Instructions to use Wikram/Legal-key-to-text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Wikram/Legal-key-to-text with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Wikram/Legal-key-to-text") model = AutoModelForSeq2SeqLM.from_pretrained("Wikram/Legal-key-to-text") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
Task:
Given a set of input keywords, generate a corresponding text output for a section in the legal domain.
Dataset:
We used the Contract Understanding Atticus Dataset (CUAD). It is a corpus of 13,000+ labels in 510 commercial legal contracts. They have been manually labeled under the supervision of experienced lawyers to identify 41 types of legal clauses (e.g. licenses, warranty, governing law, insurance, etc…).
Workflow:
You can connect me at vikram984511@gmail.com
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