Hugging Face is a company that provides a variety of tools and services to the AI community, with a particular focus on natural language processing (NLP). Hugging Face is the creator and maintainer of several popular open-source libraries for NLP, including Transformers, Tokenizers, and Datasets. These libraries provide developers with access to state-of-the-art models, tokenization tools, and data processing pipelines.
Hugging Face offers a large and growing collection of pre-trained language models, including models for text classification, question answering, and text generation. These models are available for use in a variety of frameworks and platforms, making it easy for developers to integrate them into their applications. They provide a cloud-based platform called Hugging Face Hub, which allows developers to train and deploy their own language models. The platform provides a range of tools and features to make it easy to build and fine-tune models, and to share them with others. They have a large and active community of developers, researchers, and NLP enthusiasts and host a forum where users can ask questions, share ideas, and get help with their projects.
Hugging Face Transformers
Transformers is a neural network architecture that has gained popularity in the NLP community in recent years. The architecture is based on self-attention mechanisms that allow the model to selectively attend to different parts of the input text, effectively building a representation of the long-term relationships between words.
They provides access to a wide range of pre-trained models that have been trained on large amounts of text data. These models include some of the most popular and state-of-the-art models in NLP, such as BERT (Bidirectional Encoder Representations from Transformers), GPT-2, and RoBERTa. By using these pre-trained models as a starting point, developers can significantly accelerate their development process and achieve better results with less data.
Hugging Face Transformers allows developers to fine-tune pre-trained models on their own data, which can improve their accuracy and relevance for specific use cases. Fine-tuning involves re-training the pre-trained model on a smaller dataset that is specific to the task at hand, such as sentiment analysis or text classification.
Hugging Face Transformers includes a tokeniser library that provides tools for encoding and decoding text into numerical representations that can be processed by the models. The tokeniser library supports a wide range of tokenisation techniques, including subword units and character-level representations, and can be used to tokenise text in more than 100 languages.
Hugging Face Transformers maintains a model zoo that includes a wide range of pre-trained models and fine-tuned models that have been contributed by the community. The model zoo includes models for a variety of NLP tasks, such as text classification, question answering, and language generation.