split seq2seq script into summarization & translation (#10611)

* split seq2seq script, update docs

* needless diff

* fix readme

* remove test diff

* s/summarization/translation

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* cr

* fix arguments & better mbart/t5 refs

* copyright

Co-authored-by: Suraj Patil <surajp815@gmail.com>

* reword readme

Co-authored-by: Suraj Patil <surajp815@gmail.com>

* s/summarization/translation

* short script names

* fix tests

* fix isort, include mbart doc

* delete old script, update tests

* automate source prefix

* automate source prefix for translation

* s/translation/trans

Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>

* fix script name (short version)

* typos

Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>

* exact parameter

Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>

* remove superfluous source_prefix calls in docs

* rename scripts & warn for source prefix

* black

* flake8

Co-authored-by: theo <theo@matussie.re>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>

examples/seq2seq/README.md

@@ -30,7 +30,7 @@ For the old `finetune_trainer.py` and related utils, see [`examples/legacy/seq2s
 - `FSMTForConditionalGeneration` (translation only)
 - `T5ForConditionalGeneration`
 
-`run_seq2seq.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` and `run_translation.py` are 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.
 
 For custom datasets in `jsonlines` format please see: https://huggingface.co/docs/datasets/loading_datasets.html#json-files
 and you also will find examples of these below.
@@ -39,11 +39,10 @@ and you also will find examples of these below.
 
 Here is an example on a summarization task:
 ```bash
-python examples/seq2seq/run_seq2seq.py \
+python examples/seq2seq/run_summarization.py \
     --model_name_or_path t5-small \
     --do_train \
     --do_eval \
-    --task summarization \
     --dataset_name xsum \
     --output_dir /tmp/tst-summarization \
     --per_device_train_batch_size=4 \
@@ -60,11 +59,10 @@ And here is how you would use it on your own files, after adjusting the values f
 `--train_file`, `--validation_file`, `--text_column` and `--summary_column` to match your setup:
 
 ```bash
-python examples/seq2seq/run_seq2seq.py \
+python examples/seq2seq/run_summarization.py \
     --model_name_or_path t5-small \
     --do_train \
     --do_eval \
-    --task summarization \
     --train_file path_to_csv_or_jsonlines_file \
     --validation_file path_to_csv_or_jsonlines_file \
     --output_dir /tmp/tst-summarization \
@@ -140,14 +138,14 @@ And as with the CSV files, you can specify which values to select from the file,
 Here is an example of a translation fine-tuning with T5:
 
 ```bash
-python examples/seq2seq/run_seq2seq.py \
+python examples/seq2seq/run_translation.py \
     --model_name_or_path t5-small \
     --do_train \
     --do_eval \
-    --task translation_en_to_ro \
+    --source_lang en \
+    --target_lang ro \
     --dataset_name wmt16 \
     --dataset_config_name ro-en \
-    --source_prefix "translate English to Romanian: " \
     --output_dir /tmp/tst-translation \
     --per_device_train_batch_size=4 \
     --per_device_eval_batch_size=4 \
@@ -160,11 +158,10 @@ python examples/seq2seq/run_seq2seq.py \
 And the same with MBart:
 
 ```bash
-python examples/seq2seq/run_seq2seq.py \
+python examples/seq2seq/run_translation.py \
     --model_name_or_path facebook/mbart-large-en-ro  \
     --do_train \
     --do_eval \
-    --task translation_en_to_ro \
     --dataset_name wmt16 \
     --dataset_config_name ro-en \
     --source_lang en_XX \
@@ -180,18 +177,8 @@ python examples/seq2seq/run_seq2seq.py \
 
 Note, that depending on the used model additional language-specific command-line arguments are sometimes required. Specifically:
 
-* MBart models require:
-   ```
-       --source_lang en_XX \
-       --target_lang ro_RO \
-   ```
-* T5 requires:
-
-   ```
-       --source_prefix "translate English to Romanian: "
-   ```
-
-* yet, other models, require neither.
+* MBart models require different `--{source,target}_lang` values, e.g. in place of `en` it expects `en_XX`, for `ro` it expects `ro_RO`. The full MBart specification for language codes can be looked up [here](https://huggingface.co/facebook/mbart-large-cc25)
+* T5 models can use a `--source_prefix` argument to override the otherwise automated prefix of the form `translate {source_lang} to {target_lang}` for `run_translation.py` and `summarize: ` for `run_summarization.py`
 
 Also, if you switch to a different language pair, make sure to adjust the source and target values in all command line arguments.
 
@@ -199,14 +186,14 @@ And here is how you would use the translation finetuning on your own files, afte
 values for the arguments `--train_file`, `--validation_file` to match your setup:
 
 ```bash
-python examples/seq2seq/run_seq2seq.py \
+python examples/seq2seq/run_translation.py \
     --model_name_or_path t5-small \
     --do_train \
     --do_eval \
-    --task translation_en_to_ro \
+    --source_lang en \
+    --target_lang ro \
     --dataset_name wmt16 \
     --dataset_config_name ro-en \
-    --source_prefix "translate English to Romanian: " \
     --train_file path_to_jsonlines_file \
     --validation_file path_to_jsonlines_file \
     --output_dir /tmp/tst-translation \
@@ -229,13 +216,13 @@ Here the languages are Romanian (`ro`) and English (`en`).
 If you want to use a pre-processed dataset that leads to high bleu scores, but for the `en-de` language pair, you can use `--dataset_name wmt14-en-de-pre-processed`, as following:
 
 ```bash
-python examples/seq2seq/run_seq2seq.py \
+python examples/seq2seq/run_translation.py \
     --model_name_or_path t5-small \
     --do_train \
     --do_eval \
-    --task translation_en_to_de \
+    --source_lang en \
+    --target_lang de \
     --dataset_name wmt14-en-de-pre-processed \
-    --source_prefix "translate English to German: " \
     --output_dir /tmp/tst-translation \
     --per_device_train_batch_size=4 \
     --per_device_eval_batch_size=4 \