Update README.md (#1)
Browse files- Update README.md (4c85ed6ab76c5318f7050916bb5b743d01a9caa2)
README.md
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---
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base_model: BAAI/bge-base-en-v1.5
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library_name: transformers.js
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---
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https://huggingface.co/BAAI/bge-base-en-v1.5 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/@
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```bash
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npm i @
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```
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You can then use the model to compute embeddings, as follows:
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```js
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import { pipeline } from '@
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// Create a feature-extraction pipeline
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const extractor = await pipeline('feature-extraction', 'Xenova/bge-base-en-v1.5');
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You can also use the model for retrieval. For example:
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```js
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import { pipeline, cos_sim } from '@
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// Create a feature-extraction pipeline
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const extractor = await pipeline('feature-extraction', 'Xenova/bge-small-en-v1.5');
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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`).
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---
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base_model: BAAI/bge-base-en-v1.5
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library_name: transformers.js
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license: mit
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---
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https://huggingface.co/BAAI/bge-base-en-v1.5 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/@huggingface/transformers) using:
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```bash
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npm i @huggingface/transformers
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```
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You can then use the model to compute embeddings, as follows:
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```js
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import { pipeline } from '@huggingface/transformers';
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// Create a feature-extraction pipeline
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const extractor = await pipeline('feature-extraction', 'Xenova/bge-base-en-v1.5');
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You can also use the model for retrieval. For example:
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```js
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import { pipeline, cos_sim } from '@huggingface/transformers';
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// Create a feature-extraction pipeline
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const extractor = await pipeline('feature-extraction', 'Xenova/bge-small-en-v1.5');
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// ]
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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`).
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