JohnSnowLabs · josejuanmartinez · Apr 27, 2023 · Apr 27, 2023 · Apr 27, 2023 · Apr 27, 2023
diff --git a/docs/_posts/bunyamin-polat/2023-04-27-legner_mapa_de.md b/docs/_posts/bunyamin-polat/2023-04-27-legner_mapa_de.md
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+---
+layout: model
+title: Legal NER for MAPA(Multilingual Anonymisation for Public Administrations)
+author: John Snow Labs
+name: legner_mapa
+date: 2023-04-27
+tags: [de, ner, legal, licensed, mapa]
+task: Named Entity Recognition
+language: de
+edition: Legal NLP 1.0.0
+spark_version: 3.0
+supported: true
+annotator: LegalNerModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The dataset consists of 12 documents taken from EUR-Lex, a multilingual corpus of court decisions and legal dispositions in the 24 official languages of the European Union.
+
+This model extracts `ADDRESS`, `AMOUNT`, `DATE`, `ORGANISATION`, and `PERSON` entities from `German` documents.
+
+## Predicted Entities
+
+`ADDRESS`, `AMOUNT`, `DATE`, `ORGANISATION`, `PERSON`
+
+{:.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/legal/models/legner_mapa_de_1.0.0_3.0_1682589773968.zip){:.button.button-orange}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/legal/models/legner_mapa_de_1.0.0_3.0_1682589773968.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
+document_assembler = nlp.DocumentAssembler()\
+        .setInputCol("text")\
+        .setOutputCol("document")
+
+sentence_detector = nlp.SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")\
+        .setInputCols(["document"])\
+        .setOutputCol("sentence")
+
+tokenizer = nlp.Tokenizer()\
+        .setInputCols(["sentence"])\
+        .setOutputCol("token")
+
+embeddings = nlp.BertEmbeddings.pretrained("bert_embeddings_base_de_cased", "de")\
+        .setInputCols(["sentence", "token"])\
+        .setOutputCol("embeddings")\
+        .setMaxSentenceLength(512)\
+        .setCaseSensitive(True)
+
+ner_model = legal.NerModel.pretrained("legner_mapa", "de", "legal/models")\
+        .setInputCols(["sentence", "token", "embeddings"])\
+        .setOutputCol("ner")
+
+ner_converter = nlp.NerConverter()\
+        .setInputCols(["sentence", "token", "ner"])\
+        .setOutputCol("ner_chunk")
+
+nlpPipeline = nlp.Pipeline(stages=[
+        document_assembler,
+        sentence_detector,
+        tokenizer,
+        embeddings,
+        ner_model,
+        ner_converter])
+
+empty_data = spark.createDataFrame([[""]]).toDF("text")
+
+model = nlpPipeline.fit(empty_data)
+
+text = ["""Herr Liberato und Frau Grigorescu heirateten am 22  Oktober 2005 in Rom (Italien) und lebten in diesem Mitgliedstaat bis zur Geburt ihres Kindes am 20 Februar 2006 zusammen."""]
+
+result = model.transform(spark.createDataFrame([text]).toDF("text"))
+```
+
+</div>
+
+## Results
+
+```bash
++----------------+---------+
+|chunk           |ner_label|
++----------------+---------+
+|Herr Liberato   |PERSON   |
+|Frau Grigorescu |PERSON   |
+|22  Oktober 2005|DATE     |
+|Rom (Italien)   |ADDRESS  |
+|20 Februar 2006 |DATE     |
++----------------+---------+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|legner_mapa|
+|Compatibility:|Legal NLP 1.0.0+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[sentence, token, embeddings]|
+|Output Labels:|[ner]|
+|Language:|de|
+|Size:|1.4 MB|
+
+## References
+
+The dataset is available [here](https://huggingface.co/datasets/joelito/mapa).
+
+## Benchmarking
+
+```bash
+label         precision  recall  f1-score  support 
+ADDRESS       0.69       0.85    0.76      13      
+AMOUNT        1.00       0.75    0.86      4       
+DATE          0.92       0.93    0.93      61      
+ORGANISATION  0.64       0.77    0.70      30      
+PERSON        0.85       0.87    0.86      46      
+macro-avg     0.82       0.87    0.84      154     
+macro-avg     0.82       0.83    0.82      154     
+weighted-avg  0.83       0.87    0.85      154     
+```
diff --git a/docs/_posts/bunyamin-polat/2023-04-27-legner_mapa_el.md b/docs/_posts/bunyamin-polat/2023-04-27-legner_mapa_el.md
@@ -0,0 +1,132 @@
+---
+layout: model
+title: Legal NER for MAPA(Multilingual Anonymisation for Public Administrations)
+author: John Snow Labs
+name: legner_mapa
+date: 2023-04-27
+tags: [el, ner, legal, mapa, licensed]
+task: Named Entity Recognition
+language: el
+edition: Legal NLP 1.0.0
+spark_version: 3.0
+supported: true
+annotator: LegalNerModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The dataset consists of 12 documents taken from EUR-Lex, a multilingual corpus of court decisions and legal dispositions in the 24 official languages of the European Union.
+
+This model extracts `ADDRESS`, `AMOUNT`, `DATE`, `ORGANISATION`, and `PERSON` entities from `Greek` documents.
+
+## Predicted Entities
+
+`ADDRESS`, `AMOUNT`, `DATE`, `ORGANISATION`, `PERSON`
+
+{:.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/legal/models/legner_mapa_el_1.0.0_3.0_1682590655353.zip){:.button.button-orange}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/legal/models/legner_mapa_el_1.0.0_3.0_1682590655353.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
+document_assembler = nlp.DocumentAssembler()\
+        .setInputCol("text")\
+        .setOutputCol("document")
+
+sentence_detector = nlp.SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")\
+        .setInputCols(["document"])\
+        .setOutputCol("sentence")
+
+tokenizer = nlp.Tokenizer()\
+        .setInputCols(["sentence"])\
+        .setOutputCol("token")
+
+embeddings = nlp.BertEmbeddings.pretrained("bert_embeddings_base_el_cased", "el")\
+        .setInputCols(["sentence", "token"])\
+        .setOutputCol("embeddings")\
+        .setMaxSentenceLength(512)\
+        .setCaseSensitive(True)
+
+ner_model = legal.NerModel.pretrained("legner_mapa", "el", "legal/models")\
+        .setInputCols(["sentence", "token", "embeddings"])\
+        .setOutputCol("ner")
+
+ner_converter = nlp.NerConverter()\
+        .setInputCols(["sentence", "token", "ner"])\
+        .setOutputCol("ner_chunk")
+
+nlpPipeline = nlp.Pipeline(stages=[
+        document_assembler,
+        sentence_detector,
+        tokenizer,
+        embeddings,
+        ner_model,
+        ner_converter])
+
+empty_data = spark.createDataFrame([[""]]).toDF("text")
+
+model = nlpPipeline.fit(empty_data)
+
+text = ["""86 Στην υπόθεση της κύριας δίκης, προκύπτει ότι ορισμένοι εργαζόμενοι της Martin‑Meat αποσπάσθηκαν στην Αυστρία κατά την περίοδο μεταξύ του έτους 2007 και του έτους 2012, για την εκτέλεση εργασιών τεμαχισμού κρέατος σε εγκαταστάσεις της Alpenrind."""]
+
+result = model.transform(spark.createDataFrame([text]).toDF("text"))
+```
+
+</div>
+
+## Results
+
+```bash
++-----------+------------+
+|chunk      |ner_label   |
++-----------+------------+
+|Martin‑Meat|ORGANISATION|
+|Αυστρία    |ADDRESS     |
+|2007       |DATE        |
+|2012       |DATE        |
+|Alpenrind  |ORGANISATION|
++-----------+------------+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|legner_mapa|
+|Compatibility:|Legal NLP 1.0.0+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[sentence, token, embeddings]|
+|Output Labels:|[ner]|
+|Language:|el|
+|Size:|16.4 MB|
+
+## References
+
+The dataset is available [here](https://huggingface.co/datasets/joelito/mapa).
+
+## Benchmarking
+
+```bash
+label         precision  recall  f1-score  support 
+ADDRESS       0.89       1.00    0.94      16      
+AMOUNT        0.82       0.75    0.78      12      
+DATE          0.98       0.98    0.98      65      
+ORGANISATION  0.85       0.85    0.85      40      
+PERSON        0.90       0.95    0.92      38      
+macro-avg     0.91       0.93    0.92      171     
+macro-avg     0.89       0.91    0.90      171     
+weighted-avg  0.91       0.93    0.92      171     
+```