Text Classification
Transformers
Safetensors
PEFT
English
retrievalrouter
feature-extraction
retrieval
document-retrieval
information-retrieval
routing
RAG
query-routing
late-interaction
lora
custom_code
Instructions to use emrekuruu/RetrievalRouter-lambda-l30 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emrekuruu/RetrievalRouter-lambda-l30 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="emrekuruu/RetrievalRouter-lambda-l30", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("emrekuruu/RetrievalRouter-lambda-l30", trust_remote_code=True, device_map="auto") - PEFT
How to use emrekuruu/RetrievalRouter-lambda-l30 with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Fix baseline link
Browse files
README.md
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@@ -139,7 +139,7 @@ above. Training data spans **85,103 queries across 11 benchmarks**.
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Against the strongest static pipeline, RetrievalRouter is **+2.5% nDCG@5 and 12.4× faster**.
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Against the prior adaptive strategy-selection baseline
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([`emrekuruu/
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higher nDCG@5 in accuracy-oriented settings and matches or numerically beats it on both nDCG@5
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and latency in latency-oriented settings. See the paper for full tables and significance tests.
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Against the strongest static pipeline, RetrievalRouter is **+2.5% nDCG@5 and 12.4× faster**.
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Against the prior adaptive strategy-selection baseline
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([`emrekuruu/RetrievalRouter_Baseline`](https://huggingface.co/emrekuruu/RetrievalRouter_Baseline)), it achieves significantly
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higher nDCG@5 in accuracy-oriented settings and matches or numerically beats it on both nDCG@5
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and latency in latency-oriented settings. See the paper for full tables and significance tests.
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