Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use JohanHeinsen/ENO_Runaway_Advertisement_classifier_2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use JohanHeinsen/ENO_Runaway_Advertisement_classifier_2.0 with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("JohanHeinsen/ENO_Runaway_Advertisement_classifier_2.0") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - sentence-transformers
How to use JohanHeinsen/ENO_Runaway_Advertisement_classifier_2.0 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("JohanHeinsen/ENO_Runaway_Advertisement_classifier_2.0") 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 model.safetensors from JohanHeinsen/ENO_Runaway_Advertisement_classifier_2.0: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/JohanHeinsen/ENO_Runaway_Advertisement_classifier_2.0/resolve/main/model.safetensors
- Command line
-
hf download hf://JohanHeinsen/ENO_Runaway_Advertisement_classifier_2.0/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/JohanHeinsen/ENO_Runaway_Advertisement_classifier_2.0/resolve/main/model.safetensors
438 MB
- Xet hash:
- cc31049436333b40450693e53ab34e794fcaa80fdee4d37edcc855d05bb73f16
- Size of remote file:
- 438 MB
- SHA256:
- 9d7272d77af10a7583d669cc13e72e56c5d58fbf64a10dcfaee90b9fcb988f0c
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