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") - 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
| { | |
| "bos_token": "<s>", | |
| "cls_token": "<s>", | |
| "eos_token": "</s>", | |
| "mask_token": { | |
| "content": "<mask>", | |
| "lstrip": true, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "pad_token": "<pad>", | |
| "sep_token": "</s>", | |
| "unk_token": "<unk>" | |
| } | |