Validation split added: custom data files @sgugger, @patil-suraj (#12407)

* Validation split added: custom data files

Validation split added in case of no validation file and loading custom data

* Updated documentation with custom file usage

Updated documentation with custom file usage

* Update README.md

* Update README.md

* Update README.md

* Made some suggested stylistic changes

* Used logger instead of print.

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

* Made similar changes to add validation split

In case of a missing validation file, a validation split will be used now.

* max_train_samples to be used for training only

max_train_samples got misplaced, now corrected so that it is applied on training data only, not whole data.

* styled

* changed ordering

* Improved language of documentation

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

* Improved language of documentation

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

* Fixed styling issue

* Update run_mlm.py

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

examples/tensorflow/language-modeling/README.md

@@ -49,6 +49,14 @@ python run_mlm.py \
 --dataset_config_name wikitext-103-raw-v1
 ```
 
+When using a custom dataset, the validation file can be separately passed as an input argument. Otherwise some split (customizable) of training data is used as validation.
+```
+python run_mlm.py \
+--model_name_or_path distilbert-base-cased \
+--output_dir output \
+--train_file train_file_path
+```
+
 ## run_clm.py
 
 This script trains a causal language model.
@@ -61,3 +69,12 @@ python run_clm.py \
 --dataset_name wikitext \
 --dataset_config_name wikitext-103-raw-v1
 ```
+
+When using a custom dataset, the validation file can be separately passed as an input argument. Otherwise some split (customizable) of training data is used as validation.
+
+```
+python run_clm.py \
+--model_name_or_path distilgpt2 \
+--output_dir output \
+--train_file train_file_path
+```

examples/tensorflow/language-modeling/run_clm.py

@@ -37,6 +37,7 @@ import datasets
 import numpy as np
 import tensorflow as tf
 from datasets import load_dataset
+from sklearn.model_selection import train_test_split
 
 import transformers
 from transformers import (
@@ -429,7 +430,18 @@ def main():
     )
 
     train_dataset = lm_datasets["train"]
-    eval_dataset = lm_datasets["validation"]
+    if data_args.validation_file is not None:
+        eval_dataset = lm_datasets["validation"]
+    else:
+        logger.info(
+            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
+        )
+
+        eval_dataset = train_dataset.select(val_indices)
+        train_dataset = train_dataset.select(train_indices)
 
     if data_args.max_train_samples is not None:
         train_dataset = train_dataset.select(range(data_args.max_train_samples))

examples/tensorflow/language-modeling/run_mlm.py

@@ -39,6 +39,7 @@ import datasets
 import numpy as np
 import tensorflow as tf
 from datasets import load_dataset
+from sklearn.model_selection import train_test_split
 
 import transformers
 from transformers import (
@@ -363,6 +364,7 @@ def main():
         if extension == "txt":
             extension = "text"
         raw_datasets = load_dataset(extension, data_files=data_files)
+
     # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
     # https://huggingface.co/docs/datasets/loading_datasets.html.
     # endregion
@@ -488,9 +490,22 @@ def main():
         )
 
     train_dataset = tokenized_datasets["train"]
+
+    if data_args.validation_file is not None:
+        eval_dataset = tokenized_datasets["validation"]
+    else:
+        logger.info(
+            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
+        )
+
+        eval_dataset = train_dataset.select(val_indices)
+        train_dataset = train_dataset.select(train_indices)
+
     if data_args.max_train_samples is not None:
         train_dataset = train_dataset.select(range(data_args.max_train_samples))
-    eval_dataset = tokenized_datasets["validation"]
     if data_args.max_eval_samples is not None:
         eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))