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README.md

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+---
+language:
+- en
+- ru
+tags:
+- efficientrag
+- multi-hop-qa
+- token-classification
+- deberta-v3
+license: mit
+base_model: microsoft/mdeberta-v3-base
+---
+
+# EfficientRAG Filter (mdeberta-v3-base)
+
+**Filter** component of [EfficientRAG](https://arxiv.org/abs/2408.04259) — constructs next-hop queries via token selection.
+
+## What it does
+
+Given the original question + extracted useful tokens, the Filter selects which tokens to keep in the next retrieval query. This is extractive (no generation) — it picks words from the input.
+
+## Architecture
+
+- Base: `microsoft/mdeberta-v3-base` (86M params, multilingual)
+- Standard `DebertaV2ForTokenClassification` with 2 labels (keep/discard)
+
+## Training
+
+| | |
+|--|--|
+| Data | 5,691 samples (HotpotQA EN + Dragon-derec RU) |
+| Epochs | 2 |
+| Batch size | 4 |
+| LR | 1e-5 |
+| Max length | 128 |
+| Hardware | Apple M3 Pro, ~17 minutes |
+
+## Usage
+
+
+
+## Related
+
+- Training data: [Necent/efficientrag-filter-training-data](https://huggingface.co/datasets/Necent/efficientrag-filter-training-data)
+- Labeler model: [Necent/efficientrag-labeler-mdeberta-v3-base](https://huggingface.co/Necent/efficientrag-labeler-mdeberta-v3-base)
+- Paper: [EfficientRAG (arXiv:2408.04259)](https://arxiv.org/abs/2408.04259)