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[Examples] Fixes inconsistency around eval vs val and predict vs test (#11380)

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* added changes for uniformity

* modified files

* corrected typo

* fixed qa scripts

* fix typos

* fixed predict typo in qa no trainer

* fixed test file

* reverted trainer changes

* reverted trainer changes in custom exmaples

* updated readme

* added changes in deepspeed test

* added changes for predict and eval
This commit is contained in:
Bhadresh Savani
2021-04-26 17:24:31 +01:00
committed by GitHub
parent 7959d83599
commit 1d30ec95c7
19 changed files with 288 additions and 276 deletions
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32
examples/tensorflow/text-classification/run_text_classification.py
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@@ -164,17 +164,17 @@ class DataTrainingArguments:
"value if set."
},
)
max_val_samples: Optional[int] = field(
max_eval_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of validation examples to this "
"help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
},
)
max_test_samples: Optional[int] = field(
max_predict_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of test examples to this "
"help": "For debugging purposes or quicker training, truncate the number of predict examples to this "
"value if set."
},
)
@@ -468,13 +468,13 @@ def main():
if "validation" in datasets:
eval_dataset = datasets["validation"]
if data_args.max_val_samples is not None:
eval_dataset = eval_dataset.select(range(data_args.max_val_samples))
if data_args.max_eval_samples is not None:
eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
if "test" in datasets:
test_dataset = datasets["test"]
if data_args.max_test_samples is not None:
test_dataset = test_dataset.select(range(data_args.max_test_samples))
predict_dataset = datasets["test"]
if data_args.max_predict_samples is not None:
predict_dataset = predict_dataset.select(range(data_args.max_predict_samples))
# endregion
@@ -513,15 +513,15 @@ def main():
# region Prediction
if "test" in datasets:
logger.info("Doing predictions on test dataset...")
logger.info("Doing predictions on Predict dataset...")
test_dataset = DataSequence(
test_dataset, non_label_column_names, batch_size=training_args.per_device_eval_batch_size, labels=False
predict_dataset = DataSequence(
predict_dataset, non_label_column_names, batch_size=training_args.per_device_eval_batch_size, labels=False
)
predictions = model.predict(test_dataset)["logits"]
predictions = model.predict(predict_dataset)["logits"]
predictions = np.squeeze(predictions) if is_regression else np.argmax(predictions, axis=1)
output_test_file = os.path.join(training_args.output_dir, "test_results.txt")
with open(output_test_file, "w") as writer:
output_predict_file = os.path.join(training_args.output_dir, "predict_results.txt")
with open(output_predict_file, "w") as writer:
writer.write("index\tprediction\n")
for index, item in enumerate(predictions):
if is_regression:
@@ -529,7 +529,7 @@ def main():
else:
item = model.config.id2label[item]
writer.write(f"{index}\t{item}\n")
logger.info(f"Wrote predictions to {output_test_file}!")
logger.info(f"Wrote predictions to {output_predict_file}!")
# endregion