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The_Scamper-opt70bqt

Table Question Answering
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Safetensors
Thai
English
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text-generation
text-generation-inference
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metadata
license: apache-2.0
language:
  - th
  - en
metrics:
  - accuracy
datasets:
  - AIAT/The_Scamper-train
pipeline_tag: table-question-answering

Model Card for Model ID

Scamper

Model Details

Model Description

Uses

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")

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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

[More Information Needed]

Training Procedure

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.