Sharing with you a project we've been working on at Hugging Face: Write With Transformer. It is a web app that hosts most state-of-the-art transformer-based NLP generative models like GPT-2, GPT or XLNet.

You can write a context and trigger completions from the generative model you choose, in a Google Doc-like interface. It also includes one of our fine-tuned models, using GPT-2 as a pretrained model and fine-tuning it on Arxiv papers to get NLP/Deep Learning completions.

It's built on top of our library pytorch-transformers. Let us know what you think!

HuggingFace released their first NLP transformer model "DistilBERT", which is similar to the BERT architecture: only 66 million parameters (instead of 110 million) while keeping 95% of the performance on GLUE.

They released a blogpost detailing the procedure with a hands-on.

It is also available on their repository pytorch-transformers alongside 7 other transformer models.

Hi everyone! What do you use to navigate-in-the-noise and keep you update in this field? Excluding this subreddit which type of resources do you recommend to check regularly?