| # t5_wikisql_SQL2en | |
| --- | |
| language: en | |
| datasets: | |
| - wikisql | |
| --- | |
| This is a [t5-small](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned version on the [wikisql dataset](https://huggingface.co/datasets/wikisql) for **SQL** to **English** **translation** text2text mission. | |
| To load the model: | |
| (necessary packages: !pip install transformers sentencepiece) | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelWithLMHead | |
| tokenizer = AutoTokenizer.from_pretrained("dbernsohn/t5_wikisql_SQL2en") | |
| model = AutoModelWithLMHead.from_pretrained("dbernsohn/t5_wikisql_SQL2en") | |
| ``` | |
| You can then use this model to translate SQL queries into plain english. | |
| ```python | |
| query = "SELECT people FROM peoples where age > 10" | |
| input_text = f"translate SQL to English: {query} </s>" | |
| features = tokenizer([input_text], return_tensors='pt') | |
| output = model.generate(input_ids=features['input_ids'].cuda(), | |
| attention_mask=features['attention_mask'].cuda()) | |
| tokenizer.decode(output[0]) | |
| ``` | |
| The whole training process and hyperparameters are in my [GitHub repo](https://github.com/DorBernsohn/SQLM) | |
| > Created by [Dor Bernsohn](https://www.linkedin.com/in/dor-bernsohn-70b2b1146/) | |