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") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - 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
Download model_head.pkl from gsjang/text2sql_encoder: direct link, hf CLI and curl.
- Browser
- Download file 179 kB
-
https://huggingface.co/gsjang/text2sql_encoder/resolve/main/model_head.pkl
- Command line
-
hf download hf://gsjang/text2sql_encoder/model_head.pkl
-
curl -L -o model_head.pkl https://huggingface.co/gsjang/text2sql_encoder/resolve/main/model_head.pkl
179 kB
- Xet hash:
- 76e08ab2495c0e708078693bb1d53e2577371987bd24faeab4810a73340522a7
- Size of remote file:
- 179 kB
- SHA256:
- 66db4adc8c9379e8a1ca4fdd56a8bc48ee7b74575c3d9c560df1638cd216b09a
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