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--- |
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language: en |
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pipeline_tag: fill-mask |
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tags: |
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- legal |
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license: mit |
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--- |
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### InLegalBERT |
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Model and tokenizer files for the InLegalBERT model. |
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### Training Data |
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For building the pre-training corpus of Indian legal text, we collected a large corpus of case documents from the Indian Supreme Court and many High Courts of India. |
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These documents were collected from diverse publicly available sources on the Web, such as official websites of these courts (e.g., [the website of the Indian Supreme Court](https://main.sci.gov.in/)), the erstwhile website of the Legal Information Institute of India, |
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the popular legal repository [IndianKanoon](https://www.indiankanoon.org), and so on. |
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The court cases in our dataset range from 1950 to 2019, and belong to all legal domains, such as Civil, Criminal, Constitutional, and so on. |
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Additionally, we collected 1,113 Central Government Acts, which are the documents codifying the laws of the country. Each Act is a collection of related laws, called Sections. These 1,113 Acts contain a total of 32,021 Sections. |
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In total, our dataset contains around 5.4 million Indian legal documents (all in the English language). |
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The raw text corpus size is around 27 GB. |
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### Training Objective |
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This model is initialized with the [LEGAL-BERT-SC model](https://huggingface.co/nlpaueb/legal-bert-base-uncased) from the paper [LEGAL-BERT: The Muppets straight out of Law School](https://aclanthology.org/2020.findings-emnlp.261/). In our work, we refer to this model as LegalBERT, and our re-trained model as InLegalBERT. |
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### Usage |
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Using the tokenizer (same as [LegalBERT](https://huggingface.co/nlpaueb/legal-bert-base-uncased)) |
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```python |
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from transformers import AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("law-ai/InLegalBERT") |
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``` |
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Using the model to get embeddings/representations for a sentence |
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```python |
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from transformers import AutoModel |
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model = AutoModel.from_pretrained("law-ai/InLegalBERT") |
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``` |
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Using the model for further pre-training with MLM and NSP |
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```python |
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from transformers import BertForPreTraining |
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model_with_pretraining_heads = BertForPreTraining.from_pretrained("law-ai/InLegalBERT") |
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``` |
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### Citation |
