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document-retrieval

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hierarchical-language-modeling

We address the task of learning contextualized word, sentence and document representations with a hierarchical language model by stacking Transformer-based encoders on a sentence level and subsequently on a document level and performing masked token prediction.

  • Updated Jul 25, 2023
  • Jupyter Notebook

This project is a Document Retrieval application that utilizes Retrieval-Augmented Generation (RAG) techniques to enable users to interact with uploaded PDF documents. By leveraging a Large Language Model (LLM), users can ask questions about the content of the documents and receive accurate answers based on the information retrieved.

  • Updated Oct 20, 2024
  • Jupyter Notebook

Built prediction and retrieval models for document retrieval, image retrieval, house price prediction, song recommendation, and analyzed sentiments using machine learning algorithms in Python

  • Updated Jan 20, 2018
  • Jupyter Notebook

The Intelligent "ASKDOC" project combines the power of Langchain, Azure, OpenAI models, and Python to deliver an intelligent question-answering system, that scans your PDF documents and answer queries based on its contents. It can be queried using Human Natural Language.

  • Updated Feb 4, 2024
  • Python

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