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---
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. -->
## 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.
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