Instructions to use HopitAI/moda-duo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- OpenCLIP
How to use HopitAI/moda-duo with OpenCLIP:
import open_clip model, preprocess_train, preprocess_val = open_clip.create_model_and_transforms('hf-hub:HopitAI/moda-duo') tokenizer = open_clip.get_tokenizer('hf-hub:HopitAI/moda-duo') - Notebooks
- Google Colab
- Kaggle
Card: remove selection/provenance process detail; keep results, cost, usage
Browse files
README.md
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## Results
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MAP@10, full corpus, all ground-truth queries, one evaluator (`pytrec_eval map_cut.10`),
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paired bootstrap 10,000 resamples.
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benchmark was scored.
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| benchmark | MODA | MODA Pro Lite+ | **MODA Duo** |
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duo = Duo(router=lambda q: "moda" if looks_like_a_description(q) else "moda_pro_lite_plus")
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```
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##
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the benchmarks above. No benchmark was consulted during selection.
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- Full receipts, per-query results, and the sensitivity curve are in the
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[repository](https://github.com/hopit-ai/Moda).
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## Related
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## Results
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MAP@10, full corpus, all ground-truth queries, one evaluator (`pytrec_eval map_cut.10`),
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paired bootstrap 10,000 resamples.
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| benchmark | MODA | MODA Pro Lite+ | **MODA Duo** |
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duo = Duo(router=lambda q: "moda" if looks_like_a_description(q) else "moda_pro_lite_plus")
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```
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## Evaluation
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All figures are full corpus, all ground-truth queries, MAP@10 under one evaluator
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(`pytrec_eval map_cut.10`), paired bootstrap with 10,000 resamples. Per-query results are in
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the [repository](https://github.com/hopit-ai/Moda).
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## Related
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