Text Classification
setfit
Safetensors
sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
🇪🇺 Region: EU
Instructions to use CabraVC/emb_classifier_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use CabraVC/emb_classifier_model with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("CabraVC/emb_classifier_model") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - sentence-transformers
How to use CabraVC/emb_classifier_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("CabraVC/emb_classifier_model") 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 sentence_bert_config.json from CabraVC/emb_classifier_model: direct link, hf CLI and curl.
- Browser
- Download file 53 Bytes
-
https://huggingface.co/CabraVC/emb_classifier_model/resolve/5af94b1100785260e1c2e331eb58bca9eee572a6/sentence_bert_config.json
- Command line
-
hf download hf://CabraVC/emb_classifier_model@5af94b1100785260e1c2e331eb58bca9eee572a6/sentence_bert_config.json
-
curl -L -o sentence_bert_config.json https://huggingface.co/CabraVC/emb_classifier_model/resolve/5af94b1100785260e1c2e331eb58bca9eee572a6/sentence_bert_config.json
53 Bytes
| { | |
| "max_seq_length": 512, | |
| "do_lower_case": false | |
| } |