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+ ---
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+ language:
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+ - en
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+ - ru
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+ tags:
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+ - efficientrag
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+ - multi-hop-qa
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+ - token-classification
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+ - sequence-classification
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+ - deberta-v3
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+ license: mit
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+ base_model: microsoft/mdeberta-v3-base
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+ ---
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+
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+ # EfficientRAG Labeler (mdeberta-v3-base)
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+
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+ **Labeler** component of [EfficientRAG](https://arxiv.org/abs/2408.04259) — dual-headed DeBERTa model for multi-hop retrieval.
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+
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+ ## What it does
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+
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+ Given a query and a retrieved chunk, the Labeler:
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+ 1. **Sequence classification**: Is this chunk relevant (`CONTINUE`) or irrelevant (`TERMINATE`)?
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+ 2. **Token classification**: Which tokens in the chunk are useful for answering?
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+
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+ ## Architecture
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+
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+ - Base: `microsoft/mdeberta-v3-base` (86M params, multilingual)
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+ - Custom dual head: `DebertaForSequenceTokenClassification`
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+ - Token head: binary per-token (useful/useless)
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+ - Sequence head: binary per-chunk (CONTINUE/TERMINATE)
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+
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+ ## Training
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+
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+ | | |
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+ |--|--|
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+ | Data | 30,818 samples (HotpotQA EN + Dragon-derec RU) |
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+ | Epochs | 2 |
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+ | Batch size | 4 |
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+ | LR | 5e-6 |
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+ | Max length | 384 |
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+ | Hardware | Apple M3 Pro, ~3.4 hours |
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+
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+ ## Usage
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+
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+
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+
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+ ## Results on DRAGON benchmark
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+
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+ | Metric | Baseline | EfficientRAG | Delta |
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+ |--------|----------|-------------|-------|
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+ | MRR (multi-hop) | 0.736 | 0.798 | **+0.062** |
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+ | MRR (overall) | 0.783 | 0.822 | **+0.040** |
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+ | Precision | 0.187 | 0.582 | **+0.395** |
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+
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+ ## Related
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+
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+ - Training data: [Necent/efficientrag-labeler-training-data](https://huggingface.co/datasets/Necent/efficientrag-labeler-training-data)
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+ - Filter model: [Necent/efficientrag-filter-mdeberta-v3-base](https://huggingface.co/Necent/efficientrag-filter-mdeberta-v3-base)
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+ - Paper: [EfficientRAG (arXiv:2408.04259)](https://arxiv.org/abs/2408.04259)