Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
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Sentencepiece | 8,851 | 120 | 787 | 5 months ago | 34 | May 02, 2023 | 32 | apache-2.0 | C++ | |
Unsupervised text tokenizer for Neural Network-based text generation. | ||||||||||
Catalyst | 3,151 | 19 | 19 | a year ago | 108 | April 29, 2022 | 6 | apache-2.0 | Python | |
Accelerated deep learning R&D | ||||||||||
Awesome Deeplearning | 2,670 | 5 months ago | 462 | apache-2.0 | Jupyter Notebook | |||||
深度学习入门课、资深课、特色课、学术案例、产业实践案例、深度学习知识百科及面试题库The course, case and knowledge of Deep Learning and AI | ||||||||||
Gse | 2,352 | 14 | 21 | 7 months ago | 82 | January 16, 2023 | 12 | apache-2.0 | Go | |
Go efficient multilingual NLP and text segmentation; support English, Chinese, Japanese and others. | ||||||||||
Deepnlp | 1,311 | 6 years ago | 8 | January 01, 2018 | 44 | mit | Python | |||
Deep Learning NLP Pipeline implemented on Tensorflow | ||||||||||
Jieba Php | 1,193 | 29 | 8 | 2 years ago | 17 | August 15, 2022 | 21 | mit | PHP | |
"結巴"中文分詞:做最好的 PHP 中文分詞、中文斷詞組件。 / "Jieba" (Chinese for "to stutter") Chinese text segmentation: built to be the best PHP Chinese word segmentation module. | ||||||||||
Natasha | 1,085 | 3 | 9 | 9 months ago | 19 | July 24, 2023 | 24 | mit | Python | |
Solves basic Russian NLP tasks, API for lower level Natasha projects | ||||||||||
Xmnlp | 940 | 1 | 2 years ago | 14 | November 12, 2022 | 3 | apache-2.0 | Python | ||
xmnlp:提供中文分词, 词性标注, 命名体识别,情感分析,文本纠错,文本转拼音,文本摘要,偏旁部首,句子表征及文本相似度计算等功能 | ||||||||||
Jieba Rs | 585 | 5 | 15 | a year ago | 40 | July 16, 2023 | 9 | mit | Rust | |
The Jieba Chinese Word Segmentation Implemented in Rust | ||||||||||
Ekphrasis | 583 | 7 | 2 years ago | 54 | May 17, 2022 | 18 | mit | Python | ||
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). |