File size: 6,110 Bytes
b464490 | 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 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 | """MS MARCO data loading for training and evaluation."""
import random
from typing import Optional
import torch
from torch.utils.data import Dataset, DataLoader
from datasets import load_dataset
class MSMARCOTripleDataset(Dataset):
"""MS MARCO passage ranking dataset with hard negatives.
Each example yields (query, positive_passage, [negative_passages]).
"""
def __init__(self, tokenizer, max_samples: int = 100_000,
num_negatives: int = 7, max_seq_length: int = 128,
split: str = "train", seed: int = 42):
self.tokenizer = tokenizer
self.max_seq_length = max_seq_length
self.num_negatives = num_negatives
# Load MS MARCO dataset
print(f"Loading MS MARCO ({split} split, max {max_samples} samples)...")
dataset = load_dataset("ms_marco", "v2.1", split=split, trust_remote_code=True)
# Filter to examples with at least one selected passage
self.examples = []
for i, ex in enumerate(dataset):
if len(self.examples) >= max_samples:
break
passages = ex["passages"]
selected = [j for j, s in enumerate(passages["is_selected"]) if s == 1]
if selected:
self.examples.append({
"query": ex["query"],
"positive": passages["passage_text"][selected[0]],
"negatives": [
passages["passage_text"][j]
for j in range(len(passages["passage_text"]))
if j not in selected
],
})
print(f"Loaded {len(self.examples)} training examples.")
self.rng = random.Random(seed)
def __len__(self) -> int:
return len(self.examples)
def __getitem__(self, idx: int) -> dict:
ex = self.examples[idx]
# Sample negatives (from in-passage negatives, pad with random if needed)
available_negs = ex["negatives"]
if len(available_negs) >= self.num_negatives:
negs = self.rng.sample(available_negs, self.num_negatives)
else:
negs = available_negs[:]
# Pad with random negatives from other examples
while len(negs) < self.num_negatives:
rand_ex = self.examples[self.rng.randint(0, len(self.examples) - 1)]
if rand_ex["positive"] != ex["positive"]:
negs.append(rand_ex["positive"])
return {
"query": ex["query"],
"positive": ex["positive"],
"negatives": negs,
}
def collate_fn(batch: list[dict], tokenizer, max_seq_length: int = 128) -> dict:
"""Collate batch into tokenized tensors."""
queries = [b["query"] for b in batch]
positives = [b["positive"] for b in batch]
all_negatives = []
for b in batch:
all_negatives.extend(b["negatives"])
# Tokenize
q_enc = tokenizer(
queries, padding=True, truncation=True,
max_length=max_seq_length, return_tensors="pt",
)
p_enc = tokenizer(
positives, padding=True, truncation=True,
max_length=max_seq_length, return_tensors="pt",
)
n_enc = tokenizer(
all_negatives, padding=True, truncation=True,
max_length=max_seq_length, return_tensors="pt",
)
num_negatives = len(batch[0]["negatives"])
return {
"query_input_ids": q_enc["input_ids"],
"query_attention_mask": q_enc["attention_mask"],
"pos_input_ids": p_enc["input_ids"],
"pos_attention_mask": p_enc["attention_mask"],
"neg_input_ids": n_enc["input_ids"],
"neg_attention_mask": n_enc["attention_mask"],
"num_negatives": num_negatives,
}
def get_dataloader(tokenizer, max_samples: int = 100_000,
num_negatives: int = 7, batch_size: int = 64,
max_seq_length: int = 128, split: str = "train",
seed: int = 42, num_workers: int = 0) -> DataLoader:
"""Create a DataLoader for MS MARCO training."""
dataset = MSMARCOTripleDataset(
tokenizer=tokenizer, max_samples=max_samples,
num_negatives=num_negatives, max_seq_length=max_seq_length,
split=split, seed=seed,
)
def _collate(batch):
return collate_fn(batch, tokenizer, max_seq_length)
return DataLoader(
dataset, batch_size=batch_size, shuffle=True,
collate_fn=_collate, num_workers=num_workers,
drop_last=True,
)
class MSMARCOEvalDataset:
"""MS MARCO dev set for evaluation."""
def __init__(self, tokenizer, max_queries: int = 5000,
max_seq_length: int = 128, seed: int = 42):
self.tokenizer = tokenizer
self.max_seq_length = max_seq_length
print(f"Loading MS MARCO dev set (max {max_queries} queries)...")
dataset = load_dataset("ms_marco", "v2.1", split="validation", trust_remote_code=True)
self.queries = []
self.positives = [] # list of list of positive passage texts
self.all_passages = [] # flat list of all passages for retrieval
self.passage_set = set()
rng = random.Random(seed)
indices = list(range(len(dataset)))
rng.shuffle(indices)
for i in indices:
if len(self.queries) >= max_queries:
break
ex = dataset[i]
passages = ex["passages"]
selected = [j for j, s in enumerate(passages["is_selected"]) if s == 1]
if not selected:
continue
self.queries.append(ex["query"])
pos_texts = [passages["passage_text"][j] for j in selected]
self.positives.append(pos_texts)
# Add all passages to the corpus
for text in passages["passage_text"]:
if text not in self.passage_set:
self.passage_set.add(text)
self.all_passages.append(text)
print(f"Loaded {len(self.queries)} eval queries, "
f"{len(self.all_passages)} unique passages.")
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