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30 lines
942 B
Python
30 lines
942 B
Python
from dataclasses import dataclass
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from typing import Any, Dict, Sequence
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import torch
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from transformers import DataCollatorWithPadding
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@dataclass
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class PairwiseDataCollatorWithPadding(DataCollatorWithPadding):
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r"""
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Data collator for pairwise data.
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"""
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def __call__(self, features: Sequence[Dict[str, Any]]) -> Dict[str, torch.Tensor]:
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r"""
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Pads batched data to the longest sequence in the batch.
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We generate 2 * n examples where the first n examples represent chosen examples and
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the last n examples represent rejected examples.
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"""
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features = [
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{
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"input_ids": feature["prompt_ids"] + feature[key],
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"attention_mask": [1] * (len(feature["prompt_ids"]) + len(feature[key])),
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}
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for key in ("chosen_ids", "rejected_ids")
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for feature in features
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]
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return super().__call__(features)
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