Train state-of-the-art models in 3 lines of code.Dozens of architectures with over 60,000 pretrained models across all modalities.Ĭhoose the right framework for every part of a model's lifetime:.Practitioners can reduce compute time and production costs.Researchers can share trained models instead of always retraining.Lower compute costs, smaller carbon footprint: A unified API for using all our pretrained models.Few user-facing abstractions with just three classes to learn.Low barrier to entry for educators and practitioners.High performance on natural language understanding & generation, computer vision, and audio tasks.
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This tutorial explains how to integrate such a model into a classic PyTorch or TensorFlow training loop, or how to use our Trainer API to quickly fine-tune on a new dataset. The model itself is a regular Pytorch nn.Module or a TensorFlow tf.keras.Model (depending on your backend) which you can use as usual.
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It will output a dictionary that you can use in downstream code or simply directly pass to your model using the ** argument unpacking operator. The tokenizer is responsible for all the preprocessing the pretrained model expects, and can be called directly on a single string (as in the above examples) or a list. > inputs = tokenizer( "Hello world!", return_tensors = "tf") > from transformers import AutoTokenizer, TFAutoModel > tokenizer = AutoTokenizer.
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In addition to pipeline, to download and use any of the pretrained models on your given task, all it takes is three lines of code. You can learn more about the tasks supported by the pipeline API in this tutorial. Here is the original image on the right, with the predictions displayed on the left: Here we get a list of objects detected in the image, with a box surrounding the object and a confidence score. # Allocate a pipeline for object detection > object_detector = pipeline( 'object_detection') > import requests > from PIL import Image > from transformers import pipeline # Download an image with cute cats > url = "" > image_data = requests. Here is how to quickly use a pipeline to classify positive versus negative texts: Pipelines group together a pretrained model with the preprocessing that was used during that model's training. To immediately use a model on a given input (text, image, audio. If you are looking for custom support from the Hugging Face team Write With Transformer, built by the Hugging Face team, is the official demo of this repo’s text generation capabilities. Automatic Speech Recognition with Wav2Vec2.Natural Language Inference with RoBERTa.
We also offer private model hosting, versioning, & an inference API for public and private models. You can test most of our models directly on their pages from the model hub. It's straightforward to train your models with one before loading them for inference with the other. 🤗 Transformers is backed by the three most popular deep learning libraries - Jax, PyTorch and TensorFlow - with a seamless integration between them. At the same time, each python module defining an architecture is fully standalone and can be modified to enable quick research experiments. 🤗 Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and then share them with the community on our model hub. Transformer models can also perform tasks on several modalities combined, such as table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering.
🤗 Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow
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