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Fix broken links in the agent docs #23297

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May 11, 2023
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Fix broken links in the agent docs #23297

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2 changes: 1 addition & 1 deletion docs/source/en/main_classes/agent.mdx
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Expand Up @@ -19,7 +19,7 @@ can vary as the APIs or underlying models are prone to change.

</Tip>

To learn more about agents and tools make sure to read the [introductory guide](../agents_and_tools). This page
To learn more about agents and tools make sure to read the [introductory guide](../transformers_agents). This page
contains the API docs for the underlying classes.

## Agents
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20 changes: 10 additions & 10 deletions docs/source/en/transformers_agents.mdx
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Expand Up @@ -256,16 +256,16 @@ with the code generated by the agent.
We identify a set of tools that can empower such agents. Here is an updated list of the tools we have integrated
in `transformers`:

- **Document question answering**: given a document (such as a PDF) in image format, answer a question on this document ([Donut](../model_doc/donut))
- **Text question answering**: given a long text and a question, answer the question in the text ([Flan-T5](../model_doc/flan-t5))
- **Unconditional image captioning**: Caption the image! ([BLIP](../model_doc/blip))
- **Image question answering**: given an image, answer a question on this image ([VILT](../model_doc/vilt))
- **Image segmentation**: given an image and a prompt, output the segmentation mask of that prompt ([CLIPSeg](../model_doc/clipseg))
- **Speech to text**: given an audio recording of a person talking, transcribe the speech into text ([Whisper](../model_doc/whisper))
- **Text to speech**: convert text to speech ([SpeechT5](../model_doc/speecht5))
- **Zero-shot text classification**: given a text and a list of labels, identify to which label the text corresponds the most ([BART](../model_doc/bart))
- **Text summarization**: summarize a long text in one or a few sentences ([BART](../model_doc/bart))
- **Translation**: translate the text into a given language ([NLLB](../model_doc/nllb))
- **Document question answering**: given a document (such as a PDF) in image format, answer a question on this document ([Donut](./model_doc/donut))
- **Text question answering**: given a long text and a question, answer the question in the text ([Flan-T5](./model_doc/flan-t5))
- **Unconditional image captioning**: Caption the image! ([BLIP](./model_doc/blip))
- **Image question answering**: given an image, answer a question on this image ([VILT](./model_doc/vilt))
- **Image segmentation**: given an image and a prompt, output the segmentation mask of that prompt ([CLIPSeg](./model_doc/clipseg))
- **Speech to text**: given an audio recording of a person talking, transcribe the speech into text ([Whisper](./model_doc/whisper))
- **Text to speech**: convert text to speech ([SpeechT5](./model_doc/speecht5))
- **Zero-shot text classification**: given a text and a list of labels, identify to which label the text corresponds the most ([BART](./model_doc/bart))
- **Text summarization**: summarize a long text in one or a few sentences ([BART](./model_doc/bart))
- **Translation**: translate the text into a given language ([NLLB](./model_doc/nllb))

These tools have an integration in transformers, and can be used manually as well, for example:

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