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76 lines
2.8 KiB
76 lines
2.8 KiB
import copy
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import csv
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import os
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from typing import Dict, List
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from colossalai.logging import DistributedLogger
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from .base import BaseDataset
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default_inference_kwargs = {
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"calculate_loss": True,
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"all_classes": ["A", "B", "C", "D"],
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"language": "English",
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"pretrain": False,
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"max_new_tokens": 32,
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}
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def get_few_shot_data(data: List[Dict], subject):
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few_shot_data = [f"The following are multiple choice questions (with answers) about {subject}."]
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for i in data:
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few_shot_data.append(i["input"] + i["target"])
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return few_shot_data
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class MMLUDataset(BaseDataset):
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"""
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Dataset class for MMLU dataset.
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Data source: https://github.com/hendrycks/test
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This dataset class will convert the original dataset into the inference dataset.
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"""
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@staticmethod
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def load(
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path: str, logger: DistributedLogger, few_shot: bool, forward_only: bool, load_train: bool, load_reference: bool
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) -> List[Dict]:
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dataset = {"dev": {}, "test": {}}
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for split in ["dev", "test"]:
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files = os.listdir(os.path.join(path, split))
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files.sort()
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for file in files:
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subject = file[0 : -len(f"_{split}.csv")].split("_")
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subject = " ".join([word.title() if word != "us" else "US" for word in subject])
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file_dir = os.path.join(path, split, file)
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dataset[split][subject] = {"data": [], "inference_kwargs": {}}
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# It's been tested that each data sample in one subcategory have same inference arguments.
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dataset[split][subject]["inference_kwargs"] = copy.deepcopy(default_inference_kwargs)
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if split == "test" and few_shot:
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dataset[split][subject]["inference_kwargs"]["few_shot_data"] = get_few_shot_data(
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dataset["dev"][subject]["data"], subject
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)
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with open(file_dir, encoding="utf-8") as f:
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reader = csv.reader(f)
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for row in reader:
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assert len(row) == 6
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choices = f"A. {row[1]}\nB. {row[2]}\nC. {row[3]}\nD. {row[4]}"
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data_sample = {
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"dataset": "mmlu",
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"split": split,
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"category": subject,
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"instruction": f"The following is a single-choice question on {subject}. Answer the question by replying A, B, C or D.",
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"input": f"Question: {row[0]}\n{choices}\nAnswer: ",
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"output": "",
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"target": row[5],
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}
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dataset[split][subject]["data"].append(data_sample)
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return dataset
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