Text Classification
setfit
Safetensors
sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use mini1013/master_cate_sl11 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use mini1013/master_cate_sl11 with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("mini1013/master_cate_sl11") - sentence-transformers
How to use mini1013/master_cate_sl11 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mini1013/master_cate_sl11") 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
- Xet hash:
- ca5246f2fc6b84f49c539d2f53b31b96257fa7fab00124cbdb996ec0036abe57
- Size of remote file:
- 31.6 kB
- SHA256:
- 5412873e7698ddab4a7dc8216b78d5ff58a68fd4c446f202f7465311d70e305b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.