---
language:
- ar
- fr
license: apache-2.0
task_categories:
- text-retrieval
- text-ranking
tags:
- algerian-arabic
- darja
- arabizi
- code-switching
- semantic-search
- e-commerce
- marketplace
- cross-encoder
- information-retrieval
- low-resource-nlp
size_categories:
- 10K<100K
---

# algerian-cars-realestate-search-dataset

**A multilingual query/listing relevance dataset for Algerian marketplace search, built from algerian search listings (vehicles and real estate) with user queries in Darja, Arabizi, French, and Arabic.**

Each example pairs a search query with a listing document and a relevance score in [0, 1]. Positive pairs come from true query-listing matches; negatives were mined using the [`81melody/algerianME5`](https://huggingface.co/81melody/algerianME5) dense retriever (hard negatives: top-retrieved but irrelevant listings). This dataset was used to train the cross-encoder reranker, which reaches a mean NDCG@10 of **0.967** on the full retrieval pipeline across 7 multilingual query categories.

---

## Dataset Statistics

| Split | Examples | File |
|---|---|---|
| Train | 14,292 | `train.jsonl` |
| Validation | 1,588 | `val.jsonl` |
| **Total** | **15,880** | |

| Category | Train examples |
|---|---:|
| `immo` (real estate) | 9,263 |
| `auto` (vehicles) | 5,029 |

| Strategy | Examples |
|---|---:|
| `mined_negative` (hard negatives from bi-encoder) | 10,246 |
| `positive` (true matches) | 4,046 |

Positive/negative breakdown (label > 0.5): **positive 5,937 · negative 8,355**

---

## File Schema

Each line in `train.jsonl` and `val.jsonl` is a JSON object:

```json
{
  "query":    "appart Souk Ahras",
  "document": "بيع شقة 4 غرف سوق أهراس ... Appartement f4 en plein centre ville ...",
  "label":    0.7,
  "weight":   1.0,
  "category": "immo",
  "strategy": "mined_negative"
}
```

| Field | Type | Description |
|---|---|---|
| `query` | string | User search query — Darja, Arabizi, French, MSA or mixed |
| `document` | string | Listing text (title + description concatenated) |
| `label` | float `[0, 1]` | Relevance score: 1.0 = exact match, 0.7 = partial match, 0.0 = irrelevant |
| `weight` | float | Loss weight for training (positive examples may be upweighted) |
| `category` | string | `"immo"` (real estate) or `"auto"` (vehicles) |
| `strategy` | string | How the pair was generated: `"positive"` or `"mined_negative"` |

---

## Quick Start

```python
from datasets import load_dataset
import json

# Load locally
def load_jsonl(path):
    with open(path) as f:
        return [json.loads(line) for line in f]

train = load_jsonl("train.jsonl")
val   = load_jsonl("val.jsonl")

# Inspect a positive pair
pos = [ex for ex in train if ex["label"] > 0.5 and ex["strategy"] == "positive"][0]
print("Query:   ", pos["query"])
print("Document:", pos["document"][:200])
print("Label:   ", pos["label"])

```

---

## Query Language Distribution

Queries span the full Algerian multilingual space:

| Language / Script | Examples |
|---|---|
| French | "appart Souk Ahras", "villa avec piscine" |
| Algerian Darja (Arabic) | "نحوس على فيلا في العاصمة" |
| Arabizi (Arabic in Latin) | "nhawes 3la dar" |
| Mixed French-Arabic | "appart f3 في الجزائر العاصمة" |
| MSA | "شقة للبيع في وهران" |

---

## Performance of the Trained Cross-Encoder

The model trained on this data achieves the following NDCG@10 on an internal 46-query eval set spanning both categories and all query languages:

| Category | Description | NDCG@10 |
|---|---|---:|
| A | Exact-match vehicle queries | 0.929 |
| B | Semantic French queries | 0.951 |
| C | Darja queries | 0.984 |
| D | Arabizi queries | 0.998 |
| E | Real-estate (AR/FR) | 0.956 |
| F | Semantic real-estate | 0.999 |
| G | Mixed-language | 0.999 |
| **Mean** | | **0.967** |

> These scores measure the full two-stage pipeline (bi-encoder retrieval + cross-encoder reranking), not the cross-encoder in isolation.

---

## Source

Listings collected from the largest Algerian online marketplaces (vehicles and real estate). Queries are synthetic or paraphrased from real user search patterns. Listing text is public-facing ad content. Phone numbers and email addresses in listing text were masked with `[PHONE]` and `[EMAIL]` placeholders before release.


---

## Citation

```bibtex
@dataset{himeur2026algerian_search,
  title        = {algerian-cars-realestate-search-dataset: Multilingual Query/Listing Relevance Dataset
                  for Algerian Marketplace Search},
  author       = {Himeur, Ayoub},
  year         = {2026},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/81melody/algerian-cars-realestate-search-dataset},
  note         = {15,880 query/listing pairs from algerian Marketplaces (vehicles + real estate),
                  Darja/Arabizi/French/MSA queries, hard-negative mined}
}
```

---

## License

[Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)