Implementation of the LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens Paper
-
Updated
Jun 12, 2024 - Python
Implementation of the LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens Paper
Scikit-learn friendly library to interpret, and prompt-engineer text datasets using large language models.
💥 Fast State-of-the-Art Tokenizers optimized for Research and Production
🤗 AutoTrain Advanced
Natural language processing of the Wikipedia text (as far English only) with goals to detect contradictory info, and to create a chat bot providing more accurate answers than ChatGPT.
Neuro-symbolic interpretation learning (mostly just language-learning, for now)
🚀 Catalyst is a C# Natural Language Processing library built for speed. Inspired by spaCy's design, it brings pre-trained models, out-of-the box support for training word and document embeddings, and flexible entity recognition models.
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.
Conversational AI Platform to build effective Proactive Digital Assistants using Visual LLM Chaining
An overview of the possibilities offered by artificial intelligence (AI) to serve as a technical basis for a digital product offering: from understanding, personalization, design of machine learning models and its deployment through an API built with FastAPI into the Cloud
Ragbot.AI is an augmented brain assistant developed by Rajiv Pant
NLP engine code
LLM(😽)
Repository for the ACL 2024 paper "LIEDER: Linguistically-Informed Evaluation Suite for Discourse Entity Recognition"
Learn Generative AI with PyTorch (Manning Publications, 2024)
🛸 Use pretrained transformers like BERT, XLNet and GPT-2 in spaCy
Sequence-to-Sequence Spanish Pre-trained Language Models
benchmark for Speech-to-Intent engines
This project uses BERT to build a QA system fine-tuned on the SQuAD dataset, improving the accuracy and efficiency of question-answering tasks. We address challenges in contextual understanding and ambiguity handling to enhance user experience and system performance.
Add a description, image, and links to the natural-language-understanding topic page so that developers can more easily learn about it.
To associate your repository with the natural-language-understanding topic, visit your repo's landing page and select "manage topics."