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Update all references to canonical models (#29001)

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Lysandre Debut
2024-02-16 08:16:58 +01:00
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commit f497f564bb
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docs/source/en/tasks/language_modeling.md
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@@ -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`]: