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


---
# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

![Scamper](https://media.discordapp.net/attachments/1235121514211184663/1235848951626993775/logo3.png?ex=6635dd87&is=66348c07&hm=d2e9d93fa80177448d3e85f27e52d272050517fc63d18616e06e364d32b87014&=&format=webp&quality=lossless&width=1138&height=1138)
## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **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

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

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.



### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->



## How to Get Started with the Model

Use the code below to get started with the model.

```python
>>> 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")
```

## Training Details

### Training Data

Dataset: https://huggingface.co/datasets/AIAT/The_Scamper-train

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

The methodology for fine-tuning involves a dataset with three columns: "instruction", "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.

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.