Text Classification
setfit
Safetensors
sentence-transformers
English
bert
spam-detection
email-classification
few-shot-learning
efficient
tinymodels
text-embeddings-inference
Instructions to use TinyModels/Setfit-Banking-Spam with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use TinyModels/Setfit-Banking-Spam with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("TinyModels/Setfit-Banking-Spam") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - sentence-transformers
How to use TinyModels/Setfit-Banking-Spam with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("TinyModels/Setfit-Banking-Spam") 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
Download config_sentence_transformers.json from TinyModels/Setfit-Banking-Spam: direct link, hf CLI and curl.
- Browser
- Download file 284 Bytes
-
https://huggingface.co/TinyModels/Setfit-Banking-Spam/resolve/main/config_sentence_transformers.json
- Command line
-
hf download hf://TinyModels/Setfit-Banking-Spam/config_sentence_transformers.json
-
curl -L -o config_sentence_transformers.json https://huggingface.co/TinyModels/Setfit-Banking-Spam/resolve/main/config_sentence_transformers.json
284 Bytes
| { | |
| "__version__": { | |
| "pytorch": "2.11.0+cu128", | |
| "sentence_transformers": "5.7.0", | |
| "transformers": "5.16.1" | |
| }, | |
| "default_prompt_name": null, | |
| "model_type": "SentenceTransformer", | |
| "prompts": { | |
| "document": "", | |
| "query": "" | |
| }, | |
| "similarity_fn_name": "cosine" | |
| } |