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: 446 Bytes
b642586 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | {
"backend": "tokenizers",
"bos_token": "<s>",
"cls_token": "<s>",
"do_basic_tokenize": true,
"do_lower_case": true,
"eos_token": "</s>",
"is_local": false,
"local_files_only": false,
"mask_token": "<mask>",
"model_max_length": 256,
"never_split": null,
"pad_token": "<pad>",
"sep_token": "</s>",
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "MPNetTokenizer",
"unk_token": "[UNK]"
}
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