SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

Correct validation_split_percentage argument from int (ex:5) to float (0.05) (#12897)

Browse Source 
* Fixed train_test_split test_size argument

* `Seq2SeqTrainer` set max_length and num_beams only when non None  (#12899)

* set max_length and num_beams only when non None

* fix instance variables

* fix code style

* [FLAX] Minor fixes in CLM example (#12914)

* readme: fix retrieval of vocab size for flax clm example

* examples: fix flax clm example when using training/evaluation files

* Fix module path for symbolic_trace example

Co-authored-by: cchen-dialpad <47165889+cchen-dialpad@users.noreply.github.com>
Co-authored-by: Stefan Schweter <stefan@schweter.it>
Co-authored-by: Sylvain Gugger <sylvain.gugger@gmail.com>
This commit is contained in:
Elysium1436
2021-07-27 22:01:40 -03:00
committed by GitHub
parent 68a441fa4c
commit f3d0866ed9
2 changed files with 2 additions and 2 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
examples/tensorflow/language-modeling/run_clm.py
View File

@@ -438,7 +438,7 @@ def main():
f"Validation file not found: using {data_args.validation_split_percentage}% of the dataset as validation as provided in data_args"
)
train_indices, val_indices = train_test_split(
list(range(len(train_dataset))), test_size=data_args.validation_split_percentage
list(range(len(train_dataset))), test_size=data_args.validation_split_percentage / 100
)
eval_dataset = train_dataset.select(val_indices)

2
examples/tensorflow/language-modeling/run_mlm.py
View File

@@ -499,7 +499,7 @@ def main():
f"Validation file not found: using {data_args.validation_split_percentage}% of the dataset as validation as provided in data_args"
)
train_indices, val_indices = train_test_split(
list(range(len(train_dataset))), test_size=data_args.validation_split_percentage
list(range(len(train_dataset))), test_size=data_args.validation_split_percentage / 100
)
eval_dataset = train_dataset.select(val_indices)