A summarization website that can generate summaries from either YouTube videos or PDF files.
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Updated
May 29, 2024 - Python
A summarization website that can generate summaries from either YouTube videos or PDF files.
Factuality check of the SemRep Predications
Focus - Understanding contextual retrievability.
This GitHub repository implements a novel approach for detecting Initial Public Offering (IPO) underpricing using pre-trained Transformers. The models, extended to handle large S-1 filings, leverage both textual information and financial indicators, outperforming traditional machine learning methods.
A hyperpartisan news article classification system using BERT-based techniques. The goal was to leverage state-of-the-art transformer models like BERT, ROBERTa, and Longformer to accurately classify news articles as hyperpartisan or non-hyperpartisan.
Fine-tuned Longformer for Summarization of Machine Learning Articles
An attempt of creating a model and pipeline for retrieving italian legal documents given a prompt from the user.
Abstractive and Extractive Text summarization using Transformers.
Training and inference code for the claim veracity checker built on Longformer-4096 tuned to PUBHEALTH
Project as part of COMP34812: Natural Language Understanding
This project applies the Longformer model to sentiment analysis using the IMDB movie review dataset. The Longformer model, introduced in "Longformer: The Long-Document Transformer," tackles long document processing with sliding-window and global attention mechanisms. The implementation leverages PyTorch, following the paper's architecture
Industrial Text Scoring using Multimodal Deep Natural Language Processing 🚀 | Code for IEA AIE 2022 paper
Convert pretrained RoBerta models to various long-document transformer models
[제 13회 투빅스 컨퍼런스] YoYAK - Yes or Yes, Attention with gap-sentence for Korean long sequence
Kaggle NLP competition - Top 2% solution (36/2060)
using transformers to do text classification.
A WebApp to summarize research papers using HuggingFace Transformers.
list of efficient attention modules
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