SUMIN/HuggingFace_transformer
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updated logging and saving metrics (#10436)

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* updated logging and saving metrics

* space removal
This commit is contained in:
Bhadresh Savani
2021-02-27 23:23:44 +05:30
committed by GitHub
parent f52a15897b
commit aca6288ff4
12 changed files with 70 additions and 190 deletions
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22
examples/text-classification/run_glue.py
View File

@@ -417,16 +417,9 @@ def main():
trainer.save_model() # Saves the tokenizer too for easy upload
output_train_file = os.path.join(training_args.output_dir, "train_results.txt")
if trainer.is_world_process_zero():
with open(output_train_file, "w") as writer:
logger.info("***** Train results *****")
for key, value in sorted(metrics.items()):
logger.info(f" {key} = {value}")
writer.write(f"{key} = {value}\n")
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir, "trainer_state.json"))
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
# Evaluation
eval_results = {}
@@ -443,13 +436,8 @@ def main():
for eval_dataset, task in zip(eval_datasets, tasks):
eval_result = trainer.evaluate(eval_dataset=eval_dataset)
output_eval_file = os.path.join(training_args.output_dir, f"eval_results_{task}.txt")
if trainer.is_world_process_zero():
with open(output_eval_file, "w") as writer:
logger.info(f"***** Eval results {task} *****")
for key, value in sorted(eval_result.items()):
logger.info(f" {key} = {value}")
writer.write(f"{key} = {value}\n")
trainer.log_metrics("eval", eval_result)
trainer.save_metrics("eval", eval_result)
eval_results.update(eval_result)

14
examples/text-classification/run_tf_glue.py
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@@ -247,18 +247,10 @@ def main():
results = {}
if training_args.do_eval:
logger.info("*** Evaluate ***")
result = trainer.evaluate()
output_eval_file = os.path.join(training_args.output_dir, "eval_results.txt")
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
for key, value in result.items():
logger.info(" %s = %s", key, value)
writer.write("%s = %s\n" % (key, value))
results.update(result)
trainer.log_metrics("eval", result)
trainer.save_metrics("eval", result)
results.update(result)
return results

14
examples/text-classification/run_tf_text_classification.py
View File

@@ -293,18 +293,10 @@ def main():
results = {}
if training_args.do_eval:
logger.info("*** Evaluate ***")
result = trainer.evaluate()
output_eval_file = os.path.join(training_args.output_dir, "eval_results.txt")
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
for key, value in result.items():
logger.info(" %s = %s", key, value)
writer.write("%s = %s\n" % (key, value))
results.update(result)
trainer.log_metrics("eval", result)
trainer.save_metrics("eval", result)
results.update(result)
return results

26
examples/text-classification/run_xnli.py
View File

@@ -291,33 +291,19 @@ def main():
trainer.save_model() # Saves the tokenizer too for easy upload
output_train_file = os.path.join(training_args.output_dir, "train_results.txt")
if trainer.is_world_process_zero():
with open(output_train_file, "w") as writer:
logger.info("***** Train results *****")
for key, value in sorted(metrics.items()):
logger.info(f" {key} = {value}")
writer.write(f"{key} = {value}\n")
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir, "trainer_state.json"))
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
# Evaluation
eval_results = {}
if training_args.do_eval:
logger.info("*** Evaluate ***")
eval_result = trainer.evaluate(eval_dataset=eval_dataset)
output_eval_file = os.path.join(training_args.output_dir, "eval_results_xnli.txt")
if trainer.is_world_process_zero():
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results xnli *****")
for key, value in sorted(eval_result.items()):
logger.info(f" {key} = {value}")
writer.write(f"{key} = {value}\n")
trainer.log_metrics("eval", eval_result)
trainer.save_metrics("eval", eval_result)
eval_results.update(eval_result)
return eval_results