Text Classification
setfit
PyTorch
sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use gsjang/sroberta-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use gsjang/sroberta-embedding with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("gsjang/sroberta-embedding") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - sentence-transformers
How to use gsjang/sroberta-embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gsjang/sroberta-embedding") 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_head.pkl from gsjang/sroberta-embedding: direct link, hf CLI and curl.
- Browser
- Download file 7.04 kB
-
https://huggingface.co/gsjang/sroberta-embedding/resolve/main/model_head.pkl
- Command line
-
hf download hf://gsjang/sroberta-embedding/model_head.pkl
-
curl -L -o model_head.pkl https://huggingface.co/gsjang/sroberta-embedding/resolve/main/model_head.pkl
7.04 kB
- Xet hash:
- 96ae65cc39072b1d773deda3acb0833e909db56855a1ebd158d2213e958dcc05
- Size of remote file:
- 7.04 kB
- SHA256:
- 842abcdf020371c8af9d2105a22e721863f6a3985b0dc6c3b859b684fb8daa2b
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