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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/run_scripts.md
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@@ -87,11 +87,11 @@ pip install -r requirements.txt
<frameworkcontent>
<pt>
The example script downloads and preprocesses a dataset from the 🤗 [Datasets](https://huggingface.co/docs/datasets/) library. Then the script fine-tunes a dataset with the [Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) on an architecture that supports summarization. The following example shows how to fine-tune [T5-small](https://huggingface.co/t5-small) on the [CNN/DailyMail](https://huggingface.co/datasets/cnn_dailymail) dataset. The T5 model requires an additional `source_prefix` argument due to how it was trained. This prompt lets T5 know this is a summarization task.
The example script downloads and preprocesses a dataset from the 🤗 [Datasets](https://huggingface.co/docs/datasets/) library. Then the script fine-tunes a dataset with the [Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) on an architecture that supports summarization. The following example shows how to fine-tune [T5-small](https://huggingface.co/google-t5/t5-small) on the [CNN/DailyMail](https://huggingface.co/datasets/cnn_dailymail) dataset. The T5 model requires an additional `source_prefix` argument due to how it was trained. This prompt lets T5 know this is a summarization task.
```bash
python examples/pytorch/summarization/run_summarization.py \
--model_name_or_path t5-small \
--model_name_or_path google-t5/t5-small \
--do_train \
--do_eval \
--dataset_name cnn_dailymail \
@@ -105,11 +105,11 @@ python examples/pytorch/summarization/run_summarization.py \
```
</pt>
<tf>
The example script downloads and preprocesses a dataset from the 🤗 [Datasets](https://huggingface.co/docs/datasets/) library. Then the script fine-tunes a dataset using Keras on an architecture that supports summarization. The following example shows how to fine-tune [T5-small](https://huggingface.co/t5-small) on the [CNN/DailyMail](https://huggingface.co/datasets/cnn_dailymail) dataset. The T5 model requires an additional `source_prefix` argument due to how it was trained. This prompt lets T5 know this is a summarization task.
The example script downloads and preprocesses a dataset from the 🤗 [Datasets](https://huggingface.co/docs/datasets/) library. Then the script fine-tunes a dataset using Keras on an architecture that supports summarization. The following example shows how to fine-tune [T5-small](https://huggingface.co/google-t5/t5-small) on the [CNN/DailyMail](https://huggingface.co/datasets/cnn_dailymail) dataset. The T5 model requires an additional `source_prefix` argument due to how it was trained. This prompt lets T5 know this is a summarization task.
```bash
python examples/tensorflow/summarization/run_summarization.py \
--model_name_or_path t5-small \
--model_name_or_path google-t5/t5-small \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--output_dir /tmp/tst-summarization \
@@ -133,7 +133,7 @@ The [Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) sup
torchrun \
--nproc_per_node 8 pytorch/summarization/run_summarization.py \
--fp16 \
--model_name_or_path t5-small \
--model_name_or_path google-t5/t5-small \
--do_train \
--do_eval \
--dataset_name cnn_dailymail \
@@ -157,7 +157,7 @@ Tensor Processing Units (TPUs) are specifically designed to accelerate performan
```bash
python xla_spawn.py --num_cores 8 \
summarization/run_summarization.py \
--model_name_or_path t5-small \
--model_name_or_path google-t5/t5-small \
--do_train \
--do_eval \
--dataset_name cnn_dailymail \
@@ -176,7 +176,7 @@ Tensor Processing Units (TPUs) are specifically designed to accelerate performan
```bash
python run_summarization.py \
--tpu name_of_tpu_resource \
--model_name_or_path t5-small \
--model_name_or_path google-t5/t5-small \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--output_dir /tmp/tst-summarization \
@@ -214,7 +214,7 @@ Now you are ready to launch the training:
```bash
accelerate launch run_summarization_no_trainer.py \
--model_name_or_path t5-small \
--model_name_or_path google-t5/t5-small \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--source_prefix "summarize: " \
@@ -233,7 +233,7 @@ A summarization script using a custom dataset would look like this:
```bash
python examples/pytorch/summarization/run_summarization.py \
--model_name_or_path t5-small \
--model_name_or_path google-t5/t5-small \
--do_train \
--do_eval \
--train_file path_to_csv_or_jsonlines_file \
@@ -258,7 +258,7 @@ It is often a good idea to run your script on a smaller number of dataset exampl
```bash
python examples/pytorch/summarization/run_summarization.py \
--model_name_or_path t5-small \
--model_name_or_path google-t5/t5-small \
--max_train_samples 50 \
--max_eval_samples 50 \
--max_predict_samples 50 \
@@ -288,7 +288,7 @@ The first method uses the `output_dir previous_output_dir` argument to resume tr
```bash
python examples/pytorch/summarization/run_summarization.py
--model_name_or_path t5-small \
--model_name_or_path google-t5/t5-small \
--do_train \
--do_eval \
--dataset_name cnn_dailymail \
@@ -305,7 +305,7 @@ The second method uses the `resume_from_checkpoint path_to_specific_checkpoint`
```bash
python examples/pytorch/summarization/run_summarization.py
--model_name_or_path t5-small \
--model_name_or_path google-t5/t5-small \
--do_train \
--do_eval \
--dataset_name cnn_dailymail \
@@ -335,7 +335,7 @@ The following example shows how to upload a model with a specific repository nam
```bash
python examples/pytorch/summarization/run_summarization.py
--model_name_or_path t5-small \
--model_name_or_path google-t5/t5-small \
--do_train \
--do_eval \
--dataset_name cnn_dailymail \