| library_name: "transformers.js" | |
| 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](https://huggingface.co/docs/optimum/index) 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> |