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Update run_glue for do_predict with local test data (#9442) #9486

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merged 5 commits into from
Jan 13, 2021
Merged

Update run_glue for do_predict with local test data (#9442) #9486

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dba9600
Update run_glue for do_predict with local test data (#9442)
forest1988 Jan 8, 2021
a77675e
Update run_glue (#9442): fix comments ('files' to 'a file')
forest1988 Jan 8, 2021
b1e10ac
Update run_glue (#9442): reflect the code review
forest1988 Jan 9, 2021
b2936c3
Update run_glue (#9442): auto format
forest1988 Jan 9, 2021
37531ab
Update run_glue (#9442): reflect the code review
forest1988 Jan 11, 2021
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20 changes: 20 additions & 0 deletions examples/text-classification/run_glue.py
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Original file line number Diff line number Diff line change
Expand Up @@ -93,6 +93,7 @@ class DataTrainingArguments:
validation_file: Optional[str] = field(
default=None, metadata={"help": "A csv or a json file containing the validation data."}
)
test_file: Optional[str] = field(default=None, metadata={"help": "A csv or a json file containing the test data."})

def __post_init__(self):
if self.task_name is not None:
Expand Down Expand Up @@ -413,6 +414,25 @@ def compute_metrics(p: EvalPrediction):
if training_args.do_predict:
logger.info("*** Test ***")

# Get the datasets: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.

if data_args.task_name is None and data_args.test_file is not None:
extension = data_args.test_file.split(".")[-1]
assert extension in ["csv", "json"], "`test_file` should be a csv or a json file."
if data_args.test_file.endswith(".csv"):
# Loading a dataset from a local csv file
test_dataset = load_dataset("csv", data_files={"test": data_args.test_file})
else:
# Loading a dataset from a local json file
test_dataset = load_dataset("json", data_files={"test": data_args.test_file})
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Can we put those lines earlier, with the validation dataset? This way the map will be done with the other dataset. I think we can do something nice by creating data_files={"train": data_args.train_file, "validation": data_args.validation_file} and then adding the keys test if the test_file is passed.

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Thank you for your comment! I've reflected the review.
The nested if statements in the code have increased, but I think the readability may have improved in terms of "whether to use GLUE task or to use local files". What do you think?

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I also added the logger output to make sure that the local files a user wants to use are loaded correctly. If this is superfluous, please let me know and I will remove it.

test_dataset = test_dataset.map(
preprocess_function, batched=True, load_from_cache_file=not data_args.overwrite_cache
)
test_dataset = test_dataset["test"]
else:
raise ValueError("Need either a GLUE task or a test file for `do_predict`.")

# Loop to handle MNLI double evaluation (matched, mis-matched)
tasks = [data_args.task_name]
test_datasets = [test_dataset]
Expand Down