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

# Hebrew Semantic Retrieval — 1st 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:** 🥇 **1st place** — nDCG@20 = **0.6736** (private test set)

**Author:** victord

---

## Overview

This repository contains the complete inference code and fine-tuned models for the winning solution to the **Hebrew Semantic Retrieval Challenge**. The challenge tasked participants with building a semantic retrieval system capable of ranking Hebrew paragraphs from a large-scale corpus (127,731 paragraphs) in response to natural-language Hebrew queries, evaluated by **NDCG@20**.

Hebrew is a morphologically rich, Semitic language written in an almost consonant-only script, which creates high lexical ambiguity and makes retrieval significantly harder than in English or other high-resource languages. The challenge was designed to close this gap and advance Hebrew NLP for domains such as government services, law, academia, and the public sector.

---

## 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) |

Ground-truth labels were produced in two stages: a semantic retrieval model first retrieved the top-20 candidates per query, then human annotators rated them on a 0–4 relevance scale.

---

## Solution Architecture

The solution is a classic **two-stage retrieve-then-rerank pipeline**, built on top of a large ensemble of multilingual and Hebrew-specialized embedding models, combined with a sparse BM25 stage.

```
Query
  │
  ├─► [Dense Retriever ×6]  ──┐
  │                            ├─► Score Fusion (weighted, z-normalized)
  └─► [BM25s Sparse]  ─────────┘
            │
            ▼
     Top-250 Candidates
            │
            ▼
     [BGE Cross-Encoder Reranker]  (fine-tuned)
            │
            ▼
     Final Top-20 Results (ranked by fused score)
```

### Stage 1 — Ensemble Dense + Sparse Retrieval

Six dense embedding models run in parallel. Each produces per-document cosine-similarity scores, which are **z-score normalized** (using pre-computed corpus statistics) and **linearly fused** with learned weights. BM25s contributes a 15 % weight in the final fusion.

| Model | Role | Pooling | Max Length |
|---|---|---|---|
| `multilingual-e5-large` (pseudo-fine-tuned) | Primary dense retriever | Mean pooling + L2 norm | 512 |
| `multilingual-e5-large-instruct` | Instruct-style dense retriever | Mean pooling + L2 norm | 512 |
| `BAAI/bge-m3` | Multilingual dense retriever | CLS token + L2 norm | 512 |
| `Snowflake/snowflake-arctic-embed-l-v2.0` | Multilingual dense retriever | CLS token + L2 norm | 1024 |
| `OrdalieTech/Solon-embeddings-large-0.1` | Multilingual dense retriever | Mean pooling + L2 norm | 512 |
| `Webiks/Hebrew-RAGbot-KolZchut-QA-Embedder-v1.0` | Hebrew-specialized retriever | Mean pooling + L2 norm | 512 |
| **BM25s** | Sparse lexical retriever | — | — |

**Retriever fusion weights (normalized):**

| Retriever | Weight |
|---|---|
| E5-large (pseudo-tuned) | 1.10 |
| E5-large-instruct | 0.25 |
| BGE-M3 | 0.20 |
| Snowflake Arctic | 0.30 |
| Solon | 0.30 |
| Hebrew RAGbot | 0.30 |
| BM25s | 15 % blended into final fusion |

**Long-document handling:** For passages exceeding the model's max context length, a sliding-window chunking strategy with 50 % overlap is applied at the token level, and the maximum chunk score is used to represent the document.

### Stage 2 — Cross-Encoder Reranking

The top-250 candidates from Stage 1 are reranked by a **fine-tuned BGE cross-encoder** (`bge-reranker-v2-m3`, pseudo-fine-tuned on the challenge corpus). The reranker operates with a max sequence length of 2048 tokens using the same sliding-window + max-score strategy for long documents.

The final score is a blend of the reranker score and the Stage 1 fusion score:

$$\text{score}_\text{final} = 0.35 \cdot \hat{s}_\text{reranker} + 0.65 \cdot s_\text{fusion}$$

where $\hat{s}_\text{reranker}$ is z-score normalized. The top-20 documents by this blended score are returned.

---

## Included Models (fine-tuned)

| Path in repo | Base model | Fine-tuning |
|---|---|---|
| `models/multilingual-e5-large_pseudo_full/` | `intfloat/multilingual-e5-large` | Pseudo-label fine-tuning on the challenge corpus |
| `models/bge-reranker-v2-m3_pseudo_tune_full/` | `BAAI/bge-reranker-v2-m3` | Pseudo-label fine-tuning on the challenge corpus |

The remaining models (`bge-m3`, `multilingual-e5-large-instruct`, `snowflake-arctic-embed-l-v2.0`, `Solon-embeddings-large-0.1`, `Webiks_Hebrew_RAGbot_KolZchut_QA_Embedder_v1.0`) are used as-is (no additional fine-tuning).

---

## Repository Structure

```
model.py              ← Full inference pipeline (preprocess + predict)
models/
  bge-m3/
  bge-reranker-v2-m3_pseudo_tune_full/   ← Fine-tuned reranker ✨
  multilingual-e5-large_pseudo_full/     ← Fine-tuned embedder ✨
  multilingual-e5-large-instruct/
  snowflake-arctic-embed-l-v2.0/
  Solon-embeddings-large-0.1/
  Webiks_Hebrew_RAGbot_KolZchut_QA_Embedder_v1.0/
```

---

## Usage

The pipeline exposes two functions that match 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": 0.92}, ...]  (top-20)
```

**Requirements:**
```
torch
transformers
sentence-transformers
bm25s
scikit-learn
numpy
```

A CUDA-capable GPU is strongly recommended (the pipeline loads ~6 large models simultaneously).

---

## Technical Notes

- All models are loaded in **bfloat16** precision to reduce GPU memory footprint.
- **Offline mode** is enforced at runtime (`HF_HUB_OFFLINE=1`) — all model weights must be present locally.
- BM25s tokenization uses the default `bm25s` tokenizer with no additional Hebrew-specific pre-processing.
- The pipeline is time-budgeted: the reranker respects a ~1.85 s per-query wall-clock limit and will skip remaining batches if the budget is exceeded, gracefully falling back to Stage 1 scores.
- CUDA memory is proactively freed between batches; OOM errors trigger single-sample fallback processing.

---

## Results

| Phase | NDCG@20 | Rank |
|---|---|---|
| Public (Phase I) | **0.456235** | 🥇 1st |
| Private (Phase II) | **0.6736** | 🥇 1st |

---

## 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 **victord** as the solution author.

---

## Acknowledgements

- MAFAT DDR&D and the **Israel National NLP Program** for organizing the challenge and providing the annotated Hebrew corpus.
- [Webiks](https://www.webiks.com/) for the `Hebrew-RAGbot-KolZchut-QA-Embedder-v1.0` model.
- The authors of `multilingual-e5-large`, `bge-m3`, `bge-reranker-v2-m3`, `snowflake-arctic-embed-l-v2.0`, and `Solon-embeddings-large-0.1`.