https://huggingface.co/thenlper/gte small with ONNX weights to be compatible with Transformers.js. Usage (Transformers.js) If you haven't already, you can install the Transformers.js JavaScript library from NPM using: You can then use the model to compute embeddings like this: You can convert this Tensor to a nested JavaScript array using .tolist() : By default, an 8 bit quantized version of the model is used, but you can choose to use the full precision (fp32) version by specifying { dtype: 'fp32' } in the pipeline function: Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx ).
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