Sentence Similarity
Transformers
Safetensors
English
Polish
ILKT
feature-extraction
mteb
custom_code
Eval Results (legacy)
Instructions to use ILKT/2024-06-19_21-12-17 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ILKT/2024-06-19_21-12-17 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ILKT/2024-06-19_21-12-17", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
README.md exists but content is empty.
- Downloads last month
- 142
Spaces using ILKT/2024-06-19_21-12-17 11
π₯
mteb/leaderboard_legacy
π₯
SmileXing/leaderboard
π₯
sq66/leaderboard_legacy
π
reader-1/1
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shiwan7788/leaderboard-uni
Evaluation results
- accuracy on MTEB MassiveIntentClassificationtest set self-reported0.119
- accuracy on MTEB MassiveIntentClassificationvalidation set self-reported0.116
- accuracy on MTEB MassiveScenarioClassificationtest set self-reported0.212
- accuracy on MTEB MassiveScenarioClassificationvalidation set self-reported0.200
- accuracy on MTEB CBDtest set self-reported0.521
- accuracy on MTEB PolEmo2.0-INtest set self-reported0.355
- accuracy on MTEB PolEmo2.0-OUTtest set self-reported0.298
- accuracy on MTEB AllegroReviewstest set self-reported0.222