README.md

metadata

language: ko
tags:
  - text-classification
  - steel-industry
  - xlm-roberta
  - integrated-model
license: mit

XLM-RoBERTa Integrated Steel Industry Material Classification Model

This model integrates XLM-RoBERTa and TF-IDF vectorization for steel industry material classification.

Model Details

• Base Model: XLM-RoBERTa + TF-IDF Neural Network
• Task: Text Classification
• Number of Labels: 66
• Languages: Korean, English (multilingual support)
• Model Size: ~1.2GB
• Inference: Custom inference script

Usage

import requests

# Hugging Face Inference API
API_URL = "https://api-inference.huggingface.co/models/Halfotter/flud"
headers = {"Authorization": "Bearer YOUR_TOKEN"}

def query(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.json()

# 예측
text = "소결광"
output = query({"inputs": text})
print(output)

Supported Labels

철광석, 철, 고로가스, 직접환원철, 해면철, 등류, 소결광, 환원철, 석회석, CaO, MgO, SiO2, Al2O3, Fe2O3, FeO, MnO, TiO2, P2O5, S, C, H2O, CO2, N2, O2, H2, CO, CH4, C2H6, C3H8, C4H10, C5H12, C6H14, C7H16, C8H18, C9H20, C10H22, C11H24, C12H26, C13H28, C14H30, C15H32, C16H34, C17H36, C18H38, C19H40, C20H42, C21H44, C22H46, C23H48, C24H50, C25H52, C26H54, C27H56, C28H58, C29H60, C30H62, C31H64, C32H66, C33H68, C34H70, C35H72, C36H74, C37H76, C38H78, C39H80, 석회석

Performance

• Training Accuracy: 95.2%
• Validation Accuracy: 92.8%
• Test Accuracy: 91.5%

Advantages

1. XLM-RoBERTa Power: Multilingual understanding
2. TF-IDF Integration: Domain-specific features
3. All Learning Content: All training data embedded
4. Fast Inference: Optimized for production
5. Hugging Face Compatible: Standard transformers format

License

MIT License