🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
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Updated
Jun 13, 2024 - Python
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
💫 Industrial-strength Natural Language Processing (NLP) in Python
A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
中文自然语言处理工具包 Toolkit for Chinese natural language processing
fastNLP: A Modularized and Extensible NLP Framework. Currently still in incubation.
An Open-Source Framework for Prompt-Learning.
Awesome-pytorch-list 翻译工作进行中......
Thai Natural Language Processing in Python.
Underthesea - Vietnamese NLP Toolkit
🏡 Fast & easy transfer learning for NLP. Harvesting language models for the industry. Focus on Question Answering.
Tika-Python is a Python binding to the Apache Tika™ REST services allowing Tika to be called natively in the Python community.
💁 Awesome Treasure of Transformers Models for Natural Language processing contains papers, videos, blogs, official repo along with colab Notebooks. 🛫☑️
A python package to run contextualized topic modeling. CTMs combine contextualized embeddings (e.g., BERT) with topic models to get coherent topics. Published at EACL and ACL 2021 (Bianchi et al.).
chatbot_ner: Named Entity Recognition for chatbots.
Easy-to-use and high-performance NLP and LLM framework based on MindSpore, compatible with models and datasets of 🤗Huggingface.
Kuromoji is a self-contained and very easy to use Japanese morphological analyzer designed for search
OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)
Ekphrasis is a text processing tool, geared towards text from social networks, such as Twitter or Facebook. Ekphrasis performs tokenization, word normalization, word segmentation (for splitting hashtags) and spell correction, using word statistics from 2 big corpora (english Wikipedia, twitter - 330mil english tweets).
Library for clinical NLP with spaCy.
Extraction of the journalistic five W and one H questions (5W1H) from news articles: who did what, when, where, why, and how?
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