Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
text-embeddings-inference
Instructions to use stephen-solka/feed-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use stephen-solka/feed-classifier with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("stephen-solka/feed-classifier") - sentence-transformers
How to use stephen-solka/feed-classifier with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("stephen-solka/feed-classifier") 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
| { | |
| "transformer_task": "feature-extraction", | |
| "modality_config": { | |
| "text": { | |
| "method": "forward", | |
| "method_output_name": "last_hidden_state" | |
| } | |
| }, | |
| "module_output_name": "token_embeddings" | |
| } |