Sentence Similarity
sentence-transformers
Safetensors
English
Polish
ILKT
mteb
feature-extraction
custom_code
Eval Results (legacy)
Instructions to use ILKT/2024-06-19_22-27-15 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ILKT/2024-06-19_22-27-15 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ILKT/2024-06-19_22-27-15", 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
README.md exists but content is empty.
- Downloads last month
- 52
Spaces using ILKT/2024-06-19_22-27-15 11
π₯
mteb/leaderboard_legacy
π₯
sq66/leaderboard_legacy
π
reader-1/1
π₯
shiwan7788/leaderboard-uni
π₯
SmileXing/leaderboard
Evaluation results
- accuracy on MTEB AllegroReviews (default)test set self-reported21.014
- f1 on MTEB AllegroReviews (default)test set self-reported19.340
- accuracy on MTEB CBD (default)test set self-reported53.690
- ap on MTEB CBD (default)test set self-reported13.997
- f1 on MTEB CBD (default)test set self-reported44.207
- v_measure on MTEB EightTagsClustering (default)test set self-reported2.969
- v_measure_std on MTEB EightTagsClustering (default)test set self-reported0.840
- accuracy on MTEB MassiveIntentClassification (pl)test set self-reported22.683