Stars
A game theoretic approach to explain the output of any machine learning model.
Differentiable SDE solvers with GPU support and efficient sensitivity analysis.
Typer, build great CLIs. Easy to code. Based on Python type hints.
⬛️ Lightweight and modern terminal animations using async/await
Fast, flexible and easy to use probabilistic modelling in Python.
CAPS50 / Hash-Embeddings
Forked from YannDubs/Hash-EmbeddingsPyTorch implementation of Hash Embeddings (NIPS 2017). Submission to the NIPS Implementation Challenge.
PyTorch implementation of Hash Embeddings (NIPS 2017). Submission to the NIPS Implementation Challenge.
Named-Entity-Recognition-with-Bidirectional-LSTM-CNNs
Deep neural models for core NLP tasks (Pytorch version)
A collection of corpora for named entity recognition (NER) and entity recognition tasks. These annotated datasets cover a variety of languages, domains and entity types.
LSTM language model with CNN over characters
💥 Fast State-of-the-Art Tokenizers optimized for Research and Production
Swift Core ML 3 implementations of GPT-2, DistilGPT-2, BERT, and DistilBERT for Question answering. Other Transformers coming soon!
Unsupervised Question answering via Cloze Translation
Hyphenation library based on libhnj/TeX hyphenation. Used by LibreOffice, Scribus and OpenOffice.org
In-depth tutorials for implementing deep learning models on your own with PyTorch.
Code and data accompanying Natural Language Processing with PyTorch published by O'Reilly Media https://amzn.to/3JUgR2L
Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
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.
🍳 Recipes for the Prodigy, our fully scriptable annotation tool
A very simple framework for state-of-the-art Natural Language Processing (NLP)
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations