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Update all references to canonical models (#29001)

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* Script & Manual edition

* Update
This commit is contained in:
Lysandre Debut
2024-02-16 08:16:58 +01:00
committed by GitHub
parent 1e402b957d
commit f497f564bb
561 changed files with 2682 additions and 2687 deletions
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8
examples/pytorch/translation/README.md
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@@ -59,11 +59,11 @@ python examples/pytorch/translation/run_translation.py \
MBart and some T5 models require special handling.
T5 models `t5-small`, `t5-base`, `t5-large`, `t5-3b` and `t5-11b` must use an additional argument: `--source_prefix "translate {source_lang} to {target_lang}"`. For example:
T5 models `google-t5/t5-small`, `google-t5/t5-base`, `google-t5/t5-large`, `google-t5/t5-3b` and `google-t5/t5-11b` must use an additional argument: `--source_prefix "translate {source_lang} to {target_lang}"`. For example:
```bash
python examples/pytorch/translation/run_translation.py \
--model_name_or_path t5-small \
--model_name_or_path google-t5/t5-small \
--do_train \
--do_eval \
--source_lang en \
@@ -105,7 +105,7 @@ values for the arguments `--train_file`, `--validation_file` to match your setup
```bash
python examples/pytorch/translation/run_translation.py \
--model_name_or_path t5-small \
--model_name_or_path google-t5/t5-small \
--do_train \
--do_eval \
--source_lang en \
@@ -134,7 +134,7 @@ If you want to use a pre-processed dataset that leads to high BLEU scores, but f
```bash
python examples/pytorch/translation/run_translation.py \
--model_name_or_path t5-small \
--model_name_or_path google-t5/t5-small \
--do_train \
--do_eval \
--source_lang en \

10
examples/pytorch/translation/run_translation.py
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@@ -317,11 +317,11 @@ def main():
logger.info(f"Training/evaluation parameters {training_args}")
if data_args.source_prefix is None and model_args.model_name_or_path in [
"t5-small",
"t5-base",
"t5-large",
"t5-3b",
"t5-11b",
"google-t5/t5-small",
"google-t5/t5-base",
"google-t5/t5-large",
"google-t5/t5-3b",
"google-t5/t5-11b",
]:
logger.warning(
"You're running a t5 model but didn't provide a source prefix, which is expected, e.g. with "