Text Classification
setfit
Safetensors
sentence-transformers
modernbert
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use johnpaulbin/toxicity-setfit-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use johnpaulbin/toxicity-setfit-2 with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("johnpaulbin/toxicity-setfit-2") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - sentence-transformers
How to use johnpaulbin/toxicity-setfit-2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("johnpaulbin/toxicity-setfit-2") 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 tokenizer.json from johnpaulbin/toxicity-setfit-2: direct link, hf CLI and curl.
- Browser
- Download file 34.4 MB
-
https://huggingface.co/johnpaulbin/toxicity-setfit-2/resolve/main/tokenizer.json
- Command line
-
hf download hf://johnpaulbin/toxicity-setfit-2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/johnpaulbin/toxicity-setfit-2/resolve/main/tokenizer.json
34.4 MB
- Xet hash:
- 33cd89ff8b53fbb07f89aa282b3d5bd5f5d7de3a2cdeca8540ee80380a8d3c1c
- Size of remote file:
- 34.4 MB
- SHA256:
- 8bd47075711f75a143d1b78e01a41cc65c1c591b00d3cfeffc23db07adce1392
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