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681909f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | import json
from pathlib import Path
import torch
from torch.utils.data import Dataset
from .formatting import format_example
class DialogueDataset(Dataset):
def __init__(self, path: str, tokenizer, max_seq_len: int):
self.path = Path(path)
self.tokenizer = tokenizer
self.max_seq_len = max_seq_len
self.assistant_id = tokenizer.piece_to_id("<assistant>")
self.examples = []
with self.path.open("r", encoding="utf-8") as f:
for line in f:
line = line.strip()
if not line:
continue
self.examples.append(json.loads(line))
if not self.examples:
raise ValueError(f"No examples found in {self.path}")
def __len__(self):
return len(self.examples)
def __getitem__(self, index: int):
text = format_example(self.examples[index])
ids = self.tokenizer.encode(text, out_type=int)
ids = ids[: self.max_seq_len]
input_ids = torch.tensor(ids[:-1], dtype=torch.long)
labels = torch.tensor(ids[1:], dtype=torch.long)
if self.assistant_id in ids:
assistant_pos = ids.index(self.assistant_id)
labels[:assistant_pos] = -100
return input_ids, labels
def collate_batch(batch, pad_id: int):
max_len = max(x[0].numel() for x in batch)
input_ids = torch.full((len(batch), max_len), pad_id, dtype=torch.long)
labels = torch.full((len(batch), max_len), -100, dtype=torch.long)
for i, (x, y) in enumerate(batch):
input_ids[i, : x.numel()] = x
labels[i, : y.numel()] = y
return input_ids, labels
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