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 config_sentence_transformers.json from gsjang/text2sql_encoder: direct link, hf CLI and curl.
- Browser
- Download file 201 Bytes
-
https://huggingface.co/gsjang/text2sql_encoder/resolve/main/config_sentence_transformers.json
- Command line
-
hf download hf://gsjang/text2sql_encoder/config_sentence_transformers.json
-
curl -L -o config_sentence_transformers.json https://huggingface.co/gsjang/text2sql_encoder/resolve/main/config_sentence_transformers.json
201 Bytes
| { | |
| "__version__": { | |
| "sentence_transformers": "3.0.1", | |
| "transformers": "4.39.0", | |
| "pytorch": "2.0.1+cu117" | |
| }, | |
| "prompts": {}, | |
| "default_prompt_name": null, | |
| "similarity_fn_name": null | |
| } |