Artificial Intelligence Laboratory (6th semester) course's project.
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Updated
May 30, 2024 - Jupyter Notebook
Artificial Intelligence Laboratory (6th semester) course's project.
Transformer Architectures Comparison in Natural Language Generation Tasks
Summary Evaluation Tool
Your fully proficient, AI-powered and local chatbot assistant🤖
Transformer Balance Research
Text Summarization Modeling with three different Attention Types
a collection of NLP projects&tools. 自然语言处理方向项目和工具集合。
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.
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.
🍶 llm-distillery ⇢ use LLMs to run map-reduce summarization tasks on large documents until a target token size is met.
Python scripts to use for captioning images with VLMs
Summarizes emails with artificial intelligence. Based on the main topic of a message, the most important information is extracted.
Code to address Natural Language Generation Tasks via Transformer Architecture
1 line for thousands of State of The Art NLP models in hundreds of languages The fastest and most accurate way to solve text problems.
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! 🚀
A small NLP SAAS project that summarize a webpage
LLM projects
Developed a Named Entity Recognition (NER) model with an integrated text summarizer to efficiently extract and summarize key information from unstructured text data.
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.
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