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Create mcqa_dataset.py
Browse files- mcqa_dataset.py +55 -0
mcqa_dataset.py
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# mcqa_dataset.py
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# --------------------------------------------------
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# Pre‑tokenised dataset for 4‑choice MCQA
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# --------------------------------------------------
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import json
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import torch
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from torch.utils.data import Dataset
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class MCQADataset(Dataset):
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"""
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Each item returns:
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input_ids, attention_mask : LongTensor (max_len)
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label : 0/1 (1 → correct choice)
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qid, cid : strings (question id, choice id)
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"""
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def __init__(self, path: str, tokenizer, max_len: int = 128):
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self.encodings, self.labels, self.qids, self.cids = [], [], [], []
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with open(path, encoding="utf-8") as f:
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for line in f:
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obj = json.loads(line)
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stem = obj["question"]["stem"]
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fact = obj["fact1"]
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gold = obj["answerKey"]
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for ch in obj["question"]["choices"]:
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text = f"{fact} {stem} {ch['text']}"
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enc = tokenizer(
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text,
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max_length=max_len,
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truncation=True,
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padding="max_length",
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)
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self.encodings.append(enc)
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self.labels.append(1 if ch["label"] == gold else 0)
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self.qids.append(obj["id"])
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self.cids.append(ch["label"])
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# Convert lists of dicts → dict of lists for cheaper indexing
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self.encodings = {
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k: [d[k] for d in self.encodings] for k in self.encodings[0]
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}
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# --------------------------------------------------
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def __len__(self):
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return len(self.labels)
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def __getitem__(self, idx):
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item = {k: torch.tensor(v[idx]) for k, v in self.encodings.items()}
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item["label"] = torch.tensor(self.labels[idx], dtype=torch.long)
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item["qid"] = self.qids[idx]
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item["cid"] = self.cids[idx]
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return item
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