Scikit-learn
Joblib
English
protocol-routing
llm-routing
multi-agent-systems
cost-aware-inference
protocol-selection
calibration
reasoning
reproducibility
emnlp2026
Instructions to use AgentsSci/EMNLP_Cost-Aware-Protocol-Routing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use AgentsSci/EMNLP_Cost-Aware-Protocol-Routing with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("AgentsSci/EMNLP_Cost-Aware-Protocol-Routing", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
Add pretty_name, quiet the version-warning wall in the example, and record the root widget copy's checksum
294c751 verified Download router_metadata_only/model_metadata.json from AgentsSci/EMNLP_Cost-Aware-Protocol-Routing: direct link, hf CLI and curl.
- Browser
- Download file 3.13 kB
-
https://huggingface.co/AgentsSci/EMNLP_Cost-Aware-Protocol-Routing/resolve/main/router_metadata_only/model_metadata.json
- Command line
-
hf download hf://AgentsSci/EMNLP_Cost-Aware-Protocol-Routing/router_metadata_only/model_metadata.json
-
curl -L -o model_metadata.json https://huggingface.co/AgentsSci/EMNLP_Cost-Aware-Protocol-Routing/resolve/main/router_metadata_only/model_metadata.json
3.13 kB
| { | |
| "artifact": "metadata-only protocol router (multinomial logistic regression)", | |
| "task": "predict the fixed-order-oracle action for a problem, before any protocol runs", | |
| "actions": [ | |
| "baseline_llm", | |
| "single_agent", | |
| "per", | |
| "broadcast", | |
| "none" | |
| ], | |
| "n_features_in": 220, | |
| "feature_blocks": [ | |
| { | |
| "block": "source", | |
| "type": "OneHotEncoder", | |
| "note": "competition/source identifier" | |
| }, | |
| { | |
| "block": "domain", | |
| "type": "MultiLabelBinarizer", | |
| "note": "topic tags; unseen tags dropped" | |
| }, | |
| { | |
| "block": "numeric", | |
| "type": "StandardScaler(with_mean=False)", | |
| "columns": [ | |
| "difficulty", | |
| "difficulty_tier" | |
| ] | |
| } | |
| ], | |
| "feature_order": "source one-hot, then domain multi-hot, then the two scaled numeric columns", | |
| "split": { | |
| "scheme": "stratified 80/10/10", | |
| "seed": 42, | |
| "train": 3342, | |
| "dev": 416, | |
| "test": 423 | |
| }, | |
| "selected_hyperparameters": { | |
| "name": "plain_l2_c0.25", | |
| "class_weight": null, | |
| "C": 0.25 | |
| }, | |
| "selection": "macro-F1 then excess-token-cost on DEV only, with validation-based early stopping", | |
| "leakage": "features are problem metadata only: no gold answers, no correctness, no protocol outcomes, no oracle label", | |
| "pickled_with": { | |
| "note": "recorded so a loader can explain a version warning", | |
| "sklearn_at_fit": "1.8.0 (from the InconsistentVersionWarning emitted on load)" | |
| }, | |
| "verified_in_this_environment": { | |
| "sklearn": "1.6.1", | |
| "numpy": "2.0.2", | |
| "scipy": "1.13.1", | |
| "predict": "works", | |
| "predict_proba": "raises AttributeError under this version; predict_example.py falls back to the decision function" | |
| }, | |
| "files": { | |
| "class_recalls.csv": { | |
| "bytes": 525, | |
| "sha256": "a4cbb87ec4a07cb3e56543aef9dd54aaa929ab95933adfc106cf52845da7540b" | |
| }, | |
| "feature_builders.joblib": { | |
| "bytes": 23857, | |
| "sha256": "3252c274c6ae5a90afe3655823e2a16977c21796f7d76fbe3e6141454ca61e62" | |
| }, | |
| "label_mapping.json": { | |
| "bytes": 906, | |
| "sha256": "ac5c5cfef8e8d8a556351d078e3b2fd4a6415c19c3ff8d439f53c543cf6c590e" | |
| }, | |
| "metrics.csv": { | |
| "bytes": 376, | |
| "sha256": "7e043f4946fc3219cb1c8ba5a7b259b3b991084c452b8ef388fd07d4f3356833" | |
| }, | |
| "model.joblib": { | |
| "bytes": 9711, | |
| "sha256": "63b5358423b430b39f2a586913d48bf84f2b6774cda78bf1bc383c76de7f0c3b" | |
| }, | |
| "predict_example.py": { | |
| "bytes": 4724, | |
| "sha256": "a61bdbf294349bc2c21a3543d4e197b2bb83ba0b4be824cbfd981496ad40a813" | |
| }, | |
| "selected_config.json": { | |
| "bytes": 1283, | |
| "sha256": "44542b6696893bbd23e630b208a2061f67085c5dc56fbc04cb8b61c1464770ed" | |
| }, | |
| "test_predictions.csv": { | |
| "bytes": 17241, | |
| "sha256": "5bcb554174f5cc34588d201938835473170c1c57154718dfede792341fcf3ee1" | |
| } | |
| }, | |
| "root_widget_copy": { | |
| "path": "sklearn_model.joblib", | |
| "purpose": "byte-identical duplicate so Hugging Face's auto-generated sklearn snippet resolves", | |
| "canonical": "router_metadata_only/model.joblib", | |
| "sha256": "63b5358423b430b39f2a586913d48bf84f2b6774cda78bf1bc383c76de7f0c3b" | |
| } | |
| } |