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.