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---
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
Crl Mc No. 1622 of 2006()
1. T.R.Ajayan, S/O. O.Raman,
... Petitioner
Vs
1. M.Ravindran,
... Respondent
2. Mrs. Nirmala Dinesh, W/O. Dinesh,
For Petitioner :Sri.A.Kumar
For Respondent :Smt.M.K.Pushpalatha
The Hon'ble Mr. Justice P.R.Raman
The Hon'ble Mr. Justice V.K.Mohanan
Dated :07/01/2008
O R D E R
language:
- en
license: apache-2.0
model-index:
- name: en_legal_ner_trf
results:
- task:
type: token-classification
name: Named Entity Recognition
metrics:
- type: F1-Score
value: 91.076
name: Test F1-Score
datasets:
- opennyaiorg/InLegalNER
---
# Paper details
[Named Entity Recognition in Indian court judgments](https://aclanthology.org/2022.nllp-1.15/)
---
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
```
@inproceedings{kalamkar-etal-2022-named,
title = "Named Entity Recognition in {I}ndian court judgments",
author = "Kalamkar, Prathamesh and
Agarwal, Astha and
Tiwari, Aman and
Gupta, Smita and
Karn, Saurabh and
Raghavan, Vivek",
booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.nllp-1.15",
doi = "10.18653/v1/2022.nllp-1.15",
pages = "184--193",
abstract = "Identification of named entities from legal texts is an essential building block for developing other legal Artificial Intelligence applications. Named Entities in legal texts are slightly different and more fine-grained than commonly used named entities like Person, Organization, Location etc. In this paper, we introduce a new corpus of 46545 annotated legal named entities mapped to 14 legal entity types. The Baseline model for extracting legal named entities from judgment text is also developed.",
}
``` |