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
File size: 842 Bytes
fc2bf43 7ec2ff5 fc2bf43 7ec2ff5 fc2bf43 7ec2ff5 fc2bf43 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | {
"macro_f1": 0.6363926493183459,
"micro_f1": 0.7068273092369478,
"ece": 0.016302321274583108,
"subset_accuracy": 0.5578947368421052,
"per_label_f1": {
"Agricultural communities": 0.8333,
"Coastal communities": 0.5714,
"Ethnic, racial or other minorities": 0.4444,
"Fishery communities": 0.5,
"Informal sector workers": 0.6667,
"Members of indigenous and local communities": 0.75,
"Migrants and displaced persons": 0.75,
"Older persons": 0.8421,
"Other": 0.4848,
"Persons living in poverty": 0.6667,
"Persons with disabilities": 0.875,
"Persons with pre-existing health conditions": 0.5,
"Residents of drought-prone regions": 0.5714,
"Rural populations": 0.8,
"Sexual minorities (LGBTQI+)": 0.0,
"Urban populations": 0.6154,
"Women and other genders": 0.9474
}
} |