https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english with ONNX weights to be compatible with Transformers.js.
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).
<html> <head> <script type="module" crossorigin src="https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.js"></script> <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.css" /> </head> </html>
<gradio-lite>
<gradio-requirements> transformers_js_py </gradio-requirements>
<gradio-file name="app.py" entrypoint> from transformers_js import import_transformers_js import gradio as gr
transformers = await import_transformers_js() pipeline = transformers.pipeline pipe = await pipeline('sentiment-analysis', 'osanseviero/distilbert-base-uncased-finetuned-quantized')
async def classify(text): return await pipe(text)
demo = gr.Interface(classify, "textbox", "json") demo.launch() </gradio-file>
</gradio-lite>
