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