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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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4-bit precision
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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.

Recommendations

How to Get Started with the Model

Use the code below to get started with the model.

>>> 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

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.