![]() ALBERT (from Google Research and the Toyota Technological Institute at Chicago) released with the paper ALBERT: A Lite BERT for Self-supervised Learning of Language Representations, by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.The library currently contains JAX, PyTorch and TensorFlow implementations, pretrained model weights, usage scripts and conversion utilities for the following models. ![]() INTERNAL HELPERS for the classes and functions we use internally.MODELS for the classes and functions related to each model implemented in the library.MAIN CLASSES for the main classes exposing the important APIs of the library.HOW-TO GUIDES will show you how to achieve a specific goal like fine-tuning a pretrained model for language modeling or how to create a custom model head.ĬONCEPTUAL GUIDES provides more discussion and explanation of the underlying concepts and ideas behind models, tasks, and the design philosophy of □ Transformers.ĪPI describes each class and function, grouped in: This section will help you gain the basic skills you need to start using □ Transformers. TUTORIALS are a great place to begin if you are new to our library. GET STARTED contains a quick tour and installation instructions to get up and running with □ Transformers. The documentation is organized in five parts: If you are looking for custom support from the Hugging Face team Train your model in three lines of code in one framework, and load it for inference with another.Įach □ Transformers architecture is defined in a standalone Python module so they can be easily customized for research and experiments. Our library supports seamless integration between three of the most popular deep learning libraries: PyTorch, TensorFlow and JAX. □ Multimodal: table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering.□️ Audio: speech recognition and audio classification.□️ Images: image classification, object detection, and segmentation.□ Text: text classification, information extraction, question answering, summarization, translation, and text generation in over 100 languages.The models can be used across different modalities such as: Using pretrained models can reduce your compute costs, carbon footprint, and save you time from training a model from scratch. □ Transformers provides APIs to easily download and train state-of-the-art pretrained models. State-of-the-art Machine Learning for PyTorch, TensorFlow and JAX.
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