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| title: Hackathon Generative AI | |
| emoji: 🌍 | |
| colorFrom: green | |
| colorTo: red | |
| sdk: streamlit | |
| sdk_version: 1.38.0 | |
| app_file: app.py | |
| pinned: false | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |
| # Legal Document Analysis on Hugging Face Spaces | |
| This app allows lawyers to quickly analyze legal documents using AI models from Hugging Face. Upload a document, and the app will generate a summary or other relevant analysis. | |
| ## How to Use | |
| - Upload a document (in .txt format). | |
| - View the summary or analysis generated by the AI model. | |
| Technologies: | |
| streamlit | |
| transformers | |
| # to classify text as law-related or not using zero-shot classification | |
| model="facebook/bart-large-mnli" | |
| # "summarization" | |
| model="facebook/bart-large-cnn" | |
| #Named Entity Recognition (NER) | |
| model="dslim/bert-base-NER" | |
| Named Entity Recognition (NER) is a Natural Language Processing (NLP) technique used to identify and classify key information (entities) | |
| in text. In the context of your legal document analysis project, NER plays an important role in extracting relevant entities such as names | |
| of people, organizations, locations, dates, and more, which are crucial in legal texts. | |