Instructions to use letran1110/vit5_motor_extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use letran1110/vit5_motor_extractor with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="letran1110/vit5_motor_extractor")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use letran1110/vit5_motor_extractor with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "letran1110/vit5_motor_extractor"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "letran1110/vit5_motor_extractor",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/letran1110/vit5_motor_extractor

SGLang

How to use letran1110/vit5_motor_extractor with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "letran1110/vit5_motor_extractor" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "letran1110/vit5_motor_extractor",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "letran1110/vit5_motor_extractor" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "letran1110/vit5_motor_extractor",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use letran1110/vit5_motor_extractor with Docker Model Runner:
```
docker model run hf.co/letran1110/vit5_motor_extractor
```

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

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.

🧠 Model Details

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

🔧 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}},
}

Downloads last month: 2

Safetensors

Model size

0.2B params

Tensor type

F32

letran1110
/

vit5_motor_extractor

ViT5 Motor Extractor

Model Card for `letran1110/vit5_motor_extractor`

🧠 Model Details

🔧 How to Use

✅ Intended Use

❌ Out-of-Scope Use

📚 Training

🧪 Evaluation

🤝 Citation

Space using letran1110/vit5_motor_extractor 1

ViT5 Motor Extractor

Model Card for letran1110/vit5_motor_extractor

🧠 Model Details

🔧 How to Use

✅ Intended Use

❌ Out-of-Scope Use

📚 Training

🧪 Evaluation

🤝 Citation

Space using letran1110/vit5_motor_extractor 1

Model Card for `letran1110/vit5_motor_extractor`