BERT utilizes transfer learning to understand the context of text data. In a text classification task, BERT first learns representations of text through pre-training, then fine-tuning the model with labeled data. Efficient implementations can be achieved using Numpy and PyTorch.

Hugging Face is a machine learning (ML) and data science platform and community that helps users build, deploy and train machine learning models. It provides the infrastructure to demo, run and deploy artificial intelligence (AI) in live applications.

🤗 transformers is a library maintained by Hugging Face and the community, for state-of-the-art Machine Learning for Pytorch, TensorFlow and JAX. It provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.

Each one of the models in the library falls into one of the following categories: autoregressive-models. autoencoding-models. seq-to-seq-models.