Contract Language Evaluation and Risk Analysis
Introduction CLEAR is a powerful tool designed to analyze business contracts by providing comprehensive summaries, classifying content within contract clauses, and detecting deviations from predefined templates. This application leverages state-of-the-art Natural Language Processing (NLP) techniques to offer detailed insights into the contents of uploaded PDF contracts.
Features PDF Text Extraction: Extracts text from uploaded PDF files. Text Summarization: Generates concise summaries of the contract. Content Classification: Classifies contract clauses based on predefined categories. Deviation Detection: Identifies deviations from provided template clauses using semantic similarity. Named Entity Recognition (NER): Extracts key entities from the contract text. Interactive User Interface: Provides an intuitive interface for users to upload files, ask questions, and view results. Installation Follow these steps to set up the project on your local machine:
Clone the Repository
git clone https://github.com/your-username/chat-with-pdf-gemini.git cd chat-with-pdf-gemini Create a Virtual Environment
python -m venv venv
source venv/bin/activate # On Windows use venv\Scripts\activate
Install Dependencies
pip install -r requirements.txt Set Up Environment Variables Create a .env file in the root directory and add API keys:
streamlit run main.py
Usage Upload PDF Files: Use the sidebar to upload one or more PDF files containing business contracts. Ask Questions: Enter questions about the uploaded contracts in the main input field. View Results: The application will display the summary, classified clauses, deviations from templates, and extracted entities. Technical Details PDF Text Extraction The get_pdf_text function extracts text from PDF files using the PyPDF2 library.
Text Summarization The application uses the facebook/bart-large-cnn model from Hugging Face's transformers library to summarize the contract text.
Content Classification The typeform/distilbert-base-uncased-mnli model is used for zero- ot classification of contract clauses.
Deviation Detection Semantic similarity is calculated using the sentence-transformers library with the paraphrase-MiniLM-L6-v2 model.
Named Entity Recognition (NER) The transformers library's NER pipeline is used to extract entities from the contract text.
Vector Store The FAISS library is used to create and manage a vector store for efficient similarity searches.
OCR Integration: Integrate OCR to handle scanned PDFs. Custom Model Training: Train custom models for more accurate clause classification and deviation detection. Enhanced UI: Improve the user interface for better user experience. Conclusion CLEAR is a robust tool for business contract analysis, providing valuable insights through advanced NLP techniques. This documentation serves as a comprehensive guide to setting up, using, and understanding the technical implementation of the project.
Contract Language Evaluation and Risk Analysis
Introduction CLEAR is a powerful tool designed to analyze business contracts by providing comprehensive summaries, classifying content within contract clauses, and detecting deviations from predefined templates. This application leverages state-of-the-art Natural Language Processing (NLP) techniques to offer detailed insights into the contents of uploaded PDF contracts.
Features PDF Text Extraction: Extracts text from uploaded PDF files. Text Summarization: Generates concise summaries of the contract. Content Classification: Classifies contract clauses based on predefined categories. Deviation Detection: Identifies deviations from provided template clauses using semantic similarity. Named Entity Recognition (NER): Extracts key entities from the contract text. Interactive User Interface: Provides an intuitive interface for users to upload files, ask questions, and view results. Installation Follow these steps to set up the project on your local machine:
Clone the Repository
git clone https://github.com/your-username/chat-with-pdf-gemini.git cd chat-with-pdf-gemini Create a Virtual Environment
python -m venv venv
source venv/bin/activate # On Windows use venv\Scripts\activate
Install Dependencies
pip install -r requirements.txt Set Up Environment Variables Create a .env file in the root directory and add API keys:
streamlit run main.py
Usage Upload PDF Files: Use the sidebar to upload one or more PDF files containing business contracts. Ask Questions: Enter questions about the uploaded contracts in the main input field. View Results: The application will display the summary, classified clauses, deviations from templates, and extracted entities. Technical Details PDF Text Extraction The get_pdf_text function extracts text from PDF files using the PyPDF2 library.
Text Summarization The application uses the facebook/bart-large-cnn model from Hugging Face's transformers library to summarize the contract text.
Content Classification The typeform/distilbert-base-uncased-mnli model is used for zero- ot classification of contract clauses.
Deviation Detection Semantic similarity is calculated using the sentence-transformers library with the paraphrase-MiniLM-L6-v2 model.
Named Entity Recognition (NER) The transformers library's NER pipeline is used to extract entities from the contract text.
Vector Store The FAISS library is used to create and manage a vector store for efficient similarity searches.
OCR Integration: Integrate OCR to handle scanned PDFs. Custom Model Training: Train custom models for more accurate clause classification and deviation detection. Enhanced UI: Improve the user interface for better user experience. Conclusion CLEAR is a robust tool for business contract analysis, providing valuable insights through advanced NLP techniques. This documentation serves as a comprehensive guide to setting up, using, and understanding the technical implementation of the project.
