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from datasets import load_dataset
from dataclasses import dataclass, field
import logging
from transformers import HfArgumentParser
from tqdm import tqdm
from typing import Dict, List
import json
logger = logging.getLogger()
logger.setLevel(logging.INFO)
console_handler = logging.StreamHandler()
console_handler.setFormatter(
logging.Formatter("[%(asctime)s %(levelname)s] %(message)s")
)
logger.handlers = [console_handler]
@dataclass
class ConversionAgruments:
hardneg: str = field(metadata={"help": "Path to msmarco-hard-negatives.jsonl file"})
out: str = field(metadata={"help": "Output path"})
@dataclass
class QRel:
doc: int
score: int
def load_msmarco(path: str, split) -> Dict[int, str]:
dataset = load_dataset(path, split, split=split)
cache: Dict[int, str] = {}
for row in tqdm(dataset, desc=f"loading {path} split={split}"):
index = int(row["_id"])
cache[index] = row["text"]
return cache
def load_qrel(path: str, split: str) -> Dict[int, List[QRel]]:
dataset = load_dataset(path, split=split)
print(dataset.features)
cache: Dict[int, List[QRel]] = {}
for row in tqdm(dataset, desc=f"loading {path} split={split}"):
qid = int(row["query-id"])
qrel = QRel(int(row["corpus-id"]), int(row["score"]))
if qid in cache:
cache[qid].append(qrel)
else:
cache[qid] = [qrel]
return cache
def process_raw(
qrels: Dict[int, List[QRel]],
queries: Dict[int, str],
corpus: Dict[int, str],
hardneg: Dict[int, List[int]],
) -> List[Dict]:
result = []
for query, rels in tqdm(qrels.items(), desc="processing split"):
pos = [corpus[rel.doc] for rel in rels if rel.doc in corpus and rel.score > 0]
neg = [corpus[doc] for doc in hardneg.get(query, []) if doc in corpus]
group = {"query": queries[query], "positive": pos, "negative": neg}
result.append(group)
return result
def load_hardneg(path: str):
result: Dict[int, List[int]] = {}
with open(path, "r") as jsonfile:
for line in tqdm(jsonfile, total=808731, desc="loading hard negatives"):
row = json.loads(line)
scores: Dict[int, float] = {}
for method, docs in row["neg"].items():
for index, doc in enumerate(docs):
prev = scores.get(int(doc), 0.0)
scores[int(doc)] = prev + 1.0 / (60 + index)
topneg = [
doc
for doc, score in sorted(
scores.items(), key=lambda x: x[1], reverse=True
)
]
result[int(row["qid"])] = topneg[:32]
return result
def main():
parser = HfArgumentParser((ConversionAgruments))
(args,) = parser.parse_args_into_dataclasses()
print(f"Args: {args}")
hardneg = load_hardneg(args.hardneg)
qrels = {
"train": load_qrel("BeIR/msmarco-qrels", split="train"),
"dev": load_qrel("BeIR/msmarco-qrels", split="validation"),
}
queries = load_msmarco("BeIR/msmarco", split="queries")
corpus = load_msmarco("BeIR/msmarco", split="corpus")
print("processing done")
for split, data in qrels.items():
dataset = process_raw(data, queries, corpus, hardneg)
with open(f"{args.out}/{split}.jsonl", "w") as out:
for item in dataset:
json.dump(item, out)
out.write("\n")
print("done")
if __name__ == "__main__":
main()
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