UAE Large V1 EU AI Act Compliance Profile

Universal AnglE Embedding

📢 WhereIsAI/UAE-Large-V1 is licensed under MIT. Feel free to use it in any scenario. If you use it for academic papers, you could cite us via 👉 citation info.

🤝 Follow us on:

• GitHub: https://github.com/SeanLee97/AnglE.
• Preprint Paper: AnglE-optimized Text Embeddings
• Conference Paper: AoE: Angle-optimized Embeddings for Semantic Textual Similarity (ACL24)
• 📘 Documentation: https://angle.readthedocs.io/en/latest/index.html

Welcome to using AnglE to train and infer powerful sentence embeddings.

🏆 Achievements

• 📅 May 16, 2024 | AnglE's paper is accepted by ACL 2024 Main Conference
• 📅 Dec 4, 2023 | 🔥 Our universal English sentence embedding WhereIsAI/UAE-Large-V1 achieves SOTA on the MTEB Leaderboard with an average score of 64.64!

🧑‍🤝‍🧑 Siblings:

• WhereIsAI/UAE-Code-Large-V1: This model can be used for code or GitHub issue similarity measurement.

Usage

1. angle_emb

python -m pip install -U angle-emb

1. Non-Retrieval Tasks

There is no need to specify any prompts.

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'
], normalize_embedding=True)

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

1. Retrieval Tasks

For retrieval purposes, please use the prompt Prompts.C for query (not for document).

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