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Update all references to canonical models (#29001)

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* Script & Manual edition

* Update
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Lysandre Debut
2024-02-16 08:16:58 +01:00
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docs/source/en/tasks/summarization.md
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@@ -27,7 +27,7 @@ Summarization creates a shorter version of a document or an article that capture
This guide will show you how to:
1. Finetune [T5](https://huggingface.co/t5-small) on the California state bill subset of the [BillSum](https://huggingface.co/datasets/billsum) dataset for abstractive summarization.
1. Finetune [T5](https://huggingface.co/google-t5/t5-small) on the California state bill subset of the [BillSum](https://huggingface.co/datasets/billsum) dataset for abstractive summarization.
2. Use your finetuned model for inference.
<Tip>
@@ -92,7 +92,7 @@ The next step is to load a T5 tokenizer to process `text` and `summary`:
```py
>>> from transformers import AutoTokenizer
>>> checkpoint = "t5-small"
>>> checkpoint = "google-t5/t5-small"
>>> tokenizer = AutoTokenizer.from_pretrained(checkpoint)
```