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[Examples] Added predict stage and Updated Example Template (#10868)

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* added predict stage

* added test keyword in exception message

* removed example specific saving predictions

* fixed f-string error

* removed extra line

Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>

Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
This commit is contained in:
Bhadresh Savani 2021-03-23 23:07:59 +05:30 committed by GitHub
parent fb2b89840b
commit 7ef40120a0
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2 changed files with 103 additions and 14 deletions
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56
examples/text-classification/run_xnli.py
View File

@ -207,14 +207,22 @@ def main():
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
# Downloading and loading xnli dataset from the hub.
if model_args.train_language is None:
train_dataset = load_dataset("xnli", model_args.language, split="train")
else:
train_dataset = load_dataset("xnli", model_args.train_language, split="train")
if training_args.do_train:
if model_args.train_language is None:
train_dataset = load_dataset("xnli", model_args.language, split="train")
else:
train_dataset = load_dataset("xnli", model_args.train_language, split="train")
label_list = train_dataset.features["label"].names
if training_args.do_eval:
eval_dataset = load_dataset("xnli", model_args.language, split="validation")
label_list = eval_dataset.features["label"].names
if training_args.do_predict:
test_dataset = load_dataset("xnli", model_args.language, split="test")
label_list = test_dataset.features["label"].names
eval_dataset = load_dataset("xnli", model_args.language, split="validation")
# Labels
label_list = train_dataset.features["label"].names
num_labels = len(label_list)
# Load pretrained model and tokenizer
@ -271,6 +279,9 @@ def main():
batched=True,
load_from_cache_file=not data_args.overwrite_cache,
)
# Log a few random samples from the training set:
for index in random.sample(range(len(train_dataset)), 3):
logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
if training_args.do_eval:
if data_args.max_val_samples is not None:
@ -281,9 +292,14 @@ def main():
load_from_cache_file=not data_args.overwrite_cache,
)
# Log a few random samples from the training set:
for index in random.sample(range(len(train_dataset)), 3):
logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
if training_args.do_predict:
if data_args.max_test_samples is not None:
test_dataset = test_dataset.select(range(data_args.max_test_samples))
test_dataset = test_dataset.map(
preprocess_function,
batched=True,
load_from_cache_file=not data_args.overwrite_cache,
)
# Get the metric function
metric = load_metric("xnli")
@ -307,7 +323,7 @@ def main():
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
train_dataset=train_dataset if training_args.do_train else None,
eval_dataset=eval_dataset if training_args.do_eval else None,
compute_metrics=compute_metrics,
tokenizer=tokenizer,
@ -346,6 +362,26 @@ def main():
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
# Prediction
if training_args.do_predict:
logger.info("*** Predict ***")
predictions, labels, metrics = trainer.predict(test_dataset)
max_test_samples = data_args.max_test_samples if data_args.max_test_samples is not None else len(test_dataset)
metrics["test_samples"] = min(max_test_samples, len(test_dataset))
trainer.log_metrics("test", metrics)
trainer.save_metrics("test", metrics)
predictions = np.argmax(predictions, axis=1)
output_test_file = os.path.join(training_args.output_dir, "test_predictions.txt")
if trainer.is_world_process_zero():
with open(output_test_file, "w") as writer:
writer.write("index\tprediction\n")
for index, item in enumerate(predictions):
item = label_list[item]
writer.write(f"{index}\t{item}\n")
if __name__ == "__main__":
main()

61
templates/adding_a_new_example_script/{{cookiecutter.directory_name}}/run_{{cookiecutter.example_shortcut}}.py
View File

@ -139,6 +139,10 @@ class DataTrainingArguments:
default=None,
metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
)
test_file: Optional[str] = field(
default=None,
metadata={"help": "An optional input test data file to predict the label on (a text file)."},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
)
@ -160,10 +164,22 @@ class DataTrainingArguments:
"value if set."
},
)
max_test_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of test examples to this "
"value if set."
},
)
def __post_init__(self):
if self.dataset_name is None and self.train_file is None and self.validation_file is None:
raise ValueError("Need either a dataset name or a training/validation file.")
if (
self.dataset_name is None
and self.train_file is None
and self.validation_file is None
and self.test_file is None
):
raise ValueError("Need either a dataset name or a training/validation/test file.")
else:
if self.train_file is not None:
extension = self.train_file.split(".")[-1]
@ -171,6 +187,9 @@ class DataTrainingArguments:
if self.validation_file is not None:
extension = self.validation_file.split(".")[-1]
assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
if self.test_file is not None:
extension = self.test_file.split(".")[-1]
assert extension in ["csv", "json", "txt"], "`test_file` should be a csv, a json or a txt file."
def main():
@ -238,9 +257,13 @@ def main():
data_files = {}
if data_args.train_file is not None:
data_files["train"] = data_args.train_file
extension = data_args.train_file.split(".")[-1]
if data_args.validation_file is not None:
data_files["validation"] = data_args.validation_file
extension = data_args.train_file.split(".")[-1]
extension = data_args.validation_file.split(".")[-1]
if data_args.test_file is not None:
data_files["test"] = data_args.test_file
extension = data_args.test_file.split(".")[-1]
if extension == "txt":
extension = "text"
datasets = load_dataset(extension, data_files=data_files)
@ -326,8 +349,10 @@ def main():
# First we tokenize all the texts.
if training_args.do_train:
column_names = datasets["train"].column_names
else:
elif training_args.do_eval:
column_names = datasets["validation"].column_names
elif training_args.do_predict:
column_names = datasets["test"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
@ -365,6 +390,22 @@ def main():
load_from_cache_file=not data_args.overwrite_cache,
)
if training_args.do_predict:
if "test" not in datasets:
raise ValueError("--do_predict requires a test dataset")
test_dataset = datasets["test"]
# Selecting samples from dataset
if data_args.max_test_samples is not None:
test_dataset = test_dataset.select(range(data_args.max_test_samples))
# tokenize test dataset
test_dataset = test_dataset.map(
tokenize_function,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=[text_column_name],
load_from_cache_file=not data_args.overwrite_cache,
)
# Data collator
data_collator=default_data_collator if not training_args.fp16 else DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
@ -420,6 +461,18 @@ def main():
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
# Prediction
if training_args.do_predict:
logger.info("*** Predict ***")
predictions, labels, metrics = trainer.predict(test_dataset)
max_test_samples = data_args.max_test_samples if data_args.max_test_samples is not None else len(test_dataset)
metrics["test_samples"] = min(max_test_samples, len(test_dataset))
trainer.log_metrics("test", metrics)
trainer.save_metrics("test", metrics)
# write custom code for saving predictions according to task
def _mp_fn(index):
# For xla_spawn (TPUs)