NLP
A summary of must-read papers for Neural Question Generation (NQG)
Code to support the paper "Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasets"
A curated list of resources dedicated to Natural Language Generation (NLG)
😎 A curated list of the Question Answering (QA)
Pretrain, finetune ANY AI model of ANY size on multiple GPUs, TPUs with zero code changes.
Flexible components pairing 🤗 Transformers with ⚡ Pytorch Lightning
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
由淺入深的深度學習資源 Collection of deep learning materials for everyone
All Algorithms implemented in Python
《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被70多个国家的500多所大学用于教学。
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🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
ROADMAP(Mind Map) and KEYWORD for students those who have interest in learning NLP
Evaluation code for various unsupervised automated metrics for Natural Language Generation.
Code and data to support the paper "PAQ 65 Million Probably-Asked Questions andWhat You Can Do With Them"
Pyserini is a Python toolkit for reproducible information retrieval research with sparse and dense representations.
Natural Questions (NQ) contains real user questions issued to Google search, and answers found from Wikipedia by annotators. NQ is designed for the training and evaluation of automatic question ans…
A fast, low-resource Natural Language Processing and Text Correction library written in Rust.
Dense Passage Retriever - is a set of tools and models for open domain Q&A task.
🚪✊Knock Knock: Get notified when your training ends with only two additional lines of code
Binary Passage Retriever (BPR) - an efficient passage retriever for open-domain question answering
A collection of large question answering datasets
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
Visually Explore the Stanford Question Answering Dataset
AI orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data…