Gateway into the John Snow Labs Ecosystem
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Updated
May 31, 2024 - Python
Gateway into the John Snow Labs Ecosystem
Gramify - A Grammatical Error Corrector, that not only corrects the error but also shows what type of error has been made along with visualization through graph, which shows type of error made on the basis of count.
Adversarial attack of scientific claim verification
Open Source Modding Tools for old Call Of Duty games
Repository for the paper "ViHateT5: Enhancing Hate Speech Detection in Vietnamese with A Unified Text-to-Text Transformer Model" (ACL'2024 - Findings)
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.
Tencent Pre-training framework in PyTorch & Pre-trained Model Zoo
Compilation of Localized Strings from Call of Duty games (English & Spanish).
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.
The Bot Warfare mod for Black Ops 1
pycorrector is a toolkit for text error correction. 文本纠错,实现了Kenlm,T5,MacBERT,ChatGLM3,LLaMA等模型应用在纠错场景,开箱即用。
AraT5: Text-to-Text Transformers for Arabic Language Understanding
Transformers 3rd Edition
Multi-label classification using LLMs, with additional enhancements using quantization and LoRA (Low-Rank Adaptation). Get better performance on GPU.
Open Source Pre-training Model Framework in PyTorch & Pre-trained Model Zoo
Modern applied deep learning with Transformer model methodology.
Q&Arabic is an NLP framework that generates Arabic FAQs from a given material. The project uses deep learning models (BERT and T5) and includes a detailed report and brief presentation covering the system analysis, related work, and future plans.
Unlock the depths of Wikipedia with WikiBot. It delivers concise summaries and suggests related articles to fuel your curiosity and guide your exploration.
Awesome series for Large Language Model(LLM)s
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