Text Classification
setfit
Safetensors
sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use gsjang/text2sql_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use gsjang/text2sql_encoder with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("gsjang/text2sql_encoder") - sentence-transformers
How to use gsjang/text2sql_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gsjang/text2sql_encoder") 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:
- e4f9598a7c7019aaa8c94faca752358ee6a2c4e5e91d798ef5ba8ac25de46f67
- Size of remote file:
- 442 MB
- SHA256:
- dbb9706817512b8a989191808cf5e3cdafa5248a7b966ebf54e85b5ccf5b7149
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