Datasets:
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| # coding=utf-8 | |
| # Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import json | |
| import os | |
| import re | |
| import zipfile | |
| from pathlib import Path | |
| from typing import Dict, List, Tuple | |
| import datasets | |
| from seacrowd.utils import schemas | |
| from seacrowd.utils.configs import SEACrowdConfig | |
| from seacrowd.utils.constants import Licenses, Tasks | |
| _CITATION = """\ | |
| @article{zhang2023m3exam, | |
| title={M3Exam: A Multilingual, Multimodal, Multilevel Benchmark for Examining Large Language Models}, | |
| author={Wenxuan Zhang and Sharifah Mahani Aljunied and Chang Gao and Yew Ken Chia and Lidong Bing}, | |
| year={2023}, | |
| eprint={2306.05179}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CL} | |
| } | |
| """ | |
| _DATASETNAME = "m3exam" | |
| _DESCRIPTION = """\ | |
| M3Exam is a novel benchmark sourced from real and official human exam questions for evaluating LLMs\ | |
| in a multilingual, multimodal, and multilevel context. In total, M3Exam contains 12,317 questions in 9\ | |
| diverse languages with three educational levels, where about 23% of the questions require processing images\ | |
| for successful solving. M3Exam dataset covers 3 languages spoken in Southeast Asia. | |
| """ | |
| _HOMEPAGE = "https://github.com/DAMO-NLP-SG/M3Exam" | |
| _LANGUAGES = ["jav", "tha", "vie"] | |
| _LANG_MAPPER = {"jav": "javanese", "tha": "thai", "vie": "vietnamese"} | |
| _LICENSE = Licenses.CC_BY_NC_SA_4_0.value | |
| _LOCAL = False | |
| _PASSWORD = "12317".encode("utf-8") # password to unzip dataset after downloading | |
| _URLS = { | |
| _DATASETNAME: "https://drive.usercontent.google.com/download?id=1eREETRklmXJLXrNPTyHxQ3RFdPhq_Nes&authuser=0&confirm=t", | |
| } | |
| _SUPPORTED_TASKS = [Tasks.QUESTION_ANSWERING, Tasks.VISUAL_QUESTION_ANSWERING] | |
| _SOURCE_VERSION = "1.0.0" | |
| _SEACROWD_VERSION = "2024.06.20" | |
| class M3ExamDataset(datasets.GeneratorBasedBuilder): | |
| """ | |
| M3Exam is a novel benchmark sourced from real and official human exam questions for evaluating LLMs | |
| in a multilingual, multimodal, and multilevel context. In total, M3Exam contains 12,317 questions in 9 | |
| diverse languages with three educational levels, where about 23% of the questions require processing images | |
| for successful solving. M3Exam dataset covers 3 languages spoken in Southeast Asia. | |
| """ | |
| SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) | |
| SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) | |
| BUILDER_CONFIGS = ( | |
| [SEACrowdConfig(name=f"{_DATASETNAME}_{lang}_source", version=datasets.Version(_SOURCE_VERSION), description=f"{_DATASETNAME} source schema", schema="source", subset_id=f"{_DATASETNAME}") for lang in _LANGUAGES] | |
| + [ | |
| SEACrowdConfig( | |
| name=f"{_DATASETNAME}_{lang}_seacrowd_qa", | |
| version=datasets.Version(_SEACROWD_VERSION), | |
| description=f"{_DATASETNAME} SEACrowd schema", | |
| schema="seacrowd_qa", | |
| subset_id=f"{_DATASETNAME}", | |
| ) | |
| for lang in _LANGUAGES | |
| ] | |
| + [ | |
| SEACrowdConfig( | |
| name=f"{_DATASETNAME}_{lang}_seacrowd_imqa", | |
| version=datasets.Version(_SEACROWD_VERSION), | |
| description=f"{_DATASETNAME} SEACrowd schema", | |
| schema="seacrowd_imqa", | |
| subset_id=f"{_DATASETNAME}", | |
| ) | |
| for lang in _LANGUAGES | |
| ] | |
| ) | |
| DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_jav_source" | |
| def _info(self) -> datasets.DatasetInfo: | |
| if self.config.schema == "source": | |
| features = datasets.Features( | |
| { | |
| "question_text": datasets.Value("string"), | |
| "background_description": datasets.Sequence(datasets.Value("string")), | |
| "answer_text": datasets.Value("string"), | |
| "options": datasets.Sequence(datasets.Value("string")), | |
| "language": datasets.Value("string"), | |
| "level": datasets.Value("string"), | |
| "subject": datasets.Value("string"), | |
| "subject_category": datasets.Value("string"), | |
| "year": datasets.Value("string"), | |
| "need_image": datasets.Value("string"), | |
| "image_paths": datasets.Sequence(datasets.Value("string")), | |
| } | |
| ) | |
| elif self.config.schema == "seacrowd_qa": | |
| features = schemas.qa_features | |
| features["meta"] = { | |
| "background_description": datasets.Sequence(datasets.Value("string")), | |
| "level": datasets.Value("string"), | |
| "subject": datasets.Value("string"), | |
| "subject_category": datasets.Value("string"), | |
| "year": datasets.Value("string"), | |
| } | |
| elif self.config.schema == "seacrowd_imqa": | |
| features = schemas.imqa_features | |
| features["meta"] = { | |
| "background_description": datasets.Sequence(datasets.Value("string")), | |
| "level": datasets.Value("string"), | |
| "subject": datasets.Value("string"), | |
| "subject_category": datasets.Value("string"), | |
| "year": datasets.Value("string"), | |
| } | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=features, | |
| homepage=_HOMEPAGE, | |
| license=_LICENSE, | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: | |
| """Returns SplitGenerators.""" | |
| urls = _URLS[_DATASETNAME] | |
| lang = self.config.name.split("_")[1] | |
| data_dir = dl_manager.download(urls) | |
| if not os.path.exists(data_dir + "_extracted"): | |
| if not os.path.exists(data_dir + ".zip"): | |
| os.rename(data_dir, data_dir + ".zip") | |
| with zipfile.ZipFile(data_dir + ".zip", "r") as zip_ref: | |
| zip_ref.extractall(data_dir + "_extracted", pwd=_PASSWORD) # unzipping with password | |
| if not os.path.exists(data_dir): | |
| os.rename(data_dir + ".zip", data_dir) | |
| image_generator = [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "filepath": os.path.join(data_dir + "_extracted", "data/multimodal-question"), | |
| "split": "train", | |
| }, | |
| ), | |
| ] | |
| text_generator = [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={ | |
| "filepath": os.path.join(data_dir + "_extracted", f"data/text-question/{_LANG_MAPPER[lang]}-questions-test.json"), | |
| "split": "test", | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={ | |
| "filepath": os.path.join(data_dir + "_extracted", f"data/text-question/{_LANG_MAPPER[lang]}-questions-dev.json"), | |
| "split": "dev", | |
| }, | |
| ), | |
| ] | |
| if "imqa" in self.config.name: | |
| return image_generator | |
| else: | |
| if "source" in self.config.name: | |
| image_generator.extend(text_generator) | |
| return image_generator | |
| else: | |
| return text_generator | |
| def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]: | |
| """Yields examples as (key, example) tuples.""" | |
| lang = self.config.name.split("_")[1] | |
| thai_answer_mapper = {"1": "1", "2": "2", "3": "3", "4": "4", "5": "5", "๑": "1", "๒": "2", "๓": "3", "๔": "4", "๕": "5"} | |
| if self.config.schema == "source": | |
| if split == "train": | |
| filepath_json = os.path.join(filepath, f"{_LANG_MAPPER[lang]}-questions-image.json") | |
| with open(filepath_json, "r") as file: | |
| data = json.load(file) | |
| idx = 0 | |
| for json_obj in data: | |
| image_paths = [] | |
| for text in [json_obj["question_text"]] + json_obj["options"] + json_obj["background_description"]: | |
| matches = re.findall(r"\[image-(\d+)\.(jpg|png)\]", text) | |
| if matches: | |
| image_path = [os.path.join(filepath, f"images-{_LANG_MAPPER[lang]}/image-{image_number[0]}.{image_number[1]}") for image_number in matches] | |
| image_paths.extend(image_path) | |
| example = { | |
| "question_text": json_obj["question_text"], | |
| "background_description": json_obj["background_description"] if "background_description" in json_obj.keys() else None, | |
| "answer_text": json_obj["answer_text"], | |
| "options": json_obj["options"], | |
| "language": json_obj["language"] if "language" in json_obj.keys() else None, | |
| "level": json_obj["level"] if "level" in json_obj.keys() else None, | |
| "subject": json_obj["subject"] if "subject" in json_obj.keys() else None, | |
| "subject_category": json_obj["subject_category"] if "subject_category" in json_obj.keys() else None, | |
| "year": json_obj["year"] if "year" in json_obj.keys() else None, | |
| "need_image": "yes", | |
| "image_paths": image_paths, | |
| } | |
| yield idx, example | |
| idx += 1 | |
| else: | |
| with open(filepath, "r") as file: | |
| data = json.load(file) | |
| idx = 0 | |
| for json_obj in data: | |
| example = { | |
| "question_text": json_obj["question_text"], | |
| "background_description": json_obj["background_description"] if "background_description" in json_obj.keys() else None, | |
| "answer_text": json_obj["answer_text"], | |
| "options": json_obj["options"], | |
| "language": json_obj["language"] if "language" in json_obj.keys() else None, | |
| "level": json_obj["level"] if "level" in json_obj.keys() else None, | |
| "subject": json_obj["subject"] if "subject" in json_obj.keys() else None, | |
| "subject_category": json_obj["subject_category"] if "subject_category" in json_obj.keys() else None, | |
| "year": json_obj["year"] if "year" in json_obj.keys() else None, | |
| "need_image": "no", | |
| "image_paths": None, | |
| } | |
| yield idx, example | |
| idx += 1 | |
| elif self.config.schema == "seacrowd_qa": | |
| with open(filepath, "r") as file: | |
| data = json.load(file) | |
| idx = 0 | |
| for json_obj in data: | |
| answer = [".".join(answer.split(".")[1:]).strip() for answer in json_obj["options"] if json_obj["answer_text"] == answer.split(".")[0]] | |
| if "_tha_" in self.config.name and len(answer) == 0: | |
| answer = [".".join(answer.split(".")[1:]).strip() for answer in json_obj["options"] if thai_answer_mapper[json_obj["answer_text"]] == thai_answer_mapper[answer.split(".")[0]]] | |
| example = { | |
| "id": idx, | |
| "question_id": idx, | |
| "document_id": idx, | |
| "question": json_obj["question_text"], | |
| "type": "multiple_choice", | |
| "choices": [".".join(answer.split(".")[1:]).strip() for answer in json_obj["options"]], | |
| "context": "", | |
| "answer": answer, | |
| "meta": { | |
| "background_description": json_obj["background_description"] if "background_description" in json_obj.keys() else None, | |
| "level": json_obj["level"] if "level" in json_obj.keys() else None, | |
| "subject": json_obj["subject"] if "subject" in json_obj.keys() else None, | |
| "subject_category": json_obj["subject_category"] if "subject_category" in json_obj.keys() else None, | |
| "year": json_obj["year"] if "year" in json_obj.keys() else None, | |
| }, | |
| } | |
| yield idx, example | |
| idx += 1 | |
| elif self.config.schema == "seacrowd_imqa": | |
| filepath_json = os.path.join(filepath, f"{_LANG_MAPPER[lang]}-questions-image.json") | |
| with open(filepath_json, "r") as file: | |
| data = json.load(file) | |
| idx = 0 | |
| for json_obj in data: | |
| answer = [".".join(answer.split(".")[1:]).strip() for answer in json_obj["options"] if json_obj["answer_text"] == answer.split(".")[0]] | |
| if "_tha_" in self.config.name and len(answer) == 0: | |
| answer = [".".join(answer.split(".")[1:]).strip() for answer in json_obj["options"] if thai_answer_mapper[json_obj["answer_text"]] == thai_answer_mapper[answer.split(".")[0]]] | |
| image_paths = [] | |
| for text in [json_obj["question_text"]] + json_obj["options"] + json_obj["background_description"]: | |
| matches = re.findall(r"\[image-(\d+)\.(jpg|png)\]", text) | |
| if matches: | |
| image_path = [os.path.join(filepath, f"images-{_LANG_MAPPER[lang]}/image-{image_number[0]}.{image_number[1]}") for image_number in matches] | |
| image_paths.extend(image_path) | |
| example = { | |
| "id": idx, | |
| "question_id": idx, | |
| "document_id": idx, | |
| "questions": [json_obj["question_text"]], | |
| "type": "multiple_choice", | |
| "choices": [".".join(answer.split(".")[1:]).strip() for answer in json_obj["options"]], | |
| "context": "", | |
| "answer": answer, | |
| "image_paths": image_paths, | |
| "meta": { | |
| "background_description": json_obj["background_description"] if "background_description" in json_obj.keys() else None, | |
| "level": json_obj["level"] if "level" in json_obj.keys() else None, | |
| "subject": json_obj["subject"] if "subject" in json_obj.keys() else None, | |
| "subject_category": json_obj["subject_category"] if "subject_category" in json_obj.keys() else None, | |
| "year": json_obj["year"] if "year" in json_obj.keys() else None, | |
| }, | |
| } | |
| yield idx, example | |
| idx += 1 | |