---
language:
- en
pipeline_tag: text2text-generation
metrics:
- f1
tags:
- SQL
- plSQL
- english
---

This is a fine-tuned version of T5 FLAN LARGE (783M) on English in particular on the public dataset spider for text-toSQL.

To initialize the model:

    
    from transformers import T5ForConditionalGeneration
    model = T5ForConditionalGeneration.from_pretrained("MRNH/flan-t5-large-PLsql")
    
    
Use the tokenizer:

    
    tokenizer = T5ForConditionalGeneration.from_pretrained("MRNH/flan-t5-large-PLsql")
    
    input = tokenizer("<question> "+sentence["db_id"]+" </question> "+sentence["question"],
                      text_target=sentence["query"], return_tensors='pt')

To generate text using the model:

    output = model.generate(input["input_ids"],attention_mask=input["attention_mask"])

Training of the model is performed using the following loss computation based on the hidden state output h:

    h.logits, h.loss = model(input_ids=input["input_ids"],
                                                  attention_mask=input["attention_mask"],
                                                  labels=input["labels"])