| ## Typesense Public Embedding Models | |
| We store our current supported embedding models in this repo and you can also convert your own models to ONNX format and create a PR to add it to our supported models list. | |
| ### Convert a model to ONNX format | |
| #### Converting a Hugging Face Transformers Model | |
| You can follow instructions from [this link](https://huggingface.co/docs/transformers/serialization#export-to-onnx) to convert any model from Hugging Face to ONNX format using ```optimum-cli```. | |
| #### Converting a PyTorch Model | |
| You can use ```torch.onnx``` [APIs](https://pytorch.org/docs/stable/onnx.html) to convert PyTorch models to ONNX. | |
| #### Converting a Tensorflow Model | |
| You can use ```tf2onnx``` [tool](https://onnxruntime.ai/docs/tutorials/tf-get-started.html#getting-started-converting-tensorflow-to-onnx) to convert Tensorflow models to ONNX. | |
| ### Creating model config | |
| Before creating a PR with your ONNX model, you should store model file, vocab file and model config file under a folder with model name. Your model config must be named as ```config.json``` and should contain those keys: | |
| | Key | Description | Optional | | |
| |-----|-------------|----------| | |
| |model_md5| MD5 checksum of model file as string| No | | |
| |vocab_md5| MD5 checksum of vocab file as string| No | | |
| |model_type| Model type (currently only ```bert``` and ```xlm_roberta``` supported)| No | | |
| |vocab_file_name| File name of vocab file| No | | |
| |indexing_prefix| Prefix to be added before embedding documents| Yes | | |
| |query_prefix| Prefix to be added before embedding queries | Yes | | |