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Transformers - Hugging Face
https://huggingface.co/docs/transformers/index
Web🤗 Transformers. State-of-the-art Machine Learning for PyTorch, TensorFlow, and JAX. 🤗 Transformers provides APIs and tools to easily download and train state-of-the-art pretrained models. Using pretrained models can reduce your compute costs, carbon footprint, and save you the time and resources required to train a model from scratch.
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GitHub - huggingface/transformers: Transformers: State-of-the-art
https://github.com/huggingface/transformers
WebState-of-the-art Machine Learning for JAX, PyTorch and TensorFlow. 🤗 Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. These models can be applied on: 📝 Text, for tasks like text classification, information extraction, question answering, summarization ...
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Installation - Hugging Face
https://huggingface.co/docs/transformers/installation
WebInstall 🤗 Transformers for whichever deep learning library you’re working with, setup your cache, and optionally configure 🤗 Transformers to run offline. 🤗 Transformers is tested on Python 3.6+, PyTorch 1.1.0+, TensorFlow 2.0+, and Flax. Follow the installation instructions below for the deep learning library you are using:
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Using transformers at Hugging Face
https://huggingface.co/docs/hub/transformers
Web🤗 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.
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Models - Hugging Face
https://huggingface.co/docs/transformers/main_classes/model
Web>>> from transformers import BertConfig, ... — Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the repository you want to push to with repo_id (will default to the name of save_directory in your namespace). signatures (dict or tf.function, optional) — Model’s signature used for serving.
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Quick tour - Hugging Face
https://huggingface.co/docs/transformers/quicktour
WebGet up and running with 🤗 Transformers! Whether you’re a developer or an everyday user, this quick tour will help you get started and show you how to use the pipeline() for inference, load a pretrained model and preprocessor with an AutoClass, and quickly train a model with PyTorch or TensorFlow.
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Hugging Face – The AI community building the future.
https://huggingface.co/
WebHugging Face – The AI community building the future. NEW. Try Cohere Command R+ on HuggingChat. The AI community building the future. The platform where the machine learning community collaborates on models, datasets, and applications. Trending on this week. Models. meta-llama/Meta-Llama-3-8B. Updated 4 days ago • 172k • 2.08k.
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GitHub - huggingface/course: The Hugging Face course on Transformers
https://github.com/huggingface/course
WebThe course teaches you about applying Transformers to various tasks in natural language processing and beyond. Along the way, you'll learn how to use the Hugging Face ecosystem — 🤗 Transformers , 🤗 Datasets , 🤗 Tokenizers , and 🤗 Accelerate — as well as the Hugging Face Hub .
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Getting Started with Transformers.ipynb - Colab - Google Colab
https://colab.research.google.com/github/huggingface/education-toolkit/blob/main/03_getting-started-with-transformers.ipynb
WebThe pipeline() function from the transformers library can be used to run inference with models from the Hugging Face Hub. This tutorial is based on the first of our O'Reilly book Natural...
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Releases · huggingface/transformers · GitHub
https://github.com/huggingface/transformers/releases
Webhuggingface / transformers Public. Notifications. Fork 24.7k. Star 125k. 3 days ago. LysandreJik. v4.40.0. 745bbfe. Compare. v4.40.0: Llama 3, Idefics 2, Recurrent Gemma, Jamba, DBRX, OLMo, Qwen2MoE, Grounding Dino Latest. New model additions.
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