Token Classification
spaCy
English
Eval Results
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@@ -22,6 +22,12 @@ model-index:
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  - type: F1-Score
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  value: 90.709
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  name: Test F1-Score
 
 
 
 
 
 
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  ---
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  Indian Legal Named Entity Recognition: Identifying relevant entities in an Indian legal document
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@@ -37,15 +43,57 @@ Indian Legal Named Entity Recognition: Identifying relevant entities in an India
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  | **License** | `MIT` |
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  | **Author** | [Aman Tiwari](https://www.linkedin.com/in/amant555/) |
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  ### Label Scheme
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  <details>
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  <summary>View label scheme (14 labels for 1 components)</summary>
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- | Component | Labels |
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  | --- | --- |
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- | **`ner`** | `CASE_NUMBER`, `COURT`, `DATE`, `GPE`, `JUDGE`, `LAWYER`, `ORG`, `OTHER_PERSON`, `PETITIONER`, `PRECEDENT`, `PROVISION`, `RESPONDENT`, `STATUTE`, `WITNESS` |
 
 
 
 
 
 
 
 
 
 
 
 
 
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  </details>
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@@ -56,3 +104,10 @@ Indian Legal Named Entity Recognition: Identifying relevant entities in an India
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  | **F1-Score** | **90.709** |
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  | `Precision` | 91.474 |
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  | `Recall` | 89.956 |
 
 
 
 
 
 
 
 
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  - type: F1-Score
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  value: 90.709
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  name: Test F1-Score
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+
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+ ---
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+ # To Update
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+
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+ [AUTHORS] "[PAPER NAME]". [PAPER DETAILS] [PAPER LINK]
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+
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  ---
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  Indian Legal Named Entity Recognition: Identifying relevant entities in an Indian legal document
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  | **License** | `MIT` |
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  | **Author** | [Aman Tiwari](https://www.linkedin.com/in/amant555/) |
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+ ## Load Pretrained Model
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+
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+ Install the model using pip
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+
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+ ```sh
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+ pip install https://huggingface.co/opennyaiorg/en_legal_ner_trf/resolve/main/en_legal_ner_trf-any-py3-none-any.whl
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+ ```
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+
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+ Using pretrained NER model
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+
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+ ```python
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+ # Using spacy.load().
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+ import spacy
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+ nlp = spacy.load("en_legal_ner_trf")
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+ 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"
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+ doc = nlp(text)
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+
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+ # Print indentified entites
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+ for ent in doc.ents:
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+ print(ent,ent.label_)
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+
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+ ##OUTPUT
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+ #Section 319 PROVISION
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+ #Cr.P.C. STATUTE
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+ #G. Sambiah RESPONDENT
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+ #20th June 1984 DATE
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+ ```
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+
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+
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  ### Label Scheme
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  <details>
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  <summary>View label scheme (14 labels for 1 components)</summary>
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+ | ENTITY | BELONGS TO |
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  | --- | --- |
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+ | `LAWYER` | PREAMBLE |
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+ | `COURT` | PREAMBLE, JUDGEMENT |
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+ | `JUDGE` | PREAMBLE, JUDGEMENT |
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+ | `PETITIONER` | PREAMBLE, JUDGEMENT |
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+ | `RESPONDENT` | PREAMBLE, JUDGEMENT |
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+ | `CASE_NUMBER` | JUDGEMENT |
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+ | `GPE` | JUDGEMENT |
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+ | `DATE` | JUDGEMENT |
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+ | `ORG` | JUDGEMENT |
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+ | `STATUTE` | JUDGEMENT |
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+ | `WITNESS` | JUDGEMENT |
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+ | `PRECEDENT` | JUDGEMENT |
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+ | `PROVISION` | JUDGEMENT |
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+ | `OTHER_PERSON` | JUDGEMENT |
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  </details>
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  | **F1-Score** | **90.709** |
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  | `Precision` | 91.474 |
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  | `Recall` | 89.956 |
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+
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+
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+ ## Author - Publication
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+
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+ ```
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+ [CITATION DETAILS]
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+ ```