---
pretty_name: BSLM Pair 206k Query-Title Dataset
language:
- fa
task_categories:
- sentence-similarity
- text-retrieval
tags:
- sentence-transformers
- ecommerce
- query-product-retrieval
- basalam
size_categories:
- 100K<n<1M
configs:
- config_name: default
  data_files:
  - split: train
    path: train.jsonl
  - split: validation
    path: dev.jsonl
  - split: test
    path: test.jsonl
---

# BSLM Pair 206k Query-Title Dataset

Query/product-title pair dataset used to fine-tune [`mjaliz/bslm-pair-206k`](https://huggingface.co/mjaliz/bslm-pair-206k) with Sentence Transformers `MultipleNegativesRankingLoss`.

The dataset was built from ecommerce query-to-anchor candidates by selecting one positive product title per query (`top1`) to reduce false negatives in in-batch negative training.

## Files

| File | Rows | Purpose |
|---|---:|---|
| `train.jsonl` | 202,149 | Pair training rows |
| `dev.jsonl` | 2,063 | Pair validation rows |
| `test.jsonl` | 2,063 | Pair test rows |
| `dev_queries.jsonl` | 2,063 | BEIR-style dev queries |
| `dev_corpus.jsonl` | 2,047 | BEIR-style dev corpus |
| `dev_qrels.jsonl` | 2,063 | BEIR-style dev relevance labels |
| `test_queries.jsonl` | 2,063 | BEIR-style test queries |
| `test_corpus.jsonl` | 2,046 | BEIR-style test corpus |
| `test_qrels.jsonl` | 2,063 | BEIR-style test relevance labels |
| `stats.json` | 1 | Dataset build statistics |

## Main pair schema

The main `train.jsonl`, `dev.jsonl`, and `test.jsonl` files contain one JSON object per line with fields such as:

- `query_id`: deterministic query identifier
- `anchor`: query text
- `positive`: positive product title
- `product_id`: positive product identifier
- `anchor_score`: candidate score
- `anchor_rank`: selected candidate rank
- `anchor_margin`: margin from the next candidate where available
- `query_frequency`: source query frequency
- `mapping_confidence`: source mapping confidence
- `mapping_method`: source mapping method

For Sentence Transformers training, only `anchor` and `positive` are required.

## Loading example

```python
from datasets import load_dataset

pairs = load_dataset("mjaliz/bslm-pair-206k-data")
print(pairs["train"][0]["anchor"])
print(pairs["train"][0]["positive"])
```

## Training context

This dataset corresponds to the pair-training stage described in the repository README:

```bash
CUDA_VISIBLE_DEVICES=2,3,5 torchrun --standalone --nproc_per_node=3 scripts/train_biencoder.py \
  --model-name-or-path mjaliz/bslm-mlm-25M-ptdrw \
  --train-file data/pairs/train.jsonl \
  --dev-file data/pairs/dev.jsonl \
  --dev-queries data/pairs/dev_queries.jsonl \
  --dev-corpus data/pairs/dev_corpus.jsonl \
  --dev-qrels data/pairs/dev_qrels.jsonl \
  --output-dir models/bslm-mnrl-query-title \
  --loss mnrl
```

## Notes

- Splits are by `query_id` to avoid leakage across train/dev/test.
- The pair builder selected one positive per query (`positive_selection=top1`).
- Source confidences included `high` and `medium`.
- License is not specified here; verify downstream usage permissions before redistributing derived artifacts.