Sentence Similarity
sentence-transformers
Safetensors
English
Polish
ILKT
feature-extraction
mteb
custom_code
Eval Results (legacy)
Instructions to use ILKT/2024-06-19_08-22-22 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ILKT/2024-06-19_08-22-22 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ILKT/2024-06-19_08-22-22", trust_remote_code=True) 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
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Spaces using ILKT/2024-06-19_08-22-22 11
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mteb/leaderboard_legacy
π₯
SmileXing/leaderboard
π₯
sq66/leaderboard_legacy
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reader-1/1
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shiwan7788/leaderboard-uni
Evaluation results
- accuracy on MTEB MassiveIntentClassificationtest set self-reported0.103
- accuracy on MTEB MassiveIntentClassificationvalidation set self-reported0.107
- accuracy on MTEB MassiveScenarioClassificationtest set self-reported0.176
- accuracy on MTEB MassiveScenarioClassificationvalidation set self-reported0.176
- accuracy on MTEB CBDtest set self-reported0.507
- accuracy on MTEB PolEmo2.0-INtest set self-reported0.389
- accuracy on MTEB PolEmo2.0-OUTtest set self-reported0.294
- accuracy on MTEB AllegroReviewstest set self-reported0.220