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 1_Pooling/config.json from TinyModels/Setfit-Banking-Spam: direct link, hf CLI and curl.
- Browser
- Download file 89 Bytes
-
https://huggingface.co/TinyModels/Setfit-Banking-Spam/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://TinyModels/Setfit-Banking-Spam/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/TinyModels/Setfit-Banking-Spam/resolve/main/1_Pooling/config.json
89 Bytes
| { | |
| "embedding_dimension": 384, | |
| "pooling_mode": "cls", | |
| "include_prompt": true | |
| } |