Instructions to use osanseviero/distilbert-base-uncased-finetuned-quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use osanseviero/distilbert-base-uncased-finetuned-quantized with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('text-classification', 'osanseviero/distilbert-base-uncased-finetuned-quantized');
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).
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()
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