Sentence Similarity
sentence-transformers
Safetensors
English
Polish
ILKT
feature-extraction
mteb
custom_code
Eval Results (legacy)
Instructions to use ILKT/2024-06-19_22-23-38 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ILKT/2024-06-19_22-23-38 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ILKT/2024-06-19_22-23-38", 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_22-23-38 11
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mteb/leaderboard_legacy
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sq66/leaderboard_legacy
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reader-1/1
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shiwan7788/leaderboard-uni
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SmileXing/leaderboard
Evaluation results
- accuracy on MTEB MassiveIntentClassificationtest set self-reported0.175
- accuracy on MTEB MassiveIntentClassificationvalidation set self-reported0.179
- accuracy on MTEB MassiveScenarioClassificationtest set self-reported0.252
- accuracy on MTEB MassiveScenarioClassificationvalidation set self-reported0.256
- accuracy on MTEB CBDtest set self-reported0.583
- accuracy on MTEB PolEmo2.0-INtest set self-reported0.534
- accuracy on MTEB PolEmo2.0-OUTtest set self-reported0.226
- accuracy on MTEB AllegroReviewstest set self-reported0.270