Update all references to canonical models (#29001)

* Script & Manual edition

* Update

docs/source/en/tasks/language_modeling.md

@@ -29,7 +29,7 @@ the left. This means the model cannot see future tokens. GPT-2 is an example of
 
 This guide will show you how to:
 
-1. Finetune [DistilGPT2](https://huggingface.co/distilgpt2) on the [r/askscience](https://www.reddit.com/r/askscience/) subset of the [ELI5](https://huggingface.co/datasets/eli5) dataset.
+1. Finetune [DistilGPT2](https://huggingface.co/distilbert/distilgpt2) on the [r/askscience](https://www.reddit.com/r/askscience/) subset of the [ELI5](https://huggingface.co/datasets/eli5) dataset.
 2. Use your finetuned model for inference.
 
 <Tip>
@@ -110,7 +110,7 @@ The next step is to load a DistilGPT2 tokenizer to process the `text` subfield:
 ```py
 >>> from transformers import AutoTokenizer
 
->>> tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
+>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilgpt2")
 ```
 
 You'll notice from the example above, the `text` field is actually nested inside `answers`. This means you'll need to
@@ -236,7 +236,7 @@ You're ready to start training your model now! Load DistilGPT2 with [`AutoModelF
 ```py
 >>> from transformers import AutoModelForCausalLM, TrainingArguments, Trainer
 
->>> model = AutoModelForCausalLM.from_pretrained("distilgpt2")
+>>> model = AutoModelForCausalLM.from_pretrained("distilbert/distilgpt2")
 ```
 
 At this point, only three steps remain:
@@ -300,7 +300,7 @@ Then you can load DistilGPT2 with [`TFAutoModelForCausalLM`]:
 ```py
 >>> from transformers import TFAutoModelForCausalLM
 
->>> model = TFAutoModelForCausalLM.from_pretrained("distilgpt2")
+>>> model = TFAutoModelForCausalLM.from_pretrained("distilbert/distilgpt2")
 ```
 
 Convert your datasets to the `tf.data.Dataset` format with [`~transformers.TFPreTrainedModel.prepare_tf_dataset`]:

docs/source/en/tasks/masked_language_modeling.md

@@ -26,7 +26,7 @@ require a good contextual understanding of an entire sequence. BERT is an exampl
 
 This guide will show you how to:
 
-1. Finetune [DistilRoBERTa](https://huggingface.co/distilroberta-base) on the [r/askscience](https://www.reddit.com/r/askscience/) subset of the [ELI5](https://huggingface.co/datasets/eli5) dataset.
+1. Finetune [DistilRoBERTa](https://huggingface.co/distilbert/distilroberta-base) on the [r/askscience](https://www.reddit.com/r/askscience/) subset of the [ELI5](https://huggingface.co/datasets/eli5) dataset.
 2. Use your finetuned model for inference.
 
 <Tip>
@@ -105,7 +105,7 @@ For masked language modeling, the next step is to load a DistilRoBERTa tokenizer
 ```py
 >>> from transformers import AutoTokenizer
 
->>> tokenizer = AutoTokenizer.from_pretrained("distilroberta-base")
+>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilroberta-base")
 ```
 
 You'll notice from the example above, the `text` field is actually nested inside `answers`. This means you'll need to extract the `text` subfield from its nested structure with the [`flatten`](https://huggingface.co/docs/datasets/process#flatten) method:
@@ -226,7 +226,7 @@ You're ready to start training your model now! Load DistilRoBERTa with [`AutoMod
 ```py
 >>> from transformers import AutoModelForMaskedLM
 
->>> model = AutoModelForMaskedLM.from_pretrained("distilroberta-base")
+>>> model = AutoModelForMaskedLM.from_pretrained("distilbert/distilroberta-base")
 ```
 
 At this point, only three steps remain:
@@ -291,7 +291,7 @@ Then you can load DistilRoBERTa with [`TFAutoModelForMaskedLM`]:
 ```py
 >>> from transformers import TFAutoModelForMaskedLM
 
->>> model = TFAutoModelForMaskedLM.from_pretrained("distilroberta-base")
+>>> model = TFAutoModelForMaskedLM.from_pretrained("distilbert/distilroberta-base")
 ```
 
 Convert your datasets to the `tf.data.Dataset` format with [`~transformers.TFPreTrainedModel.prepare_tf_dataset`]:

docs/source/en/tasks/multiple_choice.md

@@ -22,7 +22,7 @@ A multiple choice task is similar to question answering, except several candidat
 
 This guide will show you how to:
 
-1. Finetune [BERT](https://huggingface.co/bert-base-uncased) on the `regular` configuration of the [SWAG](https://huggingface.co/datasets/swag) dataset to select the best answer given multiple options and some context.
+1. Finetune [BERT](https://huggingface.co/google-bert/bert-base-uncased) on the `regular` configuration of the [SWAG](https://huggingface.co/datasets/swag) dataset to select the best answer given multiple options and some context.
 2. Use your finetuned model for inference.
 
 <Tip>
@@ -90,7 +90,7 @@ The next step is to load a BERT tokenizer to process the sentence starts and the
 ```py
 >>> from transformers import AutoTokenizer
 
->>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
+>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
 ```
 
 The preprocessing function you want to create needs to:
@@ -253,7 +253,7 @@ You're ready to start training your model now! Load BERT with [`AutoModelForMult
 ```py
 >>> from transformers import AutoModelForMultipleChoice, TrainingArguments, Trainer
 
->>> model = AutoModelForMultipleChoice.from_pretrained("bert-base-uncased")
+>>> model = AutoModelForMultipleChoice.from_pretrained("google-bert/bert-base-uncased")
 ```
 
 At this point, only three steps remain:
@@ -317,7 +317,7 @@ Then you can load BERT with [`TFAutoModelForMultipleChoice`]:
 ```py
 >>> from transformers import TFAutoModelForMultipleChoice
 
->>> model = TFAutoModelForMultipleChoice.from_pretrained("bert-base-uncased")
+>>> model = TFAutoModelForMultipleChoice.from_pretrained("google-bert/bert-base-uncased")
 ```
 
 Convert your datasets to the `tf.data.Dataset` format with [`~transformers.TFPreTrainedModel.prepare_tf_dataset`]:

docs/source/en/tasks/prompting.md

@@ -76,7 +76,7 @@ Run inference with decoder-only models with the `text-generation` pipeline:
 
 >>> torch.manual_seed(0) # doctest: +IGNORE_RESULT
 
->>> generator = pipeline('text-generation', model = 'gpt2')
+>>> generator = pipeline('text-generation', model = 'openai-community/gpt2')
 >>> prompt = "Hello, I'm a language model"
 
 >>> generator(prompt, max_length = 30)

docs/source/en/tasks/question_answering.md

@@ -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`]:

docs/source/en/tasks/sequence_classification.md

@@ -24,7 +24,7 @@ Text classification is a common NLP task that assigns a label or class to text.
 
 This guide will show you how to:
 
-1. Finetune [DistilBERT](https://huggingface.co/distilbert-base-uncased) on the [IMDb](https://huggingface.co/datasets/imdb) dataset to determine whether a movie review is positive or negative.
+1. Finetune [DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased) on the [IMDb](https://huggingface.co/datasets/imdb) dataset to determine whether a movie review is positive or negative.
 2. Use your finetuned model for inference.
 
 <Tip>
@@ -87,7 +87,7 @@ The next step is to load a DistilBERT tokenizer to preprocess the `text` field:
 ```py
 >>> from transformers import AutoTokenizer
 
->>> tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
+>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased")
 ```
 
 Create a preprocessing function to tokenize `text` and truncate sequences to be no longer than DistilBERT's maximum input length:
@@ -169,7 +169,7 @@ You're ready to start training your model now! Load DistilBERT with [`AutoModelF
 >>> from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer
 
 >>> model = AutoModelForSequenceClassification.from_pretrained(
-...     "distilbert-base-uncased", num_labels=2, id2label=id2label, label2id=label2id
+...     "distilbert/distilbert-base-uncased", num_labels=2, id2label=id2label, label2id=label2id
 ... )
 ```
 
@@ -243,7 +243,7 @@ Then you can load DistilBERT with [`TFAutoModelForSequenceClassification`] along
 >>> from transformers import TFAutoModelForSequenceClassification
 
 >>> model = TFAutoModelForSequenceClassification.from_pretrained(
-...     "distilbert-base-uncased", num_labels=2, id2label=id2label, label2id=label2id
+...     "distilbert/distilbert-base-uncased", num_labels=2, id2label=id2label, label2id=label2id
 ... )
 ```
 

docs/source/en/tasks/summarization.md

@@ -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)
 ```
 

docs/source/en/tasks/token_classification.md

@@ -24,7 +24,7 @@ Token classification assigns a label to individual tokens in a sentence. One of
 
 This guide will show you how to:
 
-1. Finetune [DistilBERT](https://huggingface.co/distilbert-base-uncased) on the [WNUT 17](https://huggingface.co/datasets/wnut_17) dataset to detect new entities.
+1. Finetune [DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased) on the [WNUT 17](https://huggingface.co/datasets/wnut_17) dataset to detect new entities.
 2. Use your finetuned model for inference.
 
 <Tip>
@@ -110,7 +110,7 @@ The next step is to load a DistilBERT tokenizer to preprocess the `tokens` field
 ```py
 >>> from transformers import AutoTokenizer
 
->>> tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
+>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased")
 ```
 
 As you saw in the example `tokens` field above, it looks like the input has already been tokenized. But the input actually hasn't been tokenized yet and you'll need to set `is_split_into_words=True` to tokenize the words into subwords. For example:
@@ -272,7 +272,7 @@ You're ready to start training your model now! Load DistilBERT with [`AutoModelF
 >>> from transformers import AutoModelForTokenClassification, TrainingArguments, Trainer
 
 >>> model = AutoModelForTokenClassification.from_pretrained(
-...     "distilbert-base-uncased", num_labels=13, id2label=id2label, label2id=label2id
+...     "distilbert/distilbert-base-uncased", num_labels=13, id2label=id2label, label2id=label2id
 ... )
 ```
 
@@ -343,7 +343,7 @@ Then you can load DistilBERT with [`TFAutoModelForTokenClassification`] along wi
 >>> from transformers import TFAutoModelForTokenClassification
 
 >>> model = TFAutoModelForTokenClassification.from_pretrained(
-...     "distilbert-base-uncased", num_labels=13, id2label=id2label, label2id=label2id
+...     "distilbert/distilbert-base-uncased", num_labels=13, id2label=id2label, label2id=label2id
 ... )
 ```
 

docs/source/en/tasks/translation.md

@@ -24,7 +24,7 @@ Translation converts a sequence of text from one language to another. It is one
 
 This guide will show you how to:
 
-1. Finetune [T5](https://huggingface.co/t5-small) on the English-French subset of the [OPUS Books](https://huggingface.co/datasets/opus_books) dataset to translate English text to French.
+1. Finetune [T5](https://huggingface.co/google-t5/t5-small) on the English-French subset of the [OPUS Books](https://huggingface.co/datasets/opus_books) dataset to translate English text to French.
 2. Use your finetuned model for inference.
 
 <Tip>
@@ -88,7 +88,7 @@ The next step is to load a T5 tokenizer to process the English-French language p
 ```py
 >>> from transformers import AutoTokenizer
 
->>> checkpoint = "t5-small"
+>>> checkpoint = "google-t5/t5-small"
 >>> tokenizer = AutoTokenizer.from_pretrained(checkpoint)
 ```