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Tech & science
Our science contributions
Weโ€™re on a journey to build the first truly social artificial intelligence. Along the way, we contribute to the development of technology for the better.
HMTL
Victor Sanh et al.
AAAI 2019

Hierarchical Multi-Task Learning

Our paper has been accepted to AAAI 2019. ๐Ÿ’ฅ We have open-sourced code and demo. ๐ŸŽฎ
PyTorch Transformers
10k+ stars on GitHub

Cutting-edge science

Our PyTorch implementation of Google AIโ€™s BERT model with several models ready to be fine-tuned on downstream tasks.
Transfer-Transfo
A Transfer Learning approach to Natural Language Generation. A workshop paper on the Transfer Learning approach we used to win the automatic metrics part of the Conversational Intelligence Challenge 2 at NeurIPS 2018.
Meta-learning for language modeling
Thomas Wolf et al.
ICLR 2018

Cutting-edge research

Our workshop paper on Meta-Learning a Dynamical Language Model was accepted to ICLR 2018 ๐Ÿ’ช๐Ÿ’ช. We use our implementation to power ๐Ÿค—.
Neuralcoref
State-of-the-art coreference resolution

Openness

Our coreference resolution module is now the top open source library for coreference. You can train it on your own dataset and language.
TorchMoji
State-of-the-art emotion detection
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Major blog posts
We spend a lot of time training models that can barely fit 1-4 samples/GPU. But SGD usually needs more than few samples/batch for decent results. Here is a post gathering practical tips we use, from simple tricks to multi-GPU code & distributed setups.
How you can make your Python NLP module 50-100 times faster by use spaCy's internals and a bit of Cython magic! Comes with a Jupyter notebook with examples processing over 80 millions words per sec!
A post summarizing recent developments in Universal Word/Sentence Embeddings that happened over 2017/early-2018 and future trends. With ELMo, InferSent, Google's Universal Sentence embeddings, learning by multi-tasking...
To introduce the work we presented at ICLR 2018, we drafted a visual & intuitive introduction to Meta-Learning. In this post, we start by explaining whatโ€™s meta-learning in a very visual and intuitive way. Then, we code a meta-learning model in PyTorch and share some of the lessons learned on this project.
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