A guest post by Hugging Face : Pierric Cistac , Software Engineer; Victor Sanh , Scientist; Anthony Moi , Technical Lead. Hugging Face 🤗 is an AI startup with the goal of contributing to Natural Language Processing (NLP) by developing tools to improve collaboration in the community, and by being an active part of research efforts. Because NLP is a difficult field, we believe that solving it is only possible if all actors share their research and results. That’s why we created 🤗 Transformers , a leading NLP library with more than 2M downloads and used by researchers and engineers across many companies. It allows the amazing international NLP community to quickly experiment, iterate, create and publish new models for a variety of tasks (text/token generation, text classification, question answering…) in a variety of languages (English of course, but also French, Italian, Spanish, German, Turkish, Swedish, Dutch, Arabic and many others!) More than 300 different models are available today through Transformers. While Transformers is very handy for research, we are also working hard on the production aspects of NLP, looking at and implementing solutions that can ease its adoption everywhere. In this blog post, we’re going to showcase one of the paths we believe can help fulfill this goal: the use of “small”, yet performant models (such as DistilBERT ), and frameworks targeting ecosystems different from Python such as Node via TensorFlow.js . 链接地址： https://blog.tensorflow.org/2020/05/how-hugging-face-achieved-2x-performance-boost-question-answering.html