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 left, with the predictions displayed on the right: Here we get a list of objects detected in the image, with a box surrounding the object and a confidence score. Here is how to quickly use a pipeline to classify positive versus negative texts: > from transformers import pipeline # Allocate a pipeline for sentiment-analysis > classifier = pipeline ( 'sentiment-analysis' ) > classifier ( 'We are very happy to introduce pipeline to the transformers repository.' ) 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 own or use a project that you believe should be part of the list, please open a PR to add it! If you are looking for custom support from the Hugging Face team Incredible projects built in the vicinity of transformers. In order to celebrate the 100,000 stars of transformers, we have decided to put the spotlight on theĬommunity, and we have created the awesome-transformers page which lists 100 We want Transformers to enable developers, researchers, students, professors, engineers, and anyone Transformers is more than a toolkit to use pretrained models: it's a community of projects built around it and the Write With Transformer, built by the Hugging Face team, is the official demo of this repo’s text generation capabilities. Zero-shot Video Classification with X-CLIP.Document Question Answering with LayoutLM. Zero-shot Image Classification with CLIP.Audio Classification with Audio Spectrogram Transformer.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.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |