---
license: apache-2.0
language:
- th
- en
metrics:
- accuracy
datasets:
- AIAT/The_Scamper-train
pipeline_tag: table-question-answering


---
# Model Card for Model ID

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## Model Details

### Model Description

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- **Developed by:** The Scamper
- **Model type:** Transformer
- **Language(s) (NLP):** Thai, English
- **License:** apache-2.0
- **Finetuned from model:** OpenThaiGPT-1.0.0 70B (https://huggingface.co/openthaigpt/openthaigpt-1.0.0-70b-chat) 


## Uses

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The Tubular Question Answering Large Language Model is based on OpenThaiGPT and fine-tuned for converting natural language questions into SQL queries. It learns to map the nuances of Thai language to SQL structures, enabling efficient retrieval of information from databases.

model2_path ="AIAT/The_Scamper-opt70bqt"
tokenizer = AutoTokenizer.from_pretrained(model2_path, padding_side="right",use_fast=False)
model = AutoModelForCausalLM.from_pretrained(model2_path,
                                             device_map="auto")


### Recommendations

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## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

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[More Information Needed]

### Training Procedure

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The methodology for fine-tuning involves a dataset with two columns: "question" and "SQL syntax". Here's a brief outline of the process:

1. **Data Collection**: Gather a dataset containing pairs of questions and their corresponding SQL queries. Ensure the questions cover various topics and query types, while the SQL queries represent the desired actions on a database.

2. **Pre-processing**: Clean and preprocess the data to remove noise, standardize formatting, and handle any inconsistencies. Tokenize the text and encode it into a format suitable for training.

3. **Model Architecture**: Utilize OpenThaiGPT 1.0.0 70B as the base model. 

4. **Fine-tuning Setup**: Divide the dataset into training (90%) and test sets (10%). We define the training procedure, including hyperparameters such as learning rate, batch size, and number of training epochs.

5. **Fine-tuning Process**: Train the model on the question-SQL pairs using the defined setup. During training, the model learns to predict the SQL query corresponding to a given question by minimizing a suitable loss function.

6. **Testing**: Evaluate the final model on a held-out test set to assess its generalization performance on unseen data.

7. **Deployment**: Deploy the fine-tuned model for text-to-SQL tasks in real-world applications, where it can generate SQL queries from natural language questions effectively and efficiently.

By following this methodology, the model can be fine-tuned to accurately convert natural language questions into SQL syntax, enabling seamless interaction with structured databases.