Text Ranking
Transformers.js
ONNX
sentence-transformers
xlm-roberta
text-classification
text-embeddings-inference
Instructions to use Xenova/bge-reranker-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use Xenova/bge-reranker-base with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('text-ranking', 'Xenova/bge-reranker-base'); - sentence-transformers
How to use Xenova/bge-reranker-base with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("Xenova/bge-reranker-base") query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores) - Notebooks
- Google Colab
- Kaggle
File size: 1,135 Bytes
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base_model: BAAI/bge-reranker-base
library_name: transformers.js
tags:
- sentence-transformers
pipeline_tag: text-ranking
---
https://huggingface.co/BAAI/bge-reranker-base with ONNX weights to be compatible with Transformers.js.
## Usage (Transformers.js)
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:
```bash
npm i @huggingface/transformers
```
**Example:** Text classification using a reranker model.
```js
import { pipeline } from '@huggingface/transformers';
const classifier = await pipeline('text-classification', 'Xenova/bge-reranker-base');
const output = await classifier('I love transformers!');
```
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`). |