usage.md

Steel Material Classification Model

Quick Start

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model
model_name = "your-username/steel-material-classifier"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Predict
text = "철광석을 고로에서 환원하여 선철을 제조하는 과정"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)

with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predicted_class = torch.argmax(predictions, dim=1).item()

label = model.config.id2label[predicted_class]
confidence = predictions[0][predicted_class].item()
print(f"Predicted: {label} (Confidence: {confidence:.4f})")

Model Information

• Base Model: XLM-RoBERTa
• Task: Sequence Classification
• Labels: 66 steel industry materials
• Languages: Korean, English
• Model Size: ~1GB

Supported Labels

The model can classify 66 different steel industry materials including:

• Raw Materials: 철광석, 석회석, 석유 코크스, 무연탄, 갈탄
• Fuels: 천연가스, 액화천연가스, 경유, 휘발유, 등유
• Gases: 일산화탄소, 메탄, 에탄, 고로가스, 코크스 오븐 가스
• Products: 강철, 선철, 철, 열간성형철 (HBI), 고온 성형 환원철
• By-products: 고로 슬래그, 압연 스케일, 분진, 슬러지, 절삭칩
• Others: 전기, 냉각수, 윤활유, 포장재, 열유입

Performance

• Label Independence: Good (average similarity: 0.1166)
• Orthogonality: Good (average dot product: 0.2043)
• Overall Assessment: The model shows good separation between different material categories

Usage Examples

Single Prediction

text = "천연가스를 연료로 사용하여 고로를 가열"
# Returns: "천연가스" with confidence score

Batch Prediction

texts = [
    "철광석을 고로에서 환원하여 선철을 제조하는 과정",
    "석회석을 첨가하여 슬래그를 형성"
]
# Returns: ["철광석", "석회석"] with confidence scores

Installation

pip install torch transformers

License

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Citation

If you use this model in your research, please cite:

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