🤗 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.
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《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。
💫 Industrial-strength Natural Language Processing (NLP) in Python
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
🤗 The largest hub of ready-to-use datasets for AI models with fast, easy-to-use and efficient data manipulation tools
🐫 CAMEL: The first and the best multi-agent framework. Finding the Scaling Law of Agents. https://www.camel-ai.org
An open-source, tool-augmented conversational language model from Fudan University
Low-code framework for building custom LLMs, neural networks, and other AI models
Fast and Accurate ML in 3 Lines of Code
Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages
Accelerated deep learning R&D
Toolkit for Machine Learning, Natural Language Processing, and Text Generation, in TensorFlow. This is part of the CASL project: http://casl-project.ai/
Plug and Play Language Model implementation. Allows to steer topic and attributes of GPT-2 models.
Pretrained model hub for Keras 3.
DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome
RNNLG is an open source benchmark toolkit for Natural Language Generation (NLG) in spoken dialogue system application domains. It is released by Tsung-Hsien (Shawn) Wen from Cambridge Dialogue Systems Group under Apache License 2.0.