Efficient Transformers taxonomy from Efficient Transformers: a Survey by Tay et al. Co-written by Teven Le Scao, Patrick Von Platen, Suraj Patil, Yacine Jernite and Victor Sanh. Each month, we will choose a topic to focus on, reading a set of four papers recently published on the subject. We will then write a short blog post summarizing    

Wav2Vec2 is a pretrained model for Automatic Speech Recognition (ASR) and was released in September 2020 by Alexei Baevski, Michael Auli, and Alex Conneau. Using a novel contrastive pretraining objective, Wav2Vec2 learns powerful speech representations from more than 50.000 hours of unlabeled speech. Similar, to BERT’s masked language    

Look at these smiles! Today, we announce a strategic partnership between Hugging Face and Amazon to make it easier for companies to leverage State of the Art Machine Learning models, and ship cutting-edge NLP features faster. Through this partnership, Hugging Face is leveraging Amazon Web Services as its Preferred Cloud Provider to deliver services to its    

Transformer-based models have shown to be very useful for many NLP tasks. However, a major limitation of transformers-based models is its O(n2)O(n^2)O(n2) time & memory complexity (where nn

Run your raw PyTorch training scripts on any kind of device. Most high-level libraries above PyTorch provide support for distributed training and mixed precision, but the abstraction they introduce require a user to learn a new API if they want to customize the underlying training loop. 🤗 Accelerate was created for PyTorch users who like to have full control over their training    

Back in October 2019, my colleague Lysandre Debut published a comprehensive (at the time) inference performance benchmarking blog (1). Since then, 🤗 transformers (2) welcomed a tremendous number of new architectures and thousands of new models were added to the 🤗 hub (3) which now counts more than 9,000 of them as of first quarter of 2021.    

Cross-posted from the Gradio blog. The Hugging Face Model Hub has more than 10,000 machine learning models submitted by users. You’ll find all kinds of natural language processing models that, for example, translate between Finnish    

In many Machine Learning applications, the amount of available labeled data is a barrier to producing a high-performing model. The latest developments in NLP show that you can overcome this limitation by providing a few examples at inference time with a large language model – a technique known as Few-Shot Learning. In this blog post, we’ll explain what Few-Shot Learning is, and