Instructions to use shubh7/T5-Small-FineTuned-TexttoSql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shubh7/T5-Small-FineTuned-TexttoSql with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("t5-small") model = PeftModel.from_pretrained(base_model, "shubh7/T5-Small-FineTuned-TexttoSql") - Notebooks
- Google Colab
- Kaggle
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Download README.md from shubh7/T5-Small-FineTuned-TexttoSql: direct link, hf CLI and curl.
- Browser
- Download file 953 Bytes
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https://huggingface.co/shubh7/T5-Small-FineTuned-TexttoSql/resolve/main/README.md
- Command line
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hf download hf://shubh7/T5-Small-FineTuned-TexttoSql/README.md
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curl -L -o README.md https://huggingface.co/shubh7/T5-Small-FineTuned-TexttoSql/resolve/main/README.md
953 Bytes
metadata
language: en
license: apache-2.0
tags:
- t5
- text-to-sql
- lora
- peft
datasets:
- your_dataset_here
metrics:
- bleu
- rouge
T5-Small Fine-Tuned for Text-to-SQL with LoRA
This model is a fine-tuned version of t5-small on text-to-SQL data using LoRA (Low-Rank Adaptation).
Model description
The model was trained using PEFT's LoRA implementation with the following configuration:
- rank (r): 8
- lora_alpha: 32
- Target modules: query and value projection matrices in attention
Intended uses & limitations
This model is intended to be used for converting natural language queries to SQL statements.
Training procedure
The model was trained using the following hyperparameters:
- Learning rate: 1e-3
- Batch size: 8
- Training steps: 10,000
- Optimizer: AdamW
- LR scheduler: constant
Limitations and bias
This model inherits the limitations and biases from the original T5-small model.