A few years ago, creating a chatbot -as limited as they were back then- could take months 🗓, from designing the rules to actually writing thousands of answers to cover some of the conversation topics. With the recent progress in deep-learning for NLP, we can now get rid of this…

I’ve spent most of 2018 training neural networks that tackle the limits of my GPUs. Whether it was a 150 millions parameters language model like OpenAI’s huge Generative Pre-trained Transformer (or the recent and similar BERT model) or a meta-learning neural net fed with 30 million element inputs like the…

In which Twitter talked about meaning, semantics, language models, learning Thai and Java, entailment, co-reference — all in one fascinating thread. — Last week a tweet by Jacob Andreas triggered a huge discussion on Twitter that many people have called the meaning/semantics mega-thread. Twitter is a great medium for having such a discussion, replying to any comment allows to revive the debate from the most promising point when it’s stuck in a…

How to take advantage of spaCy & a bit of Cython for blazing fast NLP — I also published a Jupyter notebook with the examples I describe in this post. When we published our Python coreference resolution package✨ last year, we got an amazing feedback from the community and people started to use it for many applications 📚, some very different from our original dialog use-case…

A Chinese version of this article can be found here, thanks to Jakukyo. Word and sentence embeddings have become an essential part of any Deep-Learning-based natural language processing systems. They encode words and sentences 📜 in fixed-length dense vectors 📐 to drastically improve the processing of textual data. A huge…

Meta-learning is an exciting trend of research in the machine-learning community which tackles the problem of learning to learn. The traditional paradigm in machine learning research is to get a huge dataset on a specific task, and train a model from scratch using this dataset. Obviously that’s very far from…

Links: Online demo Github repo: https://github.com/huggingface/neuralcoref and our previous Medium post. The last months have been quite intense at HuggingFace 🤗 with crazy usage growth 🚀 and everybody hard at work to keep up with it 🏇, but we finally managed to free some time and update our open-source library…

Introducing torchMoji, a PyTorch implementation of DeepMoji — Detecting emotions, sentiments & sarcasm is a critical element of our natural language understanding pipeline at HuggingFace 🤗. Recently, we have switched to an integrated system based on a NLP model from the MIT Media Lab. Update: We’ve open sourced it! Repo on GitHub The model was initially designed in…

TL;DR, Links: Online demo at https://huggingface.co/coref, Github repo for Neuralcoref: https://github.com/huggingface/neuralcoref At Hugging Face 🤗 we work on the most amazing and challenging subset of natural language: millennial language. Full of uncertainties 🤔, implicit references 👾, emojis 😭, jokes 😂 and constantly creating novel expressions… To navigate these stormy waters…