README.md

---
language:
- he
tags:
- hebrew
- semantic-retrieval
- information-retrieval
- dense-retrieval
- reranking
- bge-m3
- sentence-transformers
- competition
pipeline_tag: sentence-similarity
license: other
---

# Hebrew Semantic Retrieval — 3rd Place Solution

**Competition:** Hebrew Semantic Retrieval Challenge by MAFAT DDR&D (Directorate of Defense Research & Development) in partnership with the **Israel National NLP Program**

**Result:** 🥉 **3rd place** — nDCG@20 = **0.652538** (private test set) · **0.432286** (public test set)

**Author:** kdbrodt

---

## Overview

This repository contains the complete inference code and fine-tuned models for the 3rd-place solution to the **Hebrew Semantic Retrieval Challenge**. The challenge tasked participants with ranking Hebrew paragraphs from a 127,731-passage corpus in response to natural-language Hebrew queries, evaluated by **NDCG@20**.

The solution is a clean, end-to-end two-stage retrieve-then-rerank pipeline built entirely on the [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding) (`BAAI/bge-m3`) family. Both the dense embedder and the cross-encoder reranker were fine-tuned directly on the competition's annotated Hebrew data.

---

## The Challenge

| Property | Detail |
|---|---|
| Organizer | MAFAT DDR&D + Israel National NLP Program |
| Corpus size | 127,731 Hebrew paragraphs |
| Data sources | Hebrew Wikipedia, Kol-Zchut (legal/civil-rights), Knesset committee protocols |
| Evaluation metric | NDCG@20 |
| Phase I | Public leaderboard (Codabench) |
| Phase II | Private test set with additional human annotation of previously unseen retrievals |
| Relevance scale | 0–4 (human annotated) |

---

## Solution Architecture

A straightforward two-stage pipeline: dense retrieval followed by cross-encoder reranking.

```
Query
  │
  ▼
[BGE-M3 Dense Retriever]  (fine-tuned, CLS pooling, FP16)
  │  cosine similarity over 127k passages
  ▼
Top-100 Candidates
  │
  ▼
[BGE-Reranker-v2-M3]  (fine-tuned binary classifier, FP16)
  │  query-passage pairs scored, max_length=512
  ▼
Final Top-20 Results
```

### Stage 1 — Dense Retrieval

The fine-tuned `bge-m3` encoder produces **CLS-token embeddings** (L2-normalized, FP16) for all corpus passages at preprocessing time. At query time, a single query embedding is computed and scored against all corpus embeddings via **dot-product similarity** (equivalent to cosine similarity on normalized vectors). The top-100 passages are selected for reranking.

| Property | Value |
|---|---|
| Model | `test_encoder_only_base_bge_m3_new1` (fine-tuned `BAAI/bge-m3`) |
| Pooling | CLS token |
| Normalization | L2 |
| Precision | FP16 |
| Max length | 512 tokens |
| Batch size (corpus) | 64 |
| Retrieval pool | Top-100 candidates |

### Stage 2 — Cross-Encoder Reranking

The top-100 candidates are re-scored by the fine-tuned `bge-reranker-v2-m3`, a sequence classification model that takes concatenated `[query, passage]` pairs as input and outputs a relevance logit. Passages are sorted by length before scoring to minimize padding overhead. The top-20 by reranker score are returned.

| Property | Value |
|---|---|
| Model | `test_encoder_only_base_bge_reranker_v2_m3_new1` (fine-tuned `BAAI/bge-reranker-v2-m3`) |
| Max length | 512 tokens |
| Batch size | 16 |
| Output | Top-20 by reranker logit |

---

## Fine-Tuning

Both models were fine-tuned on the competition's annotated Hebrew training set using the [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding) framework.

**Training data construction:**
- Every query–document pair with a **positive relevance score (> 0)** was treated as a positive example.
- Every pair with a **score of 0** was treated as a negative example.

**Embedder (`bge-m3`):** Trained with **KL-divergence loss** to produce embeddings that better separate relevant from irrelevant documents.

**Reranker (`bge-reranker-v2-m3`):** Trained as a **binary classifier** on the same positive/negative pairs, learning to predict relevance probability directly.

| Hyperparameter | Value |
|---|---|
| Epochs | 2 |
| Batch size per device | 2 |
| Learning rate | 5e-6 |
| Hardware | 2 × Nvidia Tesla V100-SXM2-32GB |
| Training time | ~1 hour |

---

## Included Models (fine-tuned)

| Path in repo | Base model | Fine-tuning |
|---|---|---|
| `models/test_encoder_only_base_bge_m3_new1/` | `BAAI/bge-m3` | KL-divergence loss on competition data ✨ |
| `models/test_encoder_only_base_bge_reranker_v2_m3_new1/` | `BAAI/bge-reranker-v2-m3` | Binary classification on competition data ✨ |

---

## Repository Structure

```
model.py      ← Full inference pipeline (preprocess + predict)
prepare.py    ← Data preparation script
train.sh      ← Training script
models/
  test_encoder_only_base_bge_m3_new1/                  ← Fine-tuned BGE-M3 embedder ✨
  test_encoder_only_base_bge_reranker_v2_m3_new1/      ← Fine-tuned BGE reranker ✨
```

---

## Usage

The pipeline exposes two functions matching the competition API:

```python
from model import preprocess, predict

# Build corpus index (run once)
# corpus_dict: {doc_id: {"passage": "..."}, ...}
preprocessed = preprocess(corpus_dict)

# Query at inference time
results = predict({"query": "מה הזכויות של שוכרי דירה?"}, preprocessed)
# Returns: [{"paragraph_uuid": "...", "score": 1.23}, ...]  (top-20)
```

**Requirements:**
```
torch
transformers
numpy
```

**Hardware:** A CUDA-capable GPU is required. Inference takes less than 1.5 hours on an `g5.xlarge` instance.

---

## Reproducing the Models

**1. Prepare data:**
```bash
# Download competition data and unzip into `hsrc/` folder
python prepare.py
```

**2. Train:**
```bash
sh ./train.sh
```
Training takes ~1 hour on 2 × V100-SXM2-32GB GPUs.

---

## Technical Notes

- Both models are loaded in **FP16** via `torch_dtype=torch.float16` and `device_map` for automatic GPU placement.
- Corpus passages are **sorted by length** before embedding to reduce padding overhead during batch encoding.
- The reranker also sorts candidates by passage length before scoring batches.
- Fallback: if reranking fails, the pipeline falls back to returning the top-20 by dense retrieval score.

---

## Results

| Phase | NDCG@20 | Rank |
|---|---|---|
| Public (Phase I) | **0.432286** | 🥉 3rd |
| Private (Phase II) | **0.652538** | 🥉 3rd |

> The large gap between public and private scores reflects the private phase's additional human annotation of previously un-annotated retrieved documents, significantly boosting NDCG for systems that retrieved relevant but unannotated paragraphs.

---

## Citation

If you use this solution or the models in this repository, please acknowledge the **Hebrew Semantic Retrieval Challenge** by MAFAT DDR&D and the Israel National NLP Program, and credit **kdbrodt** as the solution author.

---

## Acknowledgements

- MAFAT DDR&D and the **Israel National NLP Program** for organizing the challenge and providing the annotated Hebrew corpus.
- The authors of `BAAI/bge-m3` and `BAAI/bge-reranker-v2-m3`.
- The [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding) team for the training framework.