readme: ---
  tags:
  - spacy
  - token-classification
  widget:
  - text: "Section 319 Cr.P.C. contemplates a situation where the evidence adduced by the prosecution for Respondent No.3-G. Sambiah on 20th June 1984"
  - text: "In The High Court Of Kerala At Ernakulam\n\nCrl Mc No. 1622 of 2006()\n\n\n1. T.R.Ajayan, S/O. O.Raman,\n                      ...  Petitioner\n\n                        Vs\n\n\n\n1. M.Ravindran,\n                       ...       Respondent\n\n2. Mrs. Nirmala Dinesh, W/O. Dinesh,\n\n                For Petitioner  :Sri.A.Kumar\n\n                For Respondent  :Smt.M.K.Pushpalatha\n\nThe Hon'ble Mr. Justice P.R.Raman\nThe Hon'ble Mr. Justice V.K.Mohanan\n\n Dated :07/01/2008\n\n O R D E R\n"
  language:
  - en
  license: mit
  model-index:
  - name: en_legal_ner_trf
    results:
    - task:
        type: token-classification             
        name: Named Entity Recognition              
      dataset:
        type: Named Entity Recognition 
        name: InLegalNER
        split: Test       
      metrics:
        - type: F1-Score         
          value: 91.076
          name: Test F1-Score              
  
  ---
  # To Update
  
  [AUTHORS] "[PAPER NAME]". [PAPER DETAILS] [PAPER LINK]
  
  ---
  Indian Legal Named Entity Recognition(NER): Identifying relevant named entities in an Indian legal judgement using legal NER trained on [spacy](https://github.com/explosion/spaCy).
  
  ### Scores
  
  | Type | Score |
  | --- | --- |
  | **F1-Score** | **91.076** |
  | `Precision` | 91.979 |
  | `Recall` | 90.19 |
  
  
  | Feature | Description |
  | --- | --- |
  | **Name** | `en_legal_ner_trf` |
  | **Version** | `3.2.0` |
  | **spaCy** | `>=3.2.2,<3.3.0` |
  | **Default Pipeline** | `transformer`, `ner` |
  | **Components** | `transformer`, `ner` |
  | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
  | **Sources** | [InLegalNER Train Data](https://storage.googleapis.com/indianlegalbert/OPEN_SOURCED_FILES/NER/NER_TRAIN.zip) [GitHub](https://github.com/Legal-NLP-EkStep/legal_NER)|
  | **License** | `MIT` |
  | **Author** | [Aman Tiwari](https://www.linkedin.com/in/amant555/) |
  
  ## Load Pretrained Model
  
  Install the model using pip
  
  ```sh
  pip install https://huggingface.co/opennyaiorg/en_legal_ner_trf/resolve/main/en_legal_ner_trf-any-py3-none-any.whl
  ```
  
  Using pretrained NER model
  
  ```python
  # Using spacy.load().
  import spacy
  nlp = spacy.load("en_legal_ner_trf")
  text = "Section 319 Cr.P.C. contemplates a situation where the evidence adduced by the prosecution for Respondent No.3-G. Sambiah on 20th June 1984"
  doc = nlp(text)
  
  # Print indentified entites
  for ent in doc.ents:
       print(ent,ent.label_)
  
  ##OUTPUT     
  #Section 319 PROVISION
  #Cr.P.C. STATUTE
  #G. Sambiah RESPONDENT
  #20th June 1984 DATE
  ```
  
  
  ### Label Scheme
  
  <details>
  
  <summary>View label scheme (14 labels for 1 components)</summary>
  
  | ENTITY | BELONGS TO |
  | --- | --- |
  | `LAWYER` | PREAMBLE |
  | `COURT` | PREAMBLE, JUDGEMENT |
  | `JUDGE` | PREAMBLE, JUDGEMENT |
  | `PETITIONER` | PREAMBLE, JUDGEMENT |
  | `RESPONDENT` | PREAMBLE, JUDGEMENT |
  | `CASE_NUMBER` | JUDGEMENT | 
  | `GPE` | JUDGEMENT |
  | `DATE` | JUDGEMENT |
  | `ORG` | JUDGEMENT |
  | `STATUTE` | JUDGEMENT |
  | `WITNESS` | JUDGEMENT |
  | `PRECEDENT` | JUDGEMENT |
  | `PROVISION` | JUDGEMENT |
  | `OTHER_PERSON` | JUDGEMENT |
  
  </details>
  
  ## Author - Publication
  
  ```
  [CITATION DETAILS]
  ```