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
Upload nextera fine-tune
Browse files- meta.json +18 -0
- model.joblib +3 -0
meta.json
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{
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"classes": [
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"direct_answer",
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"rag_query",
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"tool_use"
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],
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"embedding_dim": 768,
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"n_training_examples": 1926,
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"label_distribution": {
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"rag_query": 660,
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"direct_answer": 435,
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"tool_use": 831
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},
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"cv_accuracy_mean": 0.9298822421102214,
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"holdout_accuracy": 1.0,
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"n_holdout_examples": 36,
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"max_iter": 1000
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}
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model.joblib
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version https://git-lfs.github.com/spec/v1
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oid sha256:4016275ef47030b712983c6177f16248b1c56ff42a1865f398c3e1c0c9c0a494
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size 19439
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