Instructions to use pardeephck/dynamicsqlgenerator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pardeephck/dynamicsqlgenerator with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("pardeephck/dynamicsqlgenerator") model = AutoModelForSeq2SeqLM.from_pretrained("pardeephck/dynamicsqlgenerator", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| description: This model generate sql query on demand from a table with schema details provided in parameter. In the next version this will be updated to compute from multiple tables with dynamically join between required tables. | |
| datasets: | |
| - wikisql | |
| ```python | |
| This model generate sql query from a table with schema details provided in parameter | |
| ``` |