#Python tkinter page layers how to
#Python tkinter page layers code

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.INTERNAL HELPERS details utility classes and functions used internally.MODELS details the classes and functions related to each model implemented in the library.MAIN CLASSES details the most important classes like configuration, model, tokenizer, and pipeline.

#Python tkinter page layers how to

HOW-TO GUIDES show you how to achieve a specific goal, like finetuning a pretrained model for language modeling or how to write and share a custom model.ĬONCEPTUAL GUIDES offers more discussion and explanation of the underlying concepts and ideas behind models, tasks, and the design philosophy of 🤗 Transformers. This section will help you gain the basic skills you need to start using the library. TUTORIALS are a great place to start if you’re a beginner. GET STARTED provides a quick tour of the library and installation instructions to get up and running.

The documentation is organized into five sections: Join the growing community on the Hub, forum, or Discord today! If you are looking for custom support from the Hugging Face team Models can also be exported to a format like ONNX and TorchScript for deployment in production environments.

#Python tkinter page layers code

This provides the flexibility to use a different framework at each stage of a model’s life train a model in three lines of code in one framework, and load it for inference in another. 🤗 Transformers support framework interoperability between PyTorch, TensorFlow, and JAX. 🐙 Multimodal: table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering. 🗣️ Audio: automatic speech recognition and audio classification. 🖼️ Computer Vision: image classification, object detection, and segmentation. 📝 Natural Language Processing: text classification, named entity recognition, question answering, language modeling, summarization, translation, multiple choice, and text generation. These models support common tasks in different modalities, such as: Using pretrained models can reduce your compute costs, carbon footprint, and save you the time and resources required to train a model from scratch. 🤗 Transformers provides APIs and tools to easily download and train state-of-the-art pretrained models. State-of-the-art Machine Learning for PyTorch, TensorFlow, and JAX.