README.md

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Download pre-trained models

🛠️ Hugging Face to ONNX Converter

models/models_to_onnx.py

This is a unified Python script to convert Hugging Face models into the ONNX format for optimized inference.

The script handles two main use cases:

1. Feature extraction models (e.g., sentence-transformers).
2. Token classification models (e.g., Named Entity Recognition - NER).

It automatically downloads the model and organizes the exported files in a structured subdirectory.

Requirements

Before running the script, make sure you have the following Python packages installed:

pip install torch transformers onnx onnxruntime optimum

🔧 How to Use

Argument	Description
-m / --model	Hugging Face model name (e.g., dslim/bert-base-NER)
-o / --output	Output folder name
--mode	feature or token (default: token)
--base_dir	Base save directory (default: ./models)

Examples

python models/model_to_onnx.py -m="dbmdz/bert-large-cased-finetuned-conll03-english" -o="bert-large-cased-finetuned-conll03-english"

python models/model_to_onnx.py -m="sentence-transformers/all-MiniLM-L6-v2" -o="sentence-transformers/all-MiniLM-L6-v2"

Output

./models/
  ├── model_to_onnx.py 
  ├── sentence-transformers/all-MiniLM-L6-v2/ 
  │    ├── model.onnx (via optimum)
  │    └── tokenizer/ 
  └── dslim/bert-base-NER/  
       ├── model.onnx  
       ├── label_map.json  
       └── tokenizer/ 

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