Text Classification
setfit
Safetensors
sentence-transformers
multilingual
xlm-roberta
text-embeddings-inference
Instructions to use automatedMotiveCoder/setfit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use automatedMotiveCoder/setfit with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("automatedMotiveCoder/setfit") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - sentence-transformers
How to use automatedMotiveCoder/setfit with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("automatedMotiveCoder/setfit") 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 model_head.pkl from automatedMotiveCoder/setfit: direct link, hf CLI and curl.
- Browser
- Download file 35.8 kB
-
https://huggingface.co/automatedMotiveCoder/setfit/resolve/main/model_head.pkl
- Command line
-
hf download hf://automatedMotiveCoder/setfit/model_head.pkl
-
curl -L -o model_head.pkl https://huggingface.co/automatedMotiveCoder/setfit/resolve/main/model_head.pkl
35.8 kB
- Xet hash:
- 4d6bac262339ac3fe8c76d33ed1059ececf94b475297244f72df84c4277af4e7
- Size of remote file:
- 35.8 kB
- SHA256:
- 13e406ef1f1d9816c37240c218b0096fbca62a3b2610bb1ac85cc701954d8519
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.