bge base en v1.5
Overview
Highlights
- Optimized for high-accuracy semantic search and RAG pipelines
- Efficient feature extraction with low inference latency
- Strong performance in document retrieval and ranking tasks
- Lightweight architecture compatible with most vector databases
- Permissive MIT license for flexible commercial deployment
Usage
# Install Hugging Face transformers
pip install transformers torch
# Load model with transformers
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("Xenova/bge-base-en-v1.5")
tokenizer = AutoTokenizer.from_pretrained("Xenova/bge-base-en-v1.5")
Hugging Face Download
We recommend downloading the model via the Hugging Face CLI or Hub SDK.
Guidance:Before downloading, install huggingface_hub with:
pip install -U huggingface_hub
CLI Download
Download the full repository
huggingface-cli download Xenova/bge-base-en-v1.5
Download a single file to a local folder (e.g. config.json into ./dir)
huggingface-cli download Xenova/bge-base-en-v1.5 config.json --local-dir ./dir
See the official docs for more CLI options
SDK Download
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('Xenova/bge-base-en-v1.5')
Git Download
Make sure git-lfs is installed first
git lfs install
git clone https://huggingface.co/Xenova/bge-base-en-v1.5
To skip LFS large-file downloads, use:
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/Xenova/bge-base-en-v1.5
Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.
PyTorch / Transformers Usage
Install Transformers
pip install -U transformers torch
Load the model and run inference
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained('Xenova/bge-base-en-v1.5')
tokenizer = AutoTokenizer.from_pretrained('Xenova/bge-base-en-v1.5')
Model Download
We recommend downloading the model via the ModelScope CLI or SDK.
Guidance:Before downloading, install ModelScope with:
pip install modelscope
CLI Download
Download the full repository
modelscope download --model Xenova/bge-base-en-v1.5
Download a single file to a local folder (e.g. README.md into ./dir)
modelscope download --model Xenova/bge-base-en-v1.5 README.md --local_dir ./dir
See the docs for more CLI options
SDK Download
# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('Xenova/bge-base-en-v1.5')
Git Download
Make sure git-lfs is installed first
git lfs install
git clone https://www.modelscope.cn/Xenova/bge-base-en-v1.5.git
To skip LFS large-file downloads, use:
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/Xenova/bge-base-en-v1.5.git
ModelScope 模型页直接下载模型文件;无需将模型文件放在本站服务器。
Notebook Quickstart
Install the ModelScope library
pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
Load the model and run inference
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
p = pipeline('text-generation', 'Xenova/bge-base-en-v1.5')
Full Documentation
---
base_model: BAAI/bge-base-en-v1.5
library_name: transformers.js
license: mit
---
https://huggingface.co/BAAI/bge-base-en-v1.5 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/transformersYou 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-base-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, 768 ],
// type: 'float32',
// data: Float32Array(1536) [ 0.019079938530921936, 0.041718777269124985, ... ],
// size: 1536
// }
console.log(embeddings.tolist()); // Convert embeddings to a JavaScript list
// [
// [ 0.019079938530921936, 0.041718777269124985, 0.037672195583581924, ... ],
// [ 0.020936904475092888, 0.020080938935279846, -0.00787576474249363, ... ]
// ]
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-base-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: 0.7787772374597298, text: 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.' },
// { id: 2, score: 0.7071589521880506, text: 'I love pandas so much!' },
// { id: 0, score: 0.4252782730390429, 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).