From 5987e0ab69b639a0aef335fc9357b0b869948313 Mon Sep 17 00:00:00 2001
From: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Date: Mon, 13 Feb 2023 11:37:48 -0800
Subject: [PATCH] Clarify available pipelines in quicktour (#21607)
clarify available pipelines
---
docs/source/en/quicktour.mdx | 34 +++++++++++++++++++---------------
1 file changed, 19 insertions(+), 15 deletions(-)
diff --git a/docs/source/en/quicktour.mdx b/docs/source/en/quicktour.mdx
index 8b56eac0be..76f46a28a6 100644
--- a/docs/source/en/quicktour.mdx
+++ b/docs/source/en/quicktour.mdx
@@ -41,25 +41,29 @@ pip install tensorflow
-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:
+
+
+
+For a complete list of available tasks, check out the [pipeline API reference](./main_classes/pipelines).
+
+
| **Task** | **Description** | **Modality** | **Pipeline identifier** |
|------------------------------|--------------------------------------------------------------------------------------------------------------|-----------------|-----------------------------------------------|
-| 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") |
-| Name entity recognition | assign a label to each token in a sequence (people, organization, location, etc.) | NLP | pipeline(task="ner") |
-| Question answering | extract an answer from the text given some context and a question | NLP | pipeline(task="question-answering") |
-| Fill-mask | predict the correct masked token in a sequence | NLP | pipeline(task="fill-mask") |
-| Summarization | generate a summary of a sequence of text or document | NLP | pipeline(task="summarization") |
-| Translation | translate text from one language into another | NLP | pipeline(task="translation") |
-| Image classification | assign a label to an image | Computer vision | pipeline(task="image-classification") |
-| Image segmentation | assign a label to each individual pixel of an image (supports semantic, panoptic, and instance segmentation) | Computer vision | pipeline(task="image-segmentation") |
-| Object detection | predict the bounding boxes and classes of objects in an image | Computer vision | pipeline(task="object-detection") |
-| Audio classification | assign a label to an audio file | Audio | pipeline(task="audio-classification") |
-| 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") |
+| Text classification | assign a label to a given sequence of text | NLP | pipeline(task=“sentiment-analysis”) |
+| Text generation | generate text given a prompt | NLP | pipeline(task=“text-generation”) |
+| Summarization | generate a summary of a sequence of text or document | NLP | pipeline(task=“summarization”) |
+| Image classification | assign a label to an image | Computer vision | pipeline(task=“image-classification”) |
+| Image segmentation | assign a label to each individual pixel of an image (supports semantic, panoptic, and instance segmentation) | Computer vision | pipeline(task=“image-segmentation”) |
+| Object detection | predict the bounding boxes and classes of objects in an image | Computer vision | pipeline(task=“object-detection”) |
+| Audio classification | assign a label to some audio data | Audio | pipeline(task=“audio-classification”) |
+| Automatic speech recognition | transcribe speech into text | Audio | pipeline(task=“automatic-speech-recognition”) |
+| Visual question answering | answer a question about the image, given an image and a question | Multimodal | pipeline(task=“vqa”) |
+| Document question answering | answer a question about a document, given an image and a question | Multimodal | pipeline(task="document-question-answering") |
+| Image captioning | generate a caption for a given image | Multimodal | pipeline(task="image-to-text") |
-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:
+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:
```py
>>> from transformers import pipeline