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--- |
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library_name: transformers.js |
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--- |
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https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-4-v2 with ONNX weights to be compatible with Transformers.js. |
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## Usage (Transformers.js) |
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If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) using: |
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```bash |
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npm i @xenova/transformers |
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``` |
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**Example:** Information Retrieval w/ `Xenova/ms-marco-MiniLM-L-4-v2`. |
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```js |
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import { AutoTokenizer, AutoModelForSequenceClassification } from '@xenova/transformers'; |
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const model = await AutoModelForSequenceClassification.from_pretrained('Xenova/ms-marco-MiniLM-L-4-v2'); |
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const tokenizer = await AutoTokenizer.from_pretrained('Xenova/ms-marco-MiniLM-L-4-v2'); |
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const features = tokenizer( |
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['How many people live in Berlin?', 'How many people live in Berlin?'], |
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{ |
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text_pair: [ |
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'Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', |
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'New York City is famous for the Metropolitan Museum of Art.', |
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], |
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padding: true, |
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truncation: true, |
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} |
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) |
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const scores = await model(features) |
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console.log(scores); |
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// quantized: [ 9.241240501403809, -11.621903419494629 ] |
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// unquantized: [ 9.238697052001953, -11.619404792785645 ] |
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``` |
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--- |
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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`). |
