Instructions to use vvn/Text_to_SQL_BART_spider-three-ep with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vvn/Text_to_SQL_BART_spider-three-ep with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("vvn/Text_to_SQL_BART_spider-three-ep") model = AutoModelForSeq2SeqLM.from_pretrained("vvn/Text_to_SQL_BART_spider-three-ep", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| BART Large CNN model trained for converting NLP queries to SQL queries. | |
| The model was trained on the Spider dataset Link: https://yale-lily.github.io/spider | |
| The model was trained using Google colab. | |
| Hyperparameters: | |
| "num epochs" = 3 | |
| "learning rate" = 1e-5 | |
| "batch size" = 8 | |
| "weight decay" = 0.01 | |
| "max input length" = 256 | |
| "max target length" = 256 | |
| "model name" : "facebook/bart-large-cnn" | |
| Link to the github repo containing training notebook: https://github.com/vanadnarayane26/Text_to_SQL_Spider- |