Text Classification
Scikit-learn
Joblib
English
intent-classification
logistic-regression
conference-talk-demo
Instructions to use thinktecture/intent-logreg-nextera with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use thinktecture/intent-logreg-nextera with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("thinktecture/intent-logreg-nextera", "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
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- intent-classification
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**⚠️ Conference talk demo — not production weights.**
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This model accompanies a conference keynote on local on-device AI. Published
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as a reference for the fine-tuning patterns shown on stage — **not** a
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deployable artefact. No security audit, no SLA, pinned to the talk's state.
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- Source repository: [thinktecture-labs/local-multi-model-agent-slm](https://github.com/thinktecture-labs/local-multi-model-agent-slm)
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- Threat model + out-of-scope: [`SECURITY.md`](SECURITY.md)
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- Licensing details: [`MODEL_LICENSES.md`](MODEL_LICENSES.md)
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- All five models in the stack: [Collection — Local Multi-Model Agent — nextera fine-tunes](https://huggingface.co/collections/thinktecture/local-multi-model-agent-nextera-fine-tunes-6a04a8ff2a40e5696f3c2f18)
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