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Unverified Commit cfca638a authored by Philip May's avatar Philip May Committed by GitHub
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Add MT5ForConditionalGeneration as supported arch. to summarization README (#11961) 

* Add MT5ForConditionalGeneration as supported arch.

* Update README.md
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examples/pytorch/summarization/README.md
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...@@ -29,6 +29,7 @@ For the old `finetune_trainer.py` and related utils, see [`examples/legacy/seq2s ...@@ -29,6 +29,7 @@ For the old `finetune_trainer.py` and related utils, see [`examples/legacy/seq2s
- `MarianMTModel` - `MarianMTModel`
- `PegasusForConditionalGeneration` - `PegasusForConditionalGeneration`
- `T5ForConditionalGeneration` - `T5ForConditionalGeneration`
- `MT5ForConditionalGeneration`
`run_summarization.py` is a lightweight example of how to download and preprocess a dataset from the [🤗 Datasets](https://github.com/huggingface/datasets) library or use your own files (jsonlines or csv), then fine-tune one of the architectures above on it. `run_summarization.py` is a lightweight example of how to download and preprocess a dataset from the [🤗 Datasets](https://github.com/huggingface/datasets) library or use your own files (jsonlines or csv), then fine-tune one of the architectures above on it.
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examples/pytorch/translation/README.md
View file @ cfca638a
...@@ -29,6 +29,7 @@ For the old `finetune_trainer.py` and related utils, see [`examples/legacy/seq2s ...@@ -29,6 +29,7 @@ For the old `finetune_trainer.py` and related utils, see [`examples/legacy/seq2s
- `MarianMTModel` - `MarianMTModel`
- `PegasusForConditionalGeneration` - `PegasusForConditionalGeneration`
- `T5ForConditionalGeneration` - `T5ForConditionalGeneration`
- `MT5ForConditionalGeneration`
`run_translation.py` is a lightweight examples of how to download and preprocess a dataset from the [🤗 Datasets](https://github.com/huggingface/datasets) library or use your own files (jsonlines or csv), then fine-tune one of the architectures above on it. `run_translation.py` is a lightweight examples of how to download and preprocess a dataset from the [🤗 Datasets](https://github.com/huggingface/datasets) library or use your own files (jsonlines or csv), then fine-tune one of the architectures above on it.
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