CRF model to the task of Russian Named Entity Recognition which is inspired by Bi-LSTM+CRF architecture from

Dataset Test F1
:config:`Persons-1000 dataset with additional LOC and ORG markup <ner/ner_rus.json>` 95.25
:config:`DSTC 2 <ner/ner_dstc2.json>` 98.40
:config:`OntoNotes <ner/ner_ontonotes.json>` 87.07

:doc:`Slot filling components </components/slot_filling>`

Based on fuzzy Levenshtein search to extract normalized slot values from text. The components either rely on NER results or perform needle in haystack search.

Dataset Slots Accuracy
:config:`DSTC 2 <ner/slotfill_dstc2.json>` 98.85

:doc:`Classification component </components/classifiers>`

Component for classification tasks (intents, sentiment, etc) on word-level. Shallow-and-wide CNN, Deep CNN, BiLSTM, BiLSTM with self-attention and other models are presented. The model also allows multilabel classification of texts. Several pre-trained models are available and presented in Table below.

Task Dataset Lang Model Metric Valid Test Downloads
28 intents DSTC 2 En :config:`DSTC 2 emb <classifiers/intents_dstc2.json>` Accuracy 0.7732 0.7868 800 Mb
:config:`Wiki emb <classifiers/intents_dstc2_big.json>` 0.9602 0.9593 8.5 Gb
7 intents SNIPS-2017 :config:`DSTC 2 emb <classifiers/intents_snips.json>` F1 0.8685 -- 800 Mb
:config:`Wiki emb <classifiers/intents_snips_big.json>` 0.9811 -- 8.5 Gb
:config:`Tfidf + SelectKBest + PCA + Wiki emb <classifiers/intents_snips_sklearn.json>` 0.9673 -- 8.6 Gb
:config:`Wiki emb weighted by Tfidf <classifiers/intents_snips_tfidf_weighted.json>` 0.9786 -- 8.5 Gb
Insult detection Insults :config:`Reddit emb <classifiers/insults_kaggle.json>` ROC-AUC 0.9271 0.8618 6.2 Gb
5 topics AG News :config:`Wiki emb <classifiers/topic_ag_news.json>` Accuracy 0.8876 0.9011 8.5 Gb
Sentiment Twitter mokoron Ru :config:`RuWiki+Lenta emb w/o preprocessing <classifiers/sentiment_twitter.json>` 0.9972 0.9971 6.2 Gb
:config:`RuWiki+Lenta emb with preprocessing <classifiers/sentiment_twitter_preproc.json>` 0.7811 0.7749 6.2 Gb
RuSentiment :config:`RuWiki+Lenta emb <classifiers/rusentiment_cnn.json>` F1 0.6393 0.6539 6.2 Gb
:config:`ELMo <classifiers/rusentiment_elmo.json>` 0.7066 0.7301 700 Mb
Intent Yahoo-L31 :config:`Yahoo-L31 on ELMo <classifiers/yahoo_convers_vs_info.json>` pre-trained on Yahoo-L6 ROC-AUC 0.9269 -- 700 Mb

As no one had published intent recognition for DSTC-2 data, the comparison of the presented model is given on SNIPS dataset. The evaluation of model scores was conducted in the same way as in [3] to compare with the results from the report of the authors of the dataset. The results were achieved with tuning of parameters and embeddings trained on Reddit dataset.

Model AddToPlaylist BookRestaurant GetWheather PlayMusic RateBook SearchCreativeWork SearchScreeningEvent 0.9931 0.9949 0.9935 0.9811 0.9992 0.9659 0.9801
ibm.watson 0.9931 0.9950 0.9950 0.9822 0.9996 0.9643 0.9750
microsoft.luis 0.9943 0.9935 0.9925 0.9815 0.9988 0.9620 0.9749 0.9877 0.9913 0.9921 0.9766 0.9977 0.9458 0.9673 0.9873 0.9921 0.9939 0.9729 0.9985 0.9455 0.9613 0.9894 0.9943 0.9910 0.9660 0.9981 0.9424 0.9539
amazon.lex 0.9930 0.9862 0.9825 0.9709 0.9981 0.9427 0.9581
Shallow-and-wide CNN 0.9956 0.9973 0.9968 0.9871 0.9998 0.9752 0.9854

:doc:`Goal-oriented bot </skills/go_bot>`

Based on Hybrid Code Networks (HCNs) architecture from Jason D. Williams, Kavosh Asadi, Geoffrey Zweig, Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning – 2017. It allows to predict responses in goal-oriented dialog. The model is customizable: embeddings, slot filler and intent classifier can be switched on and off on demand.

Available pre-trained models:

Dataset & Model Valid turn accuracy Test turn accuracy Downloads
:config:`DSTC2, bot with slot filler & intents <go_bot/gobot_dstc2.json>` 0.5288 0.5248 8.5 Gb
:config:`DSTC2, bot with slot filler & embeddings & attention <go_bot/gobot_dstc2_best.json>` 0.5538 0.5551 8.5 Gb

Other benchmarks on DSTC2 (can't be directly compared due to dataset :doc:`modifications </skills/go_bot>`):

Dataset & Model Test turn accuracy
DSTC2, Bordes and Weston (2016) 0.411
DSTC2, Perez and Liu (2016) 0.487
DSTC2, Eric and Manning (2017) 0.480
DSTC2, Williams et al. (2017) 0.556

:doc:`Seq2seq goal-oriented bot </skills/seq2seq_go_bot>`

Dialogue agent predicts responses in a goal-oriented dialog and is able to handle multiple domains (pretrained bot allows calendar scheduling, weather information retrieval, and point-of-interest navigation). The model is end-to-end differentiable and does not need to explicitly model dialogue state or belief trackers.

Comparison of deeppavlov pretrained model with others:

Dataset & Model Valid BLEU Test BLEU Downloads
:config:`Kvret, KvretNet <go_bot/gobot_dstc2.json>` 0.1319 0.1328 10 Gb
Kvret, KvretNet, Mihail Eric et al. (2017) -- 0.132 --
Kvret, CopyNet, Mihail Eric et al. (2017) -- 0.110 --
Kvret, Attn Seq2Seq, Mihail Eric et al. (2017) -- 0.102 --
Kvret, Rule-based, Mihail Eric et al. (2017) -- 0.066 --

:doc:`Automatic spelling correction component </components/spelling_correction>`

Pipelines that use candidates search in a static dictionary and an ARPA language model to correct spelling errors.


About 4.4 GB on disc required for the Russian language model and about 7 GB for the English one.

Comparison on the test set for the SpellRuEval competition on Automatic Spelling Correction for Russian:

Correction method Precision Recall F-measure Speed (sentences/s)
Yandex.Speller 83.09 59.86 69.59
:config:`Damerau Levenshtein 1 + lm<spelling_correction/levenshtein_corrector_ru.json>` 53.26 53.74 53.50 29.3
:config:`Brill Moore top 4 + lm<spelling_correction/brillmoore_kartaslov_ru.json>` 51.92 53.94 52.91 0.6
Hunspell + lm 41.03 48.89 44.61 2.1
JamSpell 44.57 35.69 39.64 136.2
:config:`Brill Moore top 1 <spelling_correction/brillmoore_kartaslov_ru_nolm.json>` 41.29 37.26 39.17 2.4
Hunspell 30.30 34.02 32.06 20.3

:doc:`Ranking component </components/neural_ranking>`

Based on LSTM-based deep learning models for non-factoid answer selection. The model performs ranking of responses or contexts from some database by their relevance for the given context.

Available pre-trained models for ranking:

Dataset Model config Validation (Recall@1) Test1 (Recall@1) Downloads
InsuranceQA V1 :config:`ranking_insurance_interact <ranking/ranking_insurance_interact.json>` 72.0 72.2 8374M
Ubuntu V2 :config:`ranking_ubuntu_v2_interact <ranking/ranking_ubuntu_v2_interact.json>` 52.9 52.4 8913M
Ubuntu V2 :config:`ranking_ubuntu_v2_mt_interact <ranking/ranking_ubuntu_v2_mt_interact.json>` 59.2 58.7 8906M

Available pre-trained models for paraphrase identification:

Dataset Model config Val (accuracy) Test (accuracy) Val (F1) Test (F1) Val (log_loss) Test (log_loss) Downloads :config:`paraphrase_ident_paraphraser <ranking/paraphrase_ident_paraphraser_interact.json>` 83.8 75.4 87.9 80.9 0.468 0.616 5938M
Quora Question Pairs :config:`paraphrase_ident_qqp <ranking/paraphrase_ident_qqp_bilstm_interact.json>` 87.1 87.0 83.0 82.6 0.300 0.305 8134M
Quora Question Pairs :config:`paraphrase_ident_qqp <ranking/paraphrase_ident_qqp_interact.json>` 87.7 87.5 84.0 83.8 0.287 0.298 8136M

Comparison with other models on the InsuranceQA V1:

Model Validation (Recall@1) Test1 (Recall@1)
Architecture II (HLQA(200) CNNQA(4000) 1-MaxPooling Tanh) 61.8 62.8
QA-LSTM basic-model(max pooling) 64.3 63.1
:config:`ranking_insurance <ranking/ranking_insurance_interact.json>` 72.0 72.2

:doc:`TF-IDF Ranker component </components/tfidf_ranking>`

Based on Reading Wikipedia to Answer Open-Domain Questions. The model solves the task of document retrieval for a given query.

Dataset Model Wiki dump Recall@5 Downloads
SQuAD-v1.1 :config:`doc_retrieval <doc_retrieval/en_ranker_tfidf_wiki.json>` enwiki (2018-02-11) 75.6 33 GB

:doc:`Question Answering component </components/squad>`

Based on R-NET: Machine Reading Comprehension with Self-matching Networks. The model solves the task of looking for an answer on a question in a given context (SQuAD task format).


All pre-trained models could be downloaded. Model for English language will download about 2.5 Gb and model for Russian about 5 Gb.

Dataset Model config lang EM (dev) F-1 (dev)
SQuAD-v1.1 :config:`squad <squad/squad.json>` en 71.49 80.34
SDSJ Task B :config:`squad_ru <squad/squad_ru.json>` ru 60.62 80.04

In the case when answer is not necessary present in given context we have :config:`squad_noans <squad/multi_squad_noans.json>` model. This model outputs empty string in case if there is no answer in context.

:doc:`Morphological tagging component </components/morphotagger>`

Based on character-based approach to morphological tagging Heigold et al., 2017. An extensive empirical evaluation of character-based morphological tagging for 14 languages. A state-of-the-art model for Russian and several other languages. Model takes as input tokenized sentences and outputs the corresponding sequence of morphological labels in UD format. The table below contains word and sentence accuracy on UD2.0 datasets. For more scores see :doc:`full table </components/morphotagger>`.

Dataset Model Word accuracy Sent. accuracy Download size (MB)
UD2.0 (Russian) Pymorphy + russian_tagsets (first tag) 60.93 0.00  
UD Pipe 1.2 (Straka et al., 2017) 93.57 43.04  
:config:`Basic model <morpho_tagger/UD2.0/ru_syntagrus/morpho_ru_syntagrus.json>` 95.17 50.58 48.7
:config:`Pymorphy-enhanced model <morpho_tagger/UD2.0/ru_syntagrus/morpho_ru_syntagrus_pymorphy.json>` 96.23 58.00 48.7
UD2.0 (Czech) UD Pipe 1.2 (Straka et al., 2017) 91.86 42.28  
:config:`Basic model <morpho_tagger/UD2.0/morpho_cs.json>` 94.35 51.56 41.8
UD2.0 (English) UD Pipe 1.2 (Straka et al., 2017) 92.89 55.75  
:config:`Basic model <morpho_tagger/UD2.0/morpho_en.json>` 93.00 55.18 16.9
UD2.0 (German) UD Pipe 1.2 (Straka et al., 2017) 76.65 10.24  
:config:`Basic model <morpho_tagger/UD2.0/morpho_de.json>` 83.83 15.25 18.6

:doc:`Frequently Asked Questions (FAQ) component </skills/faq>`

Set of pipelines for FAQ task: classifying incoming question into set of known questions and return prepared answer. You can build different pipelines based on: tf-idf, weighted fasttext, cosine similarity, logistic regression.


:doc:`eCommerce bot </skills/ecommerce>`

The eCommerce bot intends to retrieve product items from catalog in sorted order. In addition, it asks an user to provide additional information to specify the search.


About 130 Mb on disc required for eCommerce bot with TfIdf-based ranker and 500 Mb for BLEU-based ranker.

:doc:`ODQA </skills/odqa>`

An open domain question answering skill. The skill accepts free-form questions about the world and outputs an answer based on its Wikipedia knowledge.

Dataset Model config Wiki dump F1 Downloads
SQuAD-v1.1 :config:`ODQA <odqa/en_odqa_infer_wiki.json>` enwiki (2018-02-11) 28.0 42 GB


:doc:`Hyperparameters optimization </intro/hypersearch>`

Hyperparameters optimization (either by cross-validation or neural evolution) for DeepPavlov models that requires only some small changes in a config file.


:doc:`Pre-trained embeddings for the Russian language </intro/pretrained_vectors>`

Word vectors for the Russian language trained on joint Russian Wikipedia and corpora.

Examples of some components

  • Run goal-oriented bot with Telegram interface:

    python -m deeppavlov interactbot deeppavlov/configs/go_bot/gobot_dstc2.json -d -t <TELEGRAM_TOKEN>

  • Run goal-oriented bot with console interface:

    python -m deeppavlov interact deeppavlov/configs/go_bot/gobot_dstc2.json -d

  • Run goal-oriented bot with REST API:

    python -m deeppavlov riseapi deeppavlov/configs/go_bot/gobot_dstc2.json -d

  • Run slot-filling model with Telegram interface:

    python -m deeppavlov interactbot deeppavlov/configs/ner/slotfill_dstc2.json -d -t <TELEGRAM_TOKEN>

  • Run slot-filling model with console interface:

    python -m deeppavlov interact deeppavlov/configs/ner/slotfill_dstc2.json -d

  • Run slot-filling model with REST API:

    python -m deeppavlov riseapi deeppavlov/configs/ner/slotfill_dstc2.json -d

  • Predict intents on every line in a file:

    python -m deeppavlov predict deeppavlov/configs/classifiers/intents_snips.json -d --batch-size 15 < /data/in.txt > /data/out.txt

View video demo of deployment of a goal-oriented bot and a slot-filling model with Telegram UI.