Semantic-Retrieval-1st-place

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

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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.

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The Challenge

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.

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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.

Retriever fusion weights (normalized):

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.

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Included Models (fine-tuned)

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).

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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/

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Usage

The pipeline exposes two functions that match the competition API:

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).

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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.

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Results

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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.

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Acknowledgements

• MAFAT DDR&D and the Israel National NLP Program for organizing the challenge and providing the annotated Hebrew corpus.
• Webiks 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.