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Refactor load_data function for readability and efficiency #14

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Refactor load_data function for readability and efficiency #14

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25 changes: 11 additions & 14 deletions evaluation/data_loader.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,7 @@ def load_data(data_name, split, data_dir="./data"):
if os.path.exists(data_file):
examples = list(load_jsonl(data_file))
else:
dataset = None
if data_name == "math":
dataset = load_dataset(
"competition_math",
Expand All @@ -21,7 +22,6 @@ def load_data(data_name, split, data_dir="./data"):
elif data_name == "gsm8k":
dataset = load_dataset(data_name, split=split)
elif data_name == "svamp":
# evaluate on training set + test set
dataset = load_dataset("ChilleD/SVAMP", split="train")
dataset = concatenate_datasets(
[dataset, load_dataset("ChilleD/SVAMP", split="test")]
Expand All @@ -33,16 +33,14 @@ def load_data(data_name, split, data_dir="./data"):
) # remove multi-answer examples
elif data_name == "mawps":
examples = []
# four sub-tasks
for data_name in ["singleeq", "singleop", "addsub", "multiarith"]:
sub_examples = list(load_jsonl(f"{data_dir}/mawps/{data_name}.jsonl"))
for sub_task in ["singleeq", "singleop", "addsub", "multiarith"]:
sub_examples = list(load_jsonl(f"{data_dir}/mawps/{sub_task}.jsonl"))
for example in sub_examples:
example["type"] = data_name
example["type"] = sub_task
examples.extend(sub_examples)
dataset = Dataset.from_list(examples)
elif data_name == "mmlu_stem":
dataset = load_dataset("hails/mmlu_no_train", "all", split="test")
# only keep stem subjects
stem_subjects = [
"abstract_algebra",
"astronomy",
Expand All @@ -66,20 +64,19 @@ def load_data(data_name, split, data_dir="./data"):
dataset = dataset.rename_column("subject", "type")
dataset = dataset.filter(lambda x: x["type"] in stem_subjects)
elif data_name == "carp_en":
dataset = load_jsonl(f"{data_dir}/carp_en/test.jsonl")
dataset = Dataset.from_list(load_jsonl(f"{data_dir}/carp_en/test.jsonl"))
else:
raise NotImplementedError(data_name)

examples = list(dataset)
examples = [lower_keys(example) for example in examples]
dataset = Dataset.from_list(examples)
os.makedirs(f"{data_dir}/{data_name}", exist_ok=True)
dataset.to_json(data_file)
if dataset is not None:
examples = [lower_keys(example) for example in dataset]
os.makedirs(f"{data_dir}/{data_name}", exist_ok=True)
dataset.to_json(data_file)

# add 'idx' in the first column
if "idx" not in examples[0]:
examples = [{"idx": i, **example} for i, example in enumerate(examples)]

# dedepulicate & sort
examples = sorted(examples, key=lambda x: x["idx"])

return examples