Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use Labib11/MUG-B-1.6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Labib11/MUG-B-1.6 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Labib11/MUG-B-1.6") 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 Labib11/MUG-B-1.6 11
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mteb/leaderboard_legacy
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SmileXing/leaderboard
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sq66/leaderboard_legacy
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reader-1/1
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shiwan7788/leaderboard-uni
Evaluation results
- accuracy on MTEB AmazonCounterfactualClassification (en-ext)test set self-reported74.040
- ap on MTEB AmazonCounterfactualClassification (en-ext)test set self-reported23.622
- f1 on MTEB AmazonCounterfactualClassification (en-ext)test set self-reported61.681
- accuracy on MTEB AmazonCounterfactualClassification (en)test set self-reported72.388
- ap on MTEB AmazonCounterfactualClassification (en)test set self-reported35.145
- f1 on MTEB AmazonCounterfactualClassification (en)test set self-reported66.400
- accuracy on MTEB AmazonCounterfactualClassification (de)test set self-reported54.325
- ap on MTEB AmazonCounterfactualClassification (de)test set self-reported71.953