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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/question_answering.md
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@@ -27,7 +27,7 @@ Question answering tasks return an answer given a question. If you've ever asked
This guide will show you how to:
1. Finetune [DistilBERT](https://huggingface.co/distilbert-base-uncased) on the [SQuAD](https://huggingface.co/datasets/squad) dataset for extractive question answering.
1. Finetune [DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased) on the [SQuAD](https://huggingface.co/datasets/squad) dataset for extractive question answering.
2. Use your finetuned model for inference.
<Tip>
@@ -100,7 +100,7 @@ The next step is to load a DistilBERT tokenizer to process the `question` and `c
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased")
```
There are a few preprocessing steps particular to question answering tasks you should be aware of:
@@ -206,7 +206,7 @@ You're ready to start training your model now! Load DistilBERT with [`AutoModelF
```py
>>> from transformers import AutoModelForQuestionAnswering, TrainingArguments, Trainer
>>> model = AutoModelForQuestionAnswering.from_pretrained("distilbert-base-uncased")
>>> model = AutoModelForQuestionAnswering.from_pretrained("distilbert/distilbert-base-uncased")
```
At this point, only three steps remain:
@@ -271,7 +271,7 @@ Then you can load DistilBERT with [`TFAutoModelForQuestionAnswering`]:
```py
>>> from transformers import TFAutoModelForQuestionAnswering
>>> model = TFAutoModelForQuestionAnswering.from_pretrained("distilbert-base-uncased")
>>> model = TFAutoModelForQuestionAnswering.from_pretrained("distilbert/distilbert-base-uncased")
```
Convert your datasets to the `tf.data.Dataset` format with [`~transformers.TFPreTrainedModel.prepare_tf_dataset`]: