textforge 0.1.2

Automated Synthetic Data Generation, Distillation, Quantization and Deployment Pipeline.

Infero

Overview

TextForge automates model distillation, training, quantization, and deployment for text classification. It simplifies synthetic data generation, model optimization using ONNX runtime, and FastAPI serving.

Features

• Automated synthetic data generation
• Transformer model training
• ONNX conversion with 8-bit quantization
• Automated model API serving with FastAPI

Installation

pip install textforge

Usage

import pandas as pd
from textforge.pipeline import Pipeline, PipelineConfig

pipeline_config = PipelineConfig(
    api_key=api_key,
    labels=['business','education','entertainment','sports','technology'],
    query="Classify based on headlines",
    save_steps=200,
    eval_steps=200,
    epochs=10
)

df = pd.read_csv('data.csv')

pipeline = Pipeline(pipeline_config)

pipeline.run(data=df, save=True, serve=True)

License

This project is licensed under the MIT License - see the LICENSE file for details.