Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
text-embeddings-inference
Instructions to use peter2000/setfit-vulnerability-groups with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use peter2000/setfit-vulnerability-groups with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("peter2000/setfit-vulnerability-groups") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - sentence-transformers
How to use peter2000/setfit-vulnerability-groups with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("peter2000/setfit-vulnerability-groups") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Add evaluation metrics (macro-F1, micro-F1, per-label F1, ECE)
Browse files- metrics.json +25 -0
metrics.json
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{
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"macro_f1": 0.06862745098039215,
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"micro_f1": 0.23529411764705882,
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"ece": 0.028730783743016925,
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"subset_accuracy": 0.1,
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"per_label_f1": {
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"Agricultural communities": 0.0,
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"Coastal communities": 0.0,
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"Ethnic, racial or other minorities": 0.0,
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"Fishery communities": 0.0,
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"Informal sector workers": 0.0,
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"Members of indigenous and local communities": 0.6667,
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"Migrants and displaced persons": 0.0,
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"Older persons": 0.0,
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"Other": 0.0,
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"Persons living in poverty": 0.0,
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"Persons with disabilities": 0.0,
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"Persons with pre-existing health conditions": 0.0,
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"Residents of drought-prone regions": 0.0,
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"Rural populations": 0.0,
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"Sexual minorities (LGBTQI+)": 0.0,
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"Urban populations": 0.0,
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"Women and other genders": 0.5
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}
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}
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