emrekuruu
/

RetrievalRouter_Baseline

@@ -1,199 +1,117 @@
 ---
 library_name: transformers
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 ---
+license: mit
+language:
+- en
+base_model:
+- Qwen/Qwen3-0.6B-Base
 library_name: transformers
+pipeline_tag: text-classification
+tags:
+- retrieval
+- document-retrieval
+- information-retrieval
+- routing
+- RAG
+- query-routing
+- late-interaction
+- lora
+- peft
+- baseline
+datasets:
+- emrekuruu/FinReport
+- emrekuruu/FinSlides
+- emrekuruu/FinQA
+- emrekuruu/ConvFinQA
+- emrekuruu/VQAonBD
+- emrekuruu/TATDQA
+- emrekuruu/ArxivQA
+- emrekuruu/Wiki-ss
+- emrekuruu/MP-DocVQA
+- emrekuruu/SciQAG
+- emrekuruu/DUDE
+metrics:
+- ndcg
 ---
+# RetrievalRouter — Strategy-Selection Baseline
+Adaptive routing **baseline** from **RetrievalRouter: Joint Modality and Architecture Selection
+for Document Retrieval** (EMNLP 2026). This is **not** the RetrievalRouter method — it is the
+prior strategy-selection approach of
+[Arabzadeh et al. (2021)](https://dl.acm.org/doi/10.1145/3459637.3482119), extended to our
+five-pipeline setting and trained under an identical pipeline for a controlled comparison.
+- 📄 Paper: https://arxiv.org/pdf/2608.23176
+- 💻 Code: https://github.com/emrekuruu/retrieval-router
+- 🤗 RetrievalRouter checkpoints: https://huggingface.co/collections/emrekuruu/retrieval-router
+## Motivation
+Retrieval pipelines differ in **modality** (search over text, or over page images) and
+**architecture** (cheap dense search, or expensive late-interaction). The accurate ones are
+slow; the fast ones miss evidence on hard documents. And which one fails depends on the query —
+a text pipeline can't answer "what's the red curve in Figure 3?", but a multimodal one is
+overkill for a plain factoid. Across 11 benchmarks, **no single pipeline wins on everything**.
+RetrievalRouter picks the cheapest pipeline that can still answer each query, so easy queries
+stay fast and hard ones still get the heavy pipeline.
+## What this model is for
+A **research baseline**, provided to reproduce the paper's comparisons. It trains a **hard-label
+classifier**: each query is labeled with the cheapest pipeline that ranks a relevant page first
+(BM25 when none do); at inference a probability threshold decides whether the query stays on BM25
+or escalates to the top-scoring neural pipeline. It differs from RetrievalRouter only in the
+**training signal** — hard labels + cross-entropy here, versus a soft reward vector + KL for
+RetrievalRouter. Everything else (encoder, LoRA, pooling, head, split, optimizer, schedule) is
+shared, so any difference reflects the training signal alone. For deployment, prefer the
+[RetrievalRouter checkpoints](https://huggingface.co/collections/emrekuruu/retrieval-router),
+which dominate this baseline on the accuracy–latency frontier.
+## Routing arms
+| Index | Arm (config name) | Paper name | Modality | Architecture |
+|---|---|---|---|---|
+| 0 | `MULTIMODAL_RERANK` | MM-Rerank | Multimodal | Dense → late-interaction rerank |
+| 1 | `MULTIMODAL-SINGLE` | MM-Dense | Multimodal | Single-vector dense |
+| 2 | `TEXT_RERANK` | Text-Rerank | Text | Dense → late-interaction rerank |
+| 3 | `TEXT-SINGLE` | Text-Dense | Text | Single-vector dense |
+| 4 | `BM25` | BM25 | Text | Lexical |
+## Architecture
+Identical to the RetrievalRouter checkpoints:
+[Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) encoder + LoRA (merged), mean
+pooling → 1024-d, single linear head over the five arms. Custom modeling code loads via
+`trust_remote_code=True`.
+## Usage
+```python
+import torch
+from transformers import AutoModel, AutoTokenizer
+repo = "emrekuruu/RetrievalRouter_Baseline"
+tokenizer = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
+model = AutoModel.from_pretrained(repo, trust_remote_code=True).eval()
+inputs = tokenizer("In figure 3, what does the red dashed curve represent?",
+                   return_tensors="pt", truncation=True, max_length=128)
+with torch.no_grad():
+    logits = model(**inputs)["logits"]              # shape [1, 5]
+arm = model.config.strategy_names[logits.softmax(-1).argmax(-1).item()]
+print(arm)   # e.g. "MULTIMODAL_RERANK" -> run that pipeline for this query
+```
+The router returns **which retrieval pipeline to run**, not documents. You then execute the
+selected pipeline against your own indices.
+## Citation
+```bibtex
+@inproceedings{kuru2026retrievalrouter,
+  title     = {RetrievalRouter: Joint Modality and Architecture Selection for Document Retrieval},
+  author    = {Kuru, Emre and Keskin, Mehmet Onur and Farahbakhsh, Reza and Crespi, Noel},
+  booktitle = {Proceedings of EMNLP 2026},
+  year      = {2026}
+}
+```