AnglE

// Overview

AnglE (angle_emb) is a Python library for training and running sentence embedding models. It comes from the paper "AnglE: Angle-optimized Text Embeddings" and lets you train BERT- or LLM-based embedding models, then use the same library to run inference over a range of transformer backbones.

It is aimed at machine learning engineers and NLP developers who need text embeddings for tasks like semantic similarity, retrieval, and clustering. You can load ready-made models from Hugging Face (such as WhereIsAI/UAE-Large-V1) or fine-tune your own with the included loss functions.

As an embedding-models tool, AnglE covers both ends: a training framework with several contrastive objectives (AnglE, CoSENT, contrastive, and Espresso/2DMSE losses) and an inference framework that supports BERT-family models, LLM-based models via LoRA, and bidirectional LLMs.

// What it does

• Angle-optimized (AnglE) loss plus CoSENT, contrastive, and Espresso/2DMSE loss options for training embeddings
• Works with BERT-family backbones (BERT, RoBERTa, ModernBERT) and LLM backbones (LLaMA, Mistral, Qwen)
• Bidirectional LLM embeddings via BiLLM (apply_billm=True)
• Loads pretrained models from Hugging Face, including UAE-Large-V1 and pubmed-angle models
• Single-GPU and multi-GPU training support
• Configurable pooling strategies (cls, last token) and optional prompts for retrieval tasks

// Getting started

Install the angle-emb package, then load a pretrained model and encode text into vectors. A CUDA-capable GPU is expected for the .cuda() calls below.

pip install -U angle-emb

from angle_emb import AnglE
from angle_emb.utils import cosine_similarity

angle = AnglE.from_pretrained('WhereIsAI/UAE-Large-V1', pooling_strategy='cls').cuda()

doc_vecs = angle.encode([
    'The weather is great!',
    'The weather is very good!',
    'i am going to bed'
])

for i, dv1 in enumerate(doc_vecs):
    for dv2 in doc_vecs[i+1:]:
        print(cosine_similarity(dv1, dv2))

from angle_emb import AnglE, Prompts
from angle_emb.utils import cosine_similarity

angle = AnglE.from_pretrained('WhereIsAI/UAE-Large-V1', pooling_strategy='cls').cuda()

qv = angle.encode(['what is the weather?'], to_numpy=True, prompt=Prompts.C)
doc_vecs = angle.encode([
    'The weather is great!',
    'it is rainy today.',
    'i am going to bed'
], to_numpy=True)

for dv in doc_vecs:
    print(cosine_similarity(qv[0], dv))

Commands and code are distilled from the project's own documentation — always check the official repo for the latest.

// When to use it

• Building semantic search or retrieval by embedding queries and documents and ranking by cosine similarity
• Measuring semantic textual similarity between sentence pairs
• Fine-tuning a custom embedding model on your own data with AnglE, CoSENT, or contrastive loss
• Generating embeddings for clustering, deduplication, or RAG pipelines using a pretrained model like UAE-Large-V1

// How AnglE compares

AnglE alongside other open-source embedding models & inference tools AI/TLDR tracks, ranked by GitHub stars.