Fix broken links in the agent docs (#23297)
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@ -19,7 +19,7 @@ can vary as the APIs or underlying models are prone to change.
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</Tip>
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To learn more about agents and tools make sure to read the [introductory guide](../agents_and_tools). This page
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To learn more about agents and tools make sure to read the [introductory guide](../transformers_agents). This page
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contains the API docs for the underlying classes.
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## Agents
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@ -266,16 +266,16 @@ with the code generated by the agent.
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We identify a set of tools that can empower such agents. Here is an updated list of the tools we have integrated
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in `transformers`:
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- **Document question answering**: given a document (such as a PDF) in image format, answer a question on this document ([Donut](../model_doc/donut))
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- **Text question answering**: given a long text and a question, answer the question in the text ([Flan-T5](../model_doc/flan-t5))
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- **Unconditional image captioning**: Caption the image! ([BLIP](../model_doc/blip))
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- **Image question answering**: given an image, answer a question on this image ([VILT](../model_doc/vilt))
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- **Image segmentation**: given an image and a prompt, output the segmentation mask of that prompt ([CLIPSeg](../model_doc/clipseg))
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- **Speech to text**: given an audio recording of a person talking, transcribe the speech into text ([Whisper](../model_doc/whisper))
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- **Text to speech**: convert text to speech ([SpeechT5](../model_doc/speecht5))
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- **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))
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- **Text summarization**: summarize a long text in one or a few sentences ([BART](../model_doc/bart))
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- **Translation**: translate the text into a given language ([NLLB](../model_doc/nllb))
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- **Document question answering**: given a document (such as a PDF) in image format, answer a question on this document ([Donut](./model_doc/donut))
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- **Text question answering**: given a long text and a question, answer the question in the text ([Flan-T5](./model_doc/flan-t5))
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- **Unconditional image captioning**: Caption the image! ([BLIP](./model_doc/blip))
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- **Image question answering**: given an image, answer a question on this image ([VILT](./model_doc/vilt))
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- **Image segmentation**: given an image and a prompt, output the segmentation mask of that prompt ([CLIPSeg](./model_doc/clipseg))
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- **Speech to text**: given an audio recording of a person talking, transcribe the speech into text ([Whisper](./model_doc/whisper))
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- **Text to speech**: convert text to speech ([SpeechT5](./model_doc/speecht5))
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- **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))
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- **Text summarization**: summarize a long text in one or a few sentences ([BART](./model_doc/bart))
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- **Translation**: translate the text into a given language ([NLLB](./model_doc/nllb))
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These tools have an integration in transformers, and can be used manually as well, for example:
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