Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
dense
Generated from Trainer
dataset_size:200000
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use DannyAI/embedding_fine_tuning_matryoshka_loss_bge_large_en_v1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use DannyAI/embedding_fine_tuning_matryoshka_loss_bge_large_en_v1.5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("DannyAI/embedding_fine_tuning_matryoshka_loss_bge_large_en_v1.5") sentences = [ "A man standing in front of a brick building.", "The men are together.", "A man is outside.", "The man pushes a women on the ground." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b8c8c7b5ffbee95cb9ca42b1a481c1e4536f458aa8babed1df50592018d4c303
- Size of remote file:
- 1.34 GB
- SHA256:
- 709bbd602e5475fb4f17edda7d0573d87dac0cba928ce5b047fcd7de555e54f1
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