metadata
license: apache-2.0
language:
- en
base_model:
- sentence-transformers/all-MiniLM-L6-v2
pipeline_tag: sentence-similarity
library_name: transformers.js
datasets:
- s2orc
- flax-sentence-embeddings/stackexchange_xml
- ms_marco
- gooaq
- yahoo_answers_topics
- code_search_net
- search_qa
- eli5
- snli
- multi_nli
- wikihow
- natural_questions
- trivia_qa
- embedding-data/sentence-compression
- embedding-data/flickr30k-captions
- embedding-data/altlex
- embedding-data/simple-wiki
- embedding-data/QQP
- embedding-data/SPECTER
- embedding-data/PAQ_pairs
- embedding-data/WikiAnswers
tags:
- feature-extraction
https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2 with ONNX weights to be compatible with Transformers.js.
Usage (Transformers.js)
If you haven't already, you can install the Transformers.js JavaScript library from NPM using:
npm i @huggingface/transformers
You can then use the model to compute embeddings like this:
import { pipeline } from '@huggingface/transformers';
// Create a feature-extraction pipeline
const extractor = await pipeline('feature-extraction', 'onnx-community/all-MiniLM-L6-v2-ONNX');
// Compute sentence embeddings
const sentences = ['This is an example sentence', 'Each sentence is converted'];
const output = await extractor(sentences, { pooling: 'mean', normalize: true });
console.log(output);
// Tensor {
// dims: [ 2, 384 ],
// type: 'float32',
// data: Float32Array(768) [ 0.04592696577310562, 0.07328180968761444, ... ],
// size: 768
// }
You can convert this Tensor to a nested JavaScript array using .tolist():
console.log(output.tolist());
// [
// [ 0.04592696577310562, 0.07328180968761444, 0.05400655046105385, ... ],
// [ 0.08188057690858841, 0.10760223120450974, -0.013241755776107311, ... ]
// ]