2022-10-25-legner_indian_court_judgement_en

docs/_posts/bunyamin-polat/2022-10-25-legner_indian_court_judgement_en.md

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+---
+layout: model
+title: Legal NER for Indian Court Documents
+author: John Snow Labs
+name: legner_indian_court_judgement
+date: 2022-10-25
+tags: [en, legal, ner, licensed]
+task: Named Entity Recognition
+language: en
+edition: Spark NLP for Legal 1.0.0
+spark_version: 3.0
+supported: true
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This is an NER model trained on Indian court dataset, aimed to extract the following entities from judgement documents.
+
+## Predicted Entities
+
+`COURT`, `PETITIONER`, `RESPONDENT`, `JUDGE`, `DATE`, `ORG`, `GPE`, `STATUTE`, `PROVISION`, `PRECEDENT`, `CASE_NUMBER`, `WITNESS`, `OTHER_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_indian_court_judgement_en_1.0.0_3.0_1666698501448.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+
+```python
+document_assembler = nlp.DocumentAssembler()\
+    .setInputCol("text")\
+    .setOutputCol("document")\
+    .setCleanupMode("shrink")
+
+sentence_detector = nlp.SentenceDetectorDLModel.pretrained("sentence_detector_dl", "en")\
+    .setInputCols(["document"])\
+    .setOutputCol("sentence")\
+
+tokenizer = nlp.Tokenizer()\
+    .setInputCols(["sentence"])\
+    .setOutputCol("token")
+
+embeddings = nlp.BertEmbeddings.pretrained("bert_base_cased", "en")\
+    .setInputCols("sentence", "token")\
+    .setOutputCol("embeddings")\
+    .setMaxSentenceLength(512)\
+    .setCaseSensitive(True)
+
+ner_model = legal.NerModel.pretrained("legner_indian_court_judgement", "en", "legal/models")\
+    .setInputCols(["sentence", "token", "embeddings"])\
+    .setOutputCol("ner")\
+
+ner_converter = nlp.NerConverter()\
+    .setInputCols(["sentence", "token", "ner"])\
+    .setOutputCol("ner_chunk")
+
+pipeline = Pipeline(stages=[
+    document_assembler,
+    sentence_detector,
+    tokenizer,
+    embeddings,
+    ner_model,
+    ner_converter   
+    ])
+
+model = pipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
+
+data = spark.createDataFrame([["""Let fresh bailable warrant of Rs.20,000/- (Rupees Twenty Thousand) be issued through Superintendent of Police, Dhar to the respondents No.1 Sikandar and No.2 Aziz for a date to be fixed by the Registry to secure the presence of the respondents No.1 and 2, made returnable within six weeks.
+P.K.Jaiswal)  Judge                  
+(Jarat Kumar Jain) Judge ns.
+W.P.No.1361/2013 
+14/12/2015              
+Parties through their Counsel."""]]
+
+result = model.transform(data)
+```
+```scala
+val document_assembler = new DocumentAssembler()
+    .setInputCol("text")
+    .setOutputCol("document")
+    .setCleanupMode("shrink")
+
+val sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "en")
+    .setInputCols("document")
+    .setOutputCol("sentence")
+
+val tokenizer = new Tokenizer()
+    .setInputCols("sentence")
+    .setOutputCol("token")
+
+val embeddings = BertEmbeddings.pretrained("bert_base_cased", "en")
+    .setInputCols(Array("sentence", "token"))
+    .setOutputCol("embeddings")
+    .setMaxSentenceLength(512)
+    .setCaseSensitive(True)
+
+val ner_model = NerModel.pretrained("legner_indian_court_judgement", "en", "legal/models")
+    .setInputCols(Array("sentence", "token", "embeddings"))
+    .setOutputCol("ner")
+
+val ner_converter = new NerConverter()
+    .setInputCols(Array("sentence", "token", "ner"))
+    .setOutputCol("ner_chunk")
+
+val pipeline = new PipelineModel().setStages(Array(
+    document_assembler,
+    sentence_detector,
+    tokenizer,
+    embeddings,
+    ner_model,
+    ner_converter))
+
+val data = Seq("""Let fresh bailable warrant of Rs.20,000/- (Rupees Twenty Thousand) be issued through Superintendent of Police, Dhar to the respondents No.1 Sikandar and No.2 Aziz for a date to be fixed by the Registry to secure the presence of the respondents No.1 and 2, made returnable within six weeks.
+P.K.Jaiswal)  Judge                  
+(Jarat Kumar Jain) Judge ns.
+W.P.No.1361/2013 
+14/12/2015              
+Parties through their Counsel.""").toDS.toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+</div>
+
+## Results
+
+```bash
++----------------+-----------+
+|ticker          |label      |
++----------------+-----------+
+|Dhar            |GPE        |
+|Sikandar        |RESPONDENT |
+|Aziz            |RESPONDENT |
+|P.K.Jaiswal     |JUDGE      |
+|Jarat Kumar Jain|JUDGE      |
+|W.P.No.1361/2013|CASE_NUMBER|
+|14/12/2015      |DATE       |
++----------------+-----------+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|legner_indian_court_judgement|
+|Compatibility:|Spark NLP for Legal 1.0.0+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[sentence, token, embeddings]|
+|Output Labels:|[ner]|
+|Language:|en|
+|Size:|16.4 MB|
+
+## References
+
+Training data is available [here](https://github.com/Legal-NLP-EkStep/legal_NER#3-data).
+
+## Benchmarking
+
+```bash
+| label        | precision | recall | f1-score | support |
+|--------------|-----------|--------|----------|---------|
+| CASE_NUMBER  | 0.83      | 0.80   | 0.82     | 112     |
+| COURT        | 0.92      | 0.94   | 0.93     | 140     |
+| DATE         | 0.97      | 0.97   | 0.97     | 204     |
+| GPE          | 0.81      | 0.75   | 0.78     | 95      |
+| JUDGE        | 0.84      | 0.86   | 0.85     | 57      |
+| ORG          | 0.75      | 0.76   | 0.76     | 131     |
+| OTHER_PERSON | 0.83      | 0.90   | 0.86     | 241     |
+| PETITIONER   | 0.76      | 0.61   | 0.68     | 36      |
+| PRECEDENT    | 0.84      | 0.84   | 0.84     | 127     |
+| PROVISION    | 0.90      | 0.94   | 0.92     | 220     |
+| RESPONDENT   | 0.64      | 0.70   | 0.67     | 23      |
+| STATUTE      | 0.92      | 0.96   | 0.94     | 157     |
+| WITNESS      | 0.93      | 0.78   | 0.85     | 87      |
+| micro-avg    | 0.87      | 0.87   | 0.87     | 1630    |
+| macro-avg    | 0.84      | 0.83   | 0.83     | 1630    |
+| weighted-avg | 0.87      | 0.87   | 0.87     | 1630    |
+```