Deploy PhoBERT for Abstractive Text Summarization as REST API using StreamLit, Transformers by Hugging Face and PyTorch

A super fast walkthrough of NLP Text Summarization with Hugging Face Transformers.

Document Summarization App using large language model (LLM) and Langchain framework. Used a pre-trained T5 model and its tokenizer from Hugging Face Transformers library. Created a summarization pipeline to generate summary using model.

Abstractive Text Summarization using Transformers fine-tuned on the CNN/DailyMail dataset with PyTorch & Hugging Face.

Develop a complete text summarization system from scratch, focusing on summarizing complex dialogues using the SAMSum dataset. This project emphasizes professional NLP pipelines, fine-tuning state-of-the-art models like Google Pegasus, and implementing modular Python code for maintainability and scalability.

News summarization app using Hugging Face Transformers, spaCy, and Streamlit

A YouTube video summarizer using streamlit, openai-whisper and hugging face transformers

Transformer built from scratch w/ Tensorflow w/o Hugging Face for Text Summarization (trained with news text)

Text classification with the transformers library from Hugging Face, by fine-tuning DistilBERT or using summarization + Zero-Shot classification.

Text Summarization web application using Hugging Face transformers and Streamlit

A simple web application that uses Hugging Face Transformers for text summarization.

Use MarkItDown and Hugging Face Transformers in Spring Boot to summarize various files

An advanced and interactive text summarization application built using Hugging Face’s `BART` transformer. This project showcases the power of abstractive summarization using state-of-the-art NLP models and offers both a Gradio web interface and command-line usage.

TranScan is a Streamlit app for summarizing and searching YouTube video transcripts. It uses YouTube_Transcript_API and Whisper for audio to fetch transcripts, Hugging Face's Transformers for summarization, and PyDub with FFmpeg for audio processing. Easily access key information with keyword search and timestamped results.

## NLP Tools Flask Application This Flask application serves as a web interface for various Natural Language Processing (NLP) tasks using Hugging Face's Transformers library. The application allows users to interact with different NLP models for text generation, translation, summarization, and more. ### Key Features: - **GPT-3 Text Generation:**

A lightweight text summarization tool using TinyLlama (1.1B Chat) with Hugging Face Transformers and PyTorch.

End-to-end multi-task NLP pipeline using Hugging Face Transformers for sentiment analysis, summarization, named entity recognition, and zero-shot topic classification.

Fine-tune GPT-2 for scientific paper summarization using a minimal dataset, optimized for quick execution on CPU-only machines. Demonstrates the fine-tuning workflow with Hugging Face Transformers.

A web application made with streamlit and hugging face transformers pipeline which can be used to summarize you tube video by providing url of a video.

This project demonstrates a complete workflow for summarizing news articles using the Hugging Face transformers library, storing them in a SQLite database using Flask and SQLAlchemy, and displaying the summarized articles within a Jupyter Notebook.

This repository demonstrates how to use Hugging Face Transformers for text summarization. We focus on two state-of-the-art models: BART (facebook/bart-large-cnn) T5 (t5-large) Both models are designed for sequence-to-sequence tasks, making them ideal for text summarization.

An intelligent text summarization tool that supports both extractive and abstractive approaches. It combines traditional NLP (TF-IDF, Logistic Regression, spaCy) with modern deep learning (Hugging Face Transformers) to generate concise and meaningful summaries from long documents.

This project provides a blog post summarizer using Hugging Face's Transformers library. It allows you to generate concise summaries of long blog posts or articles using state-of-the-art natural language processing models.

AI-powered, fully local GenAI app that helps you read, summarize, question, and quiz yourself on research papers or text documents — completely offline, no paid API key required. Built with Python, Hugging Face Transformers, Faiss & Gradio to keep your data private and your workflow smarter.

A full-stack GenAI assistant that retrieves & summarizes consumer complaints using semantic search and LLMs. Built with FastAPI, FAISS, and Hugging Face Transformers, and monitored in real time using Prometheus and Grafana. Includes a modular Streamlit dashboard with hierarchical filtering and UI design. observability, backend, and NLP engineering.

This repository explores the use of advanced sequence-to-sequence networks and transformer models, such as BERT, BART, PEGASUS, and T5, for summarizing multi-text documents in the medical domain. It leverages extensive datasets like CORD-19 and a Biomedical Abstracts dataset from Hugging Face to fine-tune these models.

In this project, we learnt how to use hugging face transformers, speech recognition, pipeline, etc to make a project that takes a YouTube video URL and takes its captions using YouTube transcripts or generates its captions using speech recogniser and summarizes the entire captions, converts it into an audio file making it easy for the user to go through a long video or long lectures by just listening to the summarized audio.

AI-powered text analysis tool by TheDarkSyntax. Performs sentiment detection, keyword extraction, language identification, and text summarization using open-source NLP models. Built with Python and Hugging Face Transformers.

Text Summarizer using Hugging Face Transformers

This project leverages Hugging Face's transformer models (like T5) for automatic text summarization. The model condenses long pieces of text into concise summaries while retaining key information. The implementation demonstrates how to use pre-trained models for text summarization tasks, making it easy to apply to various types of textual data.