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[examples] max samples can't be bigger than the len of dataset (#16501)

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* [examples] max samples can't be bigger than then len of dataset

* do tf and flax
This commit is contained in:
Stas Bekman
2022-03-30 12:33:16 -07:00
committed by GitHub
parent c4deb7b3ae
commit a73281e3e4
26 changed files with 154 additions and 77 deletions
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15
examples/pytorch/question-answering/run_seq2seq_qa.py
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@@ -489,7 +489,8 @@ def main():
train_dataset = raw_datasets["train"]
if data_args.max_train_samples is not None:
# We will select sample from whole data if agument is specified
train_dataset = train_dataset.select(range(data_args.max_train_samples))
max_train_samples = min(len(train_dataset), data_args.max_train_samples)
train_dataset = train_dataset.select(range(max_train_samples))
# Create train feature from dataset
with training_args.main_process_first(desc="train dataset map pre-processing"):
train_dataset = train_dataset.map(
@@ -502,7 +503,8 @@ def main():
)
if data_args.max_train_samples is not None:
# Number of samples might increase during Feature Creation, We select only specified max samples
train_dataset = train_dataset.select(range(data_args.max_train_samples))
max_train_samples = min(len(train_dataset), data_args.max_train_samples)
train_dataset = train_dataset.select(range(max_train_samples))
if training_args.do_eval:
if "validation" not in raw_datasets:
@@ -510,7 +512,8 @@ def main():
eval_examples = raw_datasets["validation"]
if data_args.max_eval_samples is not None:
# We will select sample from whole data
eval_examples = eval_examples.select(range(data_args.max_eval_samples))
max_eval_samples = min(len(eval_examples), data_args.max_eval_samples)
eval_examples = eval_examples.select(range(max_eval_samples))
# Validation Feature Creation
with training_args.main_process_first(desc="validation dataset map pre-processing"):
eval_dataset = eval_examples.map(
@@ -523,7 +526,8 @@ def main():
)
if data_args.max_eval_samples is not None:
# During Feature creation dataset samples might increase, we will select required samples again
eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
eval_dataset = eval_dataset.select(range(max_eval_samples))
if training_args.do_predict:
if "test" not in raw_datasets:
@@ -544,7 +548,8 @@ def main():
)
if data_args.max_predict_samples is not None:
# During Feature creation dataset samples might increase, we will select required samples again
predict_dataset = predict_dataset.select(range(data_args.max_predict_samples))
max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples)
predict_dataset = predict_dataset.select(range(max_predict_samples))
# Data collator
label_pad_token_id = -100 if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id