--- 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/) [Arxiv](https://arxiv.org/abs/2211.03442) --- 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
View label scheme (14 labels for 1 components) | 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 |
## 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.", } ```