Instructions to use qubing/text2sql_retrieval with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use qubing/text2sql_retrieval with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="qubing/text2sql_retrieval")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("qubing/text2sql_retrieval") model = AutoModelForSequenceClassification.from_pretrained("qubing/text2sql_retrieval") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:479c766da6ebc923da767a8363629e1c9bc32727d367290b25bd2739e8265155
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size 1421495416
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