Update README.md

README.md

@@ -15,7 +15,6 @@ pipeline_tag: table-question-answering
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
 
 ## Model Details
 
@@ -26,12 +25,10 @@ This modelcard aims to be a base template for new models. It has been generated
 
 
 - **Developed by:** The Scamper
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
 - **Model type:** Transformer
 - **Language(s) (NLP):** Thai, English
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** OpenThaiGPT-1.0.0 70B (https://huggingface.co/openthaigpt/openthaigpt-1.0.0-70b-chat) 
+- **License:** apache-2.0
+- **Finetuned from model:** OpenThaiGPT-1.0.0 70B (https://huggingface.co/openthaigpt/openthaigpt-1.0.0-70b-chat) 
 
 
 ## Uses
@@ -70,13 +67,22 @@ Use the code below to get started with the model.
 
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 
-#### Preprocessing [optional]
+The methodology for fine-tuning involves a dataset with two columns: "question" and "SQL syntax". Here's a brief outline of the process:
 
-[More Information Needed]
+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.
 
-#### Training Hyperparameters
+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.
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+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.