par Xenova
Open source · 153k downloads · 14 likes
Le modèle BGE Small en v1.5 est une version optimisée et légère du modèle BAAI BGE, spécialement conçue pour générer des embeddings textuels de haute qualité en anglais. Il excelle dans des tâches comme la recherche d'informations, la classification de textes ou la comparaison sémantique, grâce à sa capacité à capturer finement les nuances du langage. Ses principaux atouts résident dans son efficacité et sa rapidité, tout en offrant des performances comparables à des modèles plus lourds. Idéal pour des applications nécessitant des embeddings compacts et performants, il se distingue par sa compatibilité avec les environnements web via des poids ONNX, facilitant son intégration dans des projets JavaScript. Son approche équilibrée entre taille réduite et précision en fait un choix polyvalent pour les développeurs et chercheurs.
https://huggingface.co/BAAI/bge-small-en-v1.5 with ONNX weights to be compatible with 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, as follows:
import { pipeline } from '@huggingface/transformers';
// Create a feature-extraction pipeline
const extractor = await pipeline('feature-extraction', 'Xenova/bge-small-en-v1.5');
// Compute sentence embeddings
const texts = ['Hello world.', 'Example sentence.'];
const embeddings = await extractor(texts, { pooling: 'mean', normalize: true });
console.log(embeddings);
// Tensor {
// dims: [ 2, 384 ],
// type: 'float32',
// data: Float32Array(768) [ -0.04314826801419258, -0.029488801956176758, ... ],
// size: 768
// }
console.log(embeddings.tolist()); // Convert embeddings to a JavaScript list
// [
// [ -0.04314826801419258, -0.029488801956176758, 0.027080481871962547, ... ],
// [ -0.03605496883392334, 0.01643390767276287, 0.008982205763459206, ... ]
// ]
You can also use the model for retrieval. For example:
import { pipeline, cos_sim } from '@huggingface/transformers';
// Create a feature-extraction pipeline
const extractor = await pipeline('feature-extraction', 'Xenova/bge-small-en-v1.5');
// List of documents you want to embed
const texts = [
'Hello world.',
'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.',
'I love pandas so much!',
];
// Compute sentence embeddings
const embeddings = await extractor(texts, { pooling: 'mean', normalize: true });
// Prepend recommended query instruction for retrieval.
const query_prefix = 'Represent this sentence for searching relevant passages: '
const query = query_prefix + 'What is a panda?';
const query_embeddings = await extractor(query, { pooling: 'mean', normalize: true });
// Sort by cosine similarity score
const scores = embeddings.tolist().map(
(embedding, i) => ({
id: i,
score: cos_sim(query_embeddings.data, embedding),
text: texts[i],
})
).sort((a, b) => b.score - a.score);
console.log(scores);
// [
// { id: 1, score: 0.7995888037433755, text: 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.' },
// { id: 2, score: 0.6911046766159414, text: 'I love pandas so much!' },
// { id: 0, score: 0.39066192695524765, text: 'Hello world.' }
// ]
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).