|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Transformers||87,738||64||911||7 hours ago||91||June 21, 2022||617||apache-2.0||Python|
|🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.|
|D2l Zh||40,601||1||3 days ago||45||March 25, 2022||21||apache-2.0||Python|
|Made With Ml||32,763||7 days ago||5||May 15, 2019||8||mit||Jupyter Notebook|
|Learn how to responsibly develop, deploy and maintain production machine learning applications.|
|Spacy||25,599||1,533||842||16 hours ago||196||April 05, 2022||111||mit||Python|
|💫 Industrial-strength Natural Language Processing (NLP) in Python|
|Applied Ml||23,904||3 days ago||5||mit|
|📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.|
|Nlp Progress||21,398||18 days ago||45||mit||Python|
|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.|
|D2l En||16,954||8 days ago||83||other||Python|
|Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 400 universities from 60 countries including Stanford, MIT, Harvard, and Cambridge.|
|Rasa||15,844||32||28||2 days ago||274||July 06, 2022||111||apache-2.0||Python|
|💬 Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants|
|Datasets||15,594||9||208||2 days ago||52||June 15, 2022||526||apache-2.0||Python|
|🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools|
|Lectures||14,554||6 years ago||10|
|Oxford Deep NLP 2017 course|
ELI5 is a Python package which helps to debug machine learning classifiers and explain their predictions.
It provides support for the following machine learning frameworks and packages:
ELI5 also implements several algorithms for inspecting black-box models (see Inspecting Black-Box Estimators):
Explanation and formatting are separated; you can get text-based explanation
to display in console, HTML version embeddable in an IPython notebook
or web dashboards, a
pandas.DataFrame object if you want to process
results further, or JSON version which allows to implement custom rendering
and formatting on a client.
License is MIT.
Check docs for more.