The [`pipeline`] is the easiest way to use a pretrained model for inference. You can use the [`pipeline`] out-of-the-box for many tasks across different modalities. Take a look at the table below for some supported tasks:
The [`pipeline`] is the easiest and fastest way to use a pretrained model for inference. You can use the [`pipeline`] out-of-the-box for many tasks across different modalities, some of which are shown in the table below:
<Tip>
For a complete list of available tasks, check out the [pipeline API reference](./main_classes/pipelines).
| Text classification | assign a label to a given sequence of text | NLP | pipeline(task="sentiment-analysis") |
| Text classification | assign a label to a given sequence of text | NLP | pipeline(task=“sentiment-analysis”) |
| Text generation | generate text that follows a given prompt | NLP | pipeline(task="text-generation") |
| Text generation | generate text given a prompt | NLP | pipeline(task=“text-generation”) |
| Name entity recognition | assign a label to each token in a sequence (people, organization, location, etc.) | NLP | pipeline(task="ner") |
| Summarization | generate a summary of a sequence of text or document | NLP | pipeline(task=“summarization”) |
| Question answering | extract an answer from the text given some context and a question | NLP | pipeline(task="question-answering") |
| Image classification | assign a label to an image | Computer vision | pipeline(task=“image-classification”) |
| Fill-mask | predict the correct masked token in a sequence | NLP | pipeline(task="fill-mask") |
| Image segmentation | assign a label to each individual pixel of an image (supports semantic, panoptic, and instance segmentation) | Computer vision | pipeline(task=“image-segmentation”) |
| Summarization | generate a summary of a sequence of text or document | NLP | pipeline(task="summarization") |
| Object detection | predict the bounding boxes and classes of objects in an image | Computer vision | pipeline(task=“object-detection”) |
| Translation | translate text from one language into another | NLP | pipeline(task="translation") |
| Audio classification | assign a label to some audio data | Audio | pipeline(task=“audio-classification”) |
| Image classification | assign a label to an image | Computer vision | pipeline(task="image-classification") |
| Automatic speech recognition | transcribe speech into text | Audio | pipeline(task=“automatic-speech-recognition”) |
| Image segmentation | assign a label to each individual pixel of an image (supports semantic, panoptic, and instance segmentation) | Computer vision | pipeline(task="image-segmentation") |
| Visual question answering | answer a question about the image, given an image and a question | Multimodal | pipeline(task=“vqa”) |
| Object detection | predict the bounding boxes and classes of objects in an image | Computer vision | pipeline(task="object-detection") |
| Document question answering | answer a question about a document, given an image and a question | Multimodal | pipeline(task="document-question-answering") |
| Audio classification | assign a label to an audio file | Audio | pipeline(task="audio-classification") |
| Image captioning | generate a caption for a given image | Multimodal | pipeline(task="image-to-text") |
| Automatic speech recognition | extract speech from an audio file into text | Audio | pipeline(task="automatic-speech-recognition") |
| Visual question answering | given an image and a question, correctly answer a question about the image | Multimodal | pipeline(task="vqa") |
Start by creating an instance of [`pipeline`] and specifying a task you want to use it for. In this guide, you'll use the [`pipeline`] for sentiment analysis as an example:
Start by creating an instance of [`pipeline`] and specifying a task you want to use it for. You can use the [`pipeline`] for any of the previously mentioned tasks, and for a complete list of supported tasks, take a look at the [pipeline API reference](./main_classes/pipelines). In this guide though, you'll use the [`pipeline`] for sentiment analysis as an example:
