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text-summarization

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The LARGE LANGUAGE MODEL FOR HYDROGEN STORAGE project uses advanced natural language processing to improve research efficiency. It offers concise summaries and answers questions about hydrogen storage research papers, helping users quickly understand key insights and latest advancements.

  • Updated May 21, 2024
  • Jupyter Notebook

The project specializes in Text Summarization using NLP's extractive approach, preserving key information for concise summaries. Customize length, handle various formats, and enjoy a user-friendly interface. Applications include content aggregation, document skimming, and data analysis. Hop in to simplify information consumption! 🚀

  • Updated May 21, 2024
  • Python
ELBMcoclust

We unified some latent block models by proposing a flexible ELBM that is extended to SELBM to address the sparse problem by revealing a diagonal structure from sparse datasets. This leads to obtain more homogeneous co-clusters and therefore produce useful, ready-to-use and easy-to-interpret results.

  • Updated May 19, 2024
  • Python

A Python tool for splitting large Markdown files into smaller sections based on a specified token limit. This is particularly useful for processing large Markdown files with GPT models, as it allows the models to handle the data in manageable chunks.

  • Updated May 16, 2024
  • Python

DarkGPT Chat Explorer is an interactive web application that allows users to engage in conversations with various GPT (Generative Pre-trained Transformer) models in real-time. This repository contains the source code for the application.

  • Updated May 14, 2024
  • PowerShell

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