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Fine-tune a cross-encoder reranker for Turkish legal QA.
Training data (weak supervision):
- Positive: (Soru, Cevap) from HF train- Negative: (Soru, Cevap') where Cevap' is a random other answer
Output:
A folder loadable by sentence_transformers.CrossEncoder
"""
from __future__ import annotations
import argparse
import random
from pathlib import Path
from typing import List, Tuple
from datasets import load_dataset
from sentence_transformers import InputExample
from sentence_transformers.cross_encoder import CrossEncoder
from sentence_transformers.cross_encoder.evaluation import CEBinaryClassificationEvaluator
from torch.utils.data import DataLoader
def build_pairs(max_samples: int, seed: int) -> Tuple[List[InputExample], List[InputExample]]:
random.seed(seed)
ds = load_dataset("Renicames/turkish-law-chatbot")
train = ds["train"]
qs: List[str] = []
ps: List[str] = []
for row in train:
q = str(row.get("Soru", "")).strip()
p = str(row.get("Cevap", "")).strip()
if not q or not p:
continue
qs.append(q)
ps.append(p)
n = len(qs)
if n < 2:
raise RuntimeError("Not enough HF train rows.")
limit = min(max_samples, n)
indices = list(range(n))
random.shuffle(indices)
indices = indices[:limit]
train_examples: List[InputExample] = []
eval_examples: List[InputExample] = []
for i, idx in enumerate(indices):
q = qs[idx]
pos = ps[idx]
neg_j = random.randint(0, n - 1)
if neg_j == idx:
neg_j = (neg_j + 1) % n
neg = ps[neg_j]
ex_pos = InputExample(texts=[q, pos], label=1.0)
ex_neg = InputExample(texts=[q, neg], label=0.0)
# simple 90/10 split by modulo
if i % 10 == 0:
eval_examples.extend([ex_pos, ex_neg])
else:
train_examples.extend([ex_pos, ex_neg])
return train_examples, eval_examples
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--base_model", default="cross-encoder/ms-marco-MiniLM-L-6-v2")
parser.add_argument("--out_dir", default="models/ce-legal-v1")
parser.add_argument("--epochs", type=int, default=1)
parser.add_argument("--batch_size", type=int, default=16)
parser.add_argument("--max_samples", type=int, default=6000)
parser.add_argument("--seed", type=int, default=42)
args = parser.parse_args()
out = Path(args.out_dir)
out.mkdir(parents=True, exist_ok=True)
train_ex, eval_ex = build_pairs(args.max_samples, args.seed)
train_loader = DataLoader(train_ex, shuffle=True, batch_size=args.batch_size)
model = CrossEncoder(args.base_model, num_labels=1)
evaluator = CEBinaryClassificationEvaluator.from_input_examples(eval_ex, name="legal-dev")
model.fit(
train_dataloader=train_loader,
evaluator=evaluator,
epochs=args.epochs,
warmup_steps=max(100, int(len(train_loader) * 0.1)),
output_path=str(out),
show_progress_bar=True,
)
print(f"[RerankerFT] Saved cross-encoder -> {out}")
if __name__ == "__main__":
main()
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