json to jsonlines, and doc, and typo (#10043)

examples/seq2seq/README.md

@@ -33,7 +33,9 @@ This directory is in a bit of messy state and is undergoing some cleaning, pleas
 
 ## New script
 
-The new script for fine-tuning a model on a summarization or translation task is `run_seq2seq.py`. It 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 (json or csv), then fine-tune one of the architectures above on it.
+The new script for fine-tuning a model on a summarization or translation task is `run_seq2seq.py`. It 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.
+
+For custom datasets in `jsonlines` format please see: https://huggingface.co/docs/datasets/loading_datasets.html#json-files
 
 Here is an example on a summarization task:
 ```bash
@@ -50,15 +52,15 @@ python examples/seq2seq/run_seq2seq.py \
     --predict_with_generate
 ```
 
-And here is how you would use it on your own files (replace `path_to_csv_or_json_file`, `text_column_name` and `summary_column_name` by the relevant values):
+And here is how you would use it on your own files (replace `path_to_csv_or_jsonlines_file`, `text_column_name` and `summary_column_name` by the relevant values):
 ```bash
 python examples/seq2seq/run_seq2seq.py \
-    -model_name_or_path t5-small \
+    --model_name_or_path t5-small \
     --do_train \
     --do_eval \
     --task summarization \
-    --train_file path_to_csv_or_json_file \
-    --validation_file path_to_csv_or_json_file \
+    --train_file path_to_csv_or_jsonlines_file \
+    --validation_file path_to_csv_or_jsonlines_file \
     --output_dir ~/tmp/tst-summarization \
     --overwrite_output_dir \
     --per_device_train_batch_size=4 \
@@ -87,7 +89,7 @@ python examples/seq2seq/run_seq2seq.py \
     --predict_with_generate
 ```
 
-And here is how you would use it on your own files (replace `path_to_json_file`, by the relevant values):
+And here is how you would use it on your own files (replace `path_to_jsonlines_file`, by the relevant values):
 ```bash
 python examples/seq2seq/run_seq2seq.py \
     --model_name_or_path sshleifer/student_marian_en_ro_6_1 \
@@ -98,15 +100,15 @@ python examples/seq2seq/run_seq2seq.py \
     --dataset_config_name ro-en \
     --source_lang en_XX \
     --target_lang ro_RO\
-    --train_file path_to_json_file \
-    --validation_file path_to_json_file \
+    --train_file path_to_jsonlines_file \
+    --validation_file path_to_jsonlines_file \
     --output_dir ~/tmp/tst-translation \
     --per_device_train_batch_size=4 \
     --per_device_eval_batch_size=4 \
     --overwrite_output_dir \
     --predict_with_generate
 ```
-Here the files are expected to be JSON files, with each input being a dictionary with a key `"translation"` containing one key per language (here `"en"` and `"ro"`).
+Here the files are expected to be JSONLINES files, with each input being a dictionary with a key `"translation"` containing one key per language (here `"en"` and `"ro"`).
 
 ## Old script
 
@@ -417,4 +419,3 @@ uses 12,723 batches of length 48 and takes slightly more time 9.5 minutes.
 The feature is still experimental, because:
 + we can make it much more robust if we have memory mapped/preprocessed datasets.
 + The speedup over sortish sampler is not that large at the moment.
-