Vizseq
An Analysis Toolkit for Natural Language Generation (Translation, Captioning, Summarization, etc.)
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Vizseq | 437 | 3 months ago | 16 | August 07, 2020 | 6 | mit | Python | |||
| An Analysis Toolkit for Natural Language Generation (Translation, Captioning, Summarization, etc.) | ||||||||||
| Simplet5 | 305 | a year ago | 7 | February 15, 2022 | 31 | mit | Python | |||
| simpleT5 is built on top of PyTorch-lightning⚡️ and Transformers🤗 that lets you quickly train your T5 models. | ||||||||||
| How To Use Transformers | 271 | 4 months ago | 10 | apache-2.0 | Python | |||||
| Transformers 库快速入门教程 | ||||||||||
| Cybertron | 235 | 2 | 3 months ago | 5 | November 08, 2023 | 21 | bsd-2-clause | Go | ||
| Cybertron: the home planet of the Transformers in Go | ||||||||||
| Emnlp19 Moverscore | 164 |  | 1 | a year ago | 7 | April 09, 2020 | 11 | mit | Python | |
| MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance | ||||||||||
| Onnxt5 | 136 | 3 years ago | 11 | January 28, 2021 | 3 | apache-2.0 | Python | |||
| Summarization, translation, sentiment-analysis, text-generation and more at blazing speed using a T5 version implemented in ONNX. | ||||||||||
| How2 Dataset | 125 | a year ago | Python | |||||||
| This repository contains code and metadata of How2 dataset | ||||||||||
| Hfapigo | 44 | 3 months ago | 2 | mit | Go | |||||
| Unofficial (Golang) Go bindings for the Hugging Face Inference API | ||||||||||
| Hf Experiments | 37 | 2 years ago | mit | Python | ||||||
| Experiments with Hugging Face 🔬 🤗 | ||||||||||
| Nlp_workshop_odsc_europe20 | 36 | 4 years ago | gpl-3.0 | Jupyter Notebook | ||||||
| Extensive tutorials for the Advanced NLP Workshop in Open Data Science Conference Europe 2020. We will leverage machine learning, deep learning and deep transfer learning to learn and solve popular tasks using NLP including NER, Classification, Recommendation \ Information Retrieval, Summarization, Classification, Language Translation, Q&A and Topic Models. | ||||||||||
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Python
Translation
Generation
Summarization
Machine Translation