Wav2Vec2 is a popular pre-trained model for speech recognition. Released in September 2020 by Meta AI Research, the novel architecture catalyzed progress in self-supervised pretraining for speech recognition, e.g. G. Ng et al., 2021, Chen et al, 2021, Hsu et al., 2021 and

December 2022 Update: Infinity is no longer offered by Hugging Face as a commercial inference solution. To deploy and accelerate your models, we recommend the following new solutions: Introduction Transfer learning has changed Machine Learning by reaching new levels of accuracy from Natural Language Processing (NLP) to Audio and Computer Vision tasks. At Hugging Face, we work hard to make these new complex models and large checkpoints as easily accessible and usable as possible. But    

At Hugging Face, we are contributing to the ecosystem for Deep Reinforcement Learning researchers and enthusiasts. That’s why we’re happy to announce that we integrated Stable-Baselines3 to the Hugging Face Hub. Stable-Baselines3 is one of the most popular PyTorch Deep Reinforcement Learning library that makes it easy to train and test your agents in a variety of environments    

The huggingface_hub library is a lightweight interface that provides a programmatic approach to exploring the hosting endpoints Hugging Face provides: models, datasets, and Spaces. Up until now, searching on the Hub through this interface was tricky to pull off, and there were many aspects of it a user had to “just know” and get    

Tl;dr: This post explains how to use the specificities of the Connectionist Temporal Classification (CTC) architecture in order to achieve very good quality automatic speech recognition (ASR) even on arbitrarily long files or during live inference. Wav2Vec2 is a popular pre-trained model for speech recognition. Released in September 2020 by Meta AI Research, the novel architecture catalyzed progress in    

Sentiment analysis is the automated process of tagging data according to their sentiment, such as positive, negative and neutral. Sentiment analysis allows companies to analyze data at scale, detect insights and automate processes. In the past, sentiment analysis used to be limited to researchers, machine learning engineers or data scientists with experience in natural language processing. However, the AI community has built    

Just as transformers-based models have revolutionized NLP, we’re now seeing an explosion of papers applying them to all sorts of other domains. One of the most revolutionary of these was the Vision Transformer (ViT), which was introduced in June 2021 by a team of researchers at Google Brain. This paper explored how    

BERT, short for Bidirectional Encoder Representations from Transformers, is a Machine Learning (ML) model for natural language processing. It was developed in 2018 by researchers at Google AI Language and serves as a swiss army knife solution to 11+ of the most common language tasks, such as sentiment analysis and named entity recognition. Language has historically been difficult for computers to ‘understand’. Sure, computers can collect, store, and read text inputs but they lack basic language context. So, along came […]

This blog post assumes that the reader is familiar with text generation methods using the different variants of beam search, as explained in the blog post: “How to generate text: using different decoding methods for language generation with Transformers” Unlike ordinary beam search, constrained beam search allows us to exert control    

🤗 datasets is a library that makes it easy to access and share datasets. It also makes it easy to process data efficiently — including working with data which doesn’t fit into memory. When datasets was first launched, it was associated mostly with text data. However, recently, datasets has added increased