Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use JordanTallon/Unifeed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use JordanTallon/Unifeed with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("JordanTallon/Unifeed") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - sentence-transformers
How to use JordanTallon/Unifeed with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("JordanTallon/Unifeed") 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
Commit History
-
Update 1_Pooling/config.json
Verified signaturedab572d -
Update 1_Pooling/config.json
Verified signaturee9f924f -
Push model using huggingface_hub.
Verified signature2728b49
-
Push model using huggingface_hub.
Verified signature2a48c96
-
Push model using huggingface_hub.
Verified signature5b3ddba -
Upload tokenizer
Verified signature03443aa -
Upload DistilBertForSequenceClassification
Verified signature575b85b -
Upload DistilBertForSequenceClassification
Verified signatureae6d76d -
initial commit
Verified signature37c43d5