vit5_motor_extractor

ViT5 Motor Extractor

Model Card for letran1110/vit5_motor_extractor

This is a fine-tuned ViT5 model for extracting motor specifications from raw text descriptions. The model is trained to take in noisy or unstructured motor-related information and output structured key-value pairs such as power, voltage, poles, protection class, and more.

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🧠 Model Details

• Model Type: T5ForConditionalGeneration
• Language(s): Vietnamese (primary), English (partially)
• Finetuned From: VietAI/vit5-base
• License: MIT
• Framework: 🤗 Transformers

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🔧 How to Use

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("letran1110/vit5_motor_extractor")
model = AutoModelForSeq2SeqLM.from_pretrained("letran1110/vit5_motor_extractor")

text = "Động cơ 3 pha 5.5kW, 4 cực, điện áp 380V, vỏ nhôm, bảo vệ IP55"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

✅ Intended Use

This model is designed to help extract structured information from motor specification descriptions (both Vietnamese and partial English), useful in:

• Inventory parsing

• Industrial cataloging

• Smart search & indexing for motor components

❌ Out-of-Scope Use

• Long-form document QA

• General conversation

• Image-based input (OCR must be done separately)

📚 Training

Dataset: Custom dataset crawled and annotated from motor product pages

Epochs: 10

Batch Size: 16

Max Length: 512

Optimizer: AdamW

🧪 Evaluation

Evaluation is manual by checking structured JSON outputs. Target fields include:

• motor_name
• power
• voltage
• poles
• protection
• frame_size
• shaft_diameter
• material

🤝 Citation

If you use this model, please cite the repo:

@misc{vit5motor2024,
  title={ViT5 Motor Extractor},
  author={letran1110},
  year={2024},
  howpublished={\url{https://huggingface.co/letran1110/vit5_motor_extractor}},
}