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: 610 Bytes
b642586 aa9c289 b642586 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"labels": [
"Agricultural communities",
"Coastal communities",
"Ethnic, racial or other minorities",
"Fishery communities",
"Informal sector workers",
"Members of indigenous and local communities",
"Migrants and displaced persons",
"Older persons",
"Other",
"Persons living in poverty",
"Persons with disabilities",
"Persons with pre-existing health conditions",
"Residents of drought-prone regions",
"Rural populations",
"Sexual minorities (LGBTQI+)",
"Urban populations",
"Women and other genders"
],
"normalize_embeddings": false
} |