Sentence Similarity
sentence-transformers
PyTorch
Chinese
bert
feature-extraction
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use Amu/tao with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Amu/tao with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Amu/tao") sentences = [ "那是 個快樂的人", "那是 條快樂的狗", "那是 個非常幸福的人", "今天是晴天" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
A try for emebdding model:
The method is the same as the stella-v2, I just fine-tuned it in a small dataset for test.
Now I'm working on the tao-v2, It will have a different sturcture.
I will release tao-v2 as fast as I can.
Thank you to the open source community.
- Downloads last month
- 24
Spaces using Amu/tao 11
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mteb/leaderboard_legacy
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SmileXing/leaderboard
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sq66/leaderboard_legacy
🚀
reader-1/1
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shiwan7788/leaderboard-uni
Evaluation results
- cos_sim_pearson on MTEB AFQMCvalidation set self-reported47.338
- cos_sim_spearman on MTEB AFQMCvalidation set self-reported49.941
- euclidean_pearson on MTEB AFQMCvalidation set self-reported48.121
- euclidean_spearman on MTEB AFQMCvalidation set self-reported49.941
- manhattan_pearson on MTEB AFQMCvalidation set self-reported48.076
- manhattan_spearman on MTEB AFQMCvalidation set self-reported49.895
- cos_sim_pearson on MTEB ATECtest set self-reported50.977
- cos_sim_spearman on MTEB ATECtest set self-reported53.113