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import json |
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import os |
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import datasets |
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import ast |
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_DESCRIPTION = """\ |
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The M-QALM Dataset Repository contains Multiple-Choice and Abstractive Questions for evaluating the performance of LLMs in the clinical and biomedical domain. |
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""" |
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_HOMEPAGE = "https://huggingface.co/datasets/anand-s/m_qalm" |
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_LICENSE = "Apache License 2.0" |
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_URLS = { |
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"train_normal_mcqa": "https://huggingface.co/datasets/anand-s/m_qalm/resolve/main/train_normal_mcqa.zip", |
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"val_normal_mcqa": "https://huggingface.co/datasets/anand-s/m_qalm/resolve/main/val_normal_mcqa.zip", |
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"test_normal_mcqa": "https://huggingface.co/datasets/anand-s/m_qalm/resolve/main/test_normal_mcqa.zip", |
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"train_context_mcqa": "https://huggingface.co/datasets/anand-s/m_qalm/resolve/main/train_context_mcqa.zip", |
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"val_context_mcqa": "https://huggingface.co/datasets/anand-s/m_qalm/resolve/main/val_context_mcqa.zip", |
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"test_context_mcqa": "https://huggingface.co/datasets/anand-s/m_qalm/resolve/main/test_context_mcqa.zip", |
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"train_aqa": "https://huggingface.co/datasets/anand-s/m_qalm/resolve/main/train_aqa.zip", |
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"val_aqa": "https://huggingface.co/datasets/anand-s/m_qalm/resolve/main/val_aqa.zip", |
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"test_aqa": "https://huggingface.co/datasets/anand-s/m_qalm/resolve/main/test_aqa.zip", |
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} |
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class MQalm(datasets.GeneratorBasedBuilder): |
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"""Dataset for multiple choice questions from Test Repo.""" |
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VERSION = datasets.Version("1.0.0") |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig(name="train_normal_mcqa", version=VERSION, description="Train set MCQA"), |
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datasets.BuilderConfig(name="val_normal_mcqa", version=VERSION, description="Val set MCQA"), |
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datasets.BuilderConfig(name="test_normal_mcqa", version=VERSION, description="Test set MCQA"), |
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datasets.BuilderConfig(name="train_context_mcqa", version=VERSION, description="Train set context MCQA"), |
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datasets.BuilderConfig(name="val_context_mcqa", version=VERSION, description="Val set context MCQA"), |
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datasets.BuilderConfig(name="test_context_mcqa", version=VERSION, description="Test set context MCQA"), |
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datasets.BuilderConfig(name="train_aqa", version=VERSION, description="Train set AQA"), |
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datasets.BuilderConfig(name="val_aqa", version=VERSION, description="Val set AQA"), |
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datasets.BuilderConfig(name="test_aqa", version=VERSION, description="Test set AQA"), |
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] |
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def _info(self): |
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features_dict = { |
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"train_normal_mcqa": datasets.Features({ |
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"prompt": datasets.Value("string"), |
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"question": datasets.Value("string"), |
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"options": datasets.Sequence(datasets.Value("string")), |
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"answer": datasets.Value("string"), |
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"num_options": datasets.Value("string"), |
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"question_type": datasets.Value("string"), |
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"dataset_name": datasets.Value("string"), |
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}), |
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"val_normal_mcqa": datasets.Features({ |
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"prompt": datasets.Value("string"), |
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"question": datasets.Value("string"), |
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"options": datasets.Sequence(datasets.Value("string")), |
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"answer": datasets.Value("string"), |
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"num_options": datasets.Value("string"), |
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"question_type": datasets.Value("string"), |
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"dataset_name": datasets.Value("string"), |
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"few_shot_prompt": datasets.Sequence(datasets.Features({ |
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"question": datasets.Value("string"), |
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"answer": datasets.Value("string"), |
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"options": datasets.Sequence(datasets.Value("string")), |
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})), |
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}), |
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"test_normal_mcqa": datasets.Features({ |
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"prompt": datasets.Value("string"), |
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"question": datasets.Value("string"), |
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"options": datasets.Sequence(datasets.Value("string")), |
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"answer": datasets.Value("string"), |
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"num_options": datasets.Value("string"), |
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"question_type": datasets.Value("string"), |
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"dataset_name": datasets.Value("string"), |
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"few_shot_prompt": datasets.Sequence(datasets.Features({ |
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"question": datasets.Value("string"), |
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"answer": datasets.Value("string"), |
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"options": datasets.Sequence(datasets.Value("string")), |
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})), |
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}), |
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"train_context_mcqa": datasets.Features({ |
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"prompt": datasets.Value("string"), |
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"question": datasets.Value("string"), |
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"options": datasets.Sequence(datasets.Value("string")), |
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"answer": datasets.Value("string"), |
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"num_options": datasets.Value("string"), |
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"question_type": datasets.Value("string"), |
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"dataset_name": datasets.Value("string"), |
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"context": datasets.Sequence(datasets.Value("string")) |
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}), |
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"val_context_mcqa": datasets.Features({ |
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"prompt": datasets.Value("string"), |
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"question": datasets.Value("string"), |
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"options": datasets.Sequence(datasets.Value("string")), |
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"answer": datasets.Value("string"), |
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"num_options": datasets.Value("string"), |
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"question_type": datasets.Value("string"), |
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"dataset_name": datasets.Value("string"), |
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"context": datasets.Sequence(datasets.Value("string")), |
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"few_shot_prompt": [{ |
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"question": datasets.Value("string"), |
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"answer": datasets.Value("string"), |
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"options": datasets.Sequence(datasets.Value("string")), |
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"context": datasets.Sequence(datasets.Value("string")), |
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}], |
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}), |
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"test_context_mcqa": datasets.Features({ |
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"prompt": datasets.Value("string"), |
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"question": datasets.Value("string"), |
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"options": datasets.Sequence(datasets.Value("string")), |
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"answer": datasets.Value("string"), |
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"num_options": datasets.Value("string"), |
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"question_type": datasets.Value("string"), |
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"dataset_name": datasets.Value("string"), |
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"context": datasets.Sequence(datasets.Value("string")), |
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"few_shot_prompt": datasets.Sequence(datasets.Features({ |
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"question": datasets.Value("string"), |
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"answer": datasets.Value("string"), |
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"options": datasets.Sequence(datasets.Value("string")), |
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"context": datasets.Sequence(datasets.Value("string")), |
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})), |
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}), |
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"train_aqa": datasets.Features({ |
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"question": datasets.Value("string"), |
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"answer": datasets.Value("string"), |
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"prompt": datasets.Value("string"), |
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"dataset_name": datasets.Value("string"), |
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}), |
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"val_aqa": datasets.Features({ |
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"question": datasets.Value("string"), |
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"answer": datasets.Sequence(datasets.Value("string")), |
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"prompt": datasets.Value("string"), |
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"dataset_name": datasets.Value("string"), |
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}), |
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"test_aqa": datasets.Features({ |
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"question": datasets.Value("string"), |
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"answer": datasets.Sequence(datasets.Value("string")), |
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"prompt": datasets.Value("string"), |
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"dataset_name": datasets.Value("string"), |
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}), |
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} |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features_dict[self.config.name], |
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homepage=_HOMEPAGE, |
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license=_LICENSE |
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) |
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def _split_generators(self, dl_manager): |
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data_dir = dl_manager.download_and_extract(_URLS[self.config.name]) |
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return [ |
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datasets.SplitGenerator( |
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name=self.config.name, |
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gen_kwargs={"directory": data_dir, "split": self.config.name}, |
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) |
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] |
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def _generate_examples(self, directory, split): |
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key_idx = 0 |
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for filename in os.listdir(directory): |
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filepath = os.path.join(directory, filename) |
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if filepath.endswith(".jsonl"): |
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with open(filepath, encoding="utf-8") as f: |
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for key, row in enumerate(f): |
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data = json.loads(row) |
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if split == "train_normal_mcqa": |
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yield key_idx, { |
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"prompt": data["prompt"], |
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"question": data["question"], |
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"options": data["options"], |
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"answer": data["answer"], |
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"num_options": data["num_options"], |
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"question_type": data["question_type"], |
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"dataset_name": os.path.split(filepath)[-1].replace(".jsonl","") |
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} |
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key_idx +=1 |
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elif split in ["val_normal_mcqa", "test_normal_mcqa"]: |
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yield key_idx, { |
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"prompt": data["prompt"], |
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"question": data["question"], |
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"options": data["options"], |
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"answer": data["answer"], |
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"num_options": data["num_options"], |
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"question_type": data["question_type"], |
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"dataset_name": os.path.split(filepath)[-1].replace(".jsonl",""), |
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"few_shot_prompt": [{ |
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"question": item["question"], |
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"answer": item["answer"], |
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"options": item["options"], |
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} for item in data["few_shot_prompt"]] |
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} |
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key_idx +=1 |
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elif split == "train_context_mcqa": |
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yield key_idx, { |
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"prompt": data["prompt"], |
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"question": data["question"], |
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"options": data["options"], |
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"answer": data["answer"], |
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"num_options": data["num_options"], |
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"context": data["context"], |
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"question_type": data["question_type"], |
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"dataset_name": os.path.split(filepath)[-1].replace(".jsonl","") |
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} |
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key_idx +=1 |
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elif split in ["val_context_mcqa", "test_context_mcqa"]: |
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yield key_idx, { |
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"prompt": data["prompt"], |
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"question": data["question"], |
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"options": data["options"], |
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"answer": data["answer"], |
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"num_options": data["num_options"], |
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"context": data["context"], |
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"question_type": data["question_type"], |
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"dataset_name": os.path.split(filepath)[-1].replace(".jsonl",""), |
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"few_shot_prompt": data["few_shot_prompt"]} |
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key_idx +=1 |
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elif split in ["train_aqa", "val_aqa", "test_aqa"]: |
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yield key_idx, { |
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"prompt": data["prompt"], |
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"question": data["question"], |
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"answer": data["answer"], |
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"dataset_name": os.path.split(filepath)[-1].replace(".jsonl","") |
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} |
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key_idx +=1 |
