Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use akarum/cloudy-large-zh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use akarum/cloudy-large-zh with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("akarum/cloudy-large-zh") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| Model Name | Dimension | Sequence Length | Language | Need instruction for retrieval? |
|---|---|---|---|---|
| cloudy-largh-zh | 1024 | 1024 | Chinese | NO |
- Downloads last month
- 44
Spaces using akarum/cloudy-large-zh 11
🥇
mteb/leaderboard_legacy
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SmileXing/leaderboard
🥇
sq66/leaderboard_legacy
🚀
reader-1/1
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shiwan7788/leaderboard-uni
Evaluation results
- map on MTEB CMedQAv1test set self-reported86.104
- mrr on MTEB CMedQAv1test set self-reported88.779
- map on MTEB CMedQAv2test set self-reported86.947
- mrr on MTEB CMedQAv2test set self-reported89.473
- map_at_1 on MTEB CmedqaRetrievalself-reported25.297
- map_at_10 on MTEB CmedqaRetrievalself-reported37.159
- map_at_100 on MTEB CmedqaRetrievalself-reported39.016
- map_at_1000 on MTEB CmedqaRetrievalself-reported39.134
- map_at_3 on MTEB CmedqaRetrievalself-reported33.248
- map_at_5 on MTEB CmedqaRetrievalself-reported35.371
- mrr_at_1 on MTEB CmedqaRetrievalself-reported38.435
- mrr_at_10 on MTEB CmedqaRetrievalself-reported46.235
- mrr_at_100 on MTEB CmedqaRetrievalself-reported47.265
- mrr_at_1000 on MTEB CmedqaRetrievalself-reported47.308
- mrr_at_3 on MTEB CmedqaRetrievalself-reported43.828
- mrr_at_5 on MTEB CmedqaRetrievalself-reported45.210