Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:1128
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use databio/sbert-encode-cellines-tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use databio/sbert-encode-cellines-tuned with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("databio/sbert-encode-cellines-tuned") sentences = [ "connective tissue cell", "GM18507", "GM18526", "GM08714" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 051d7cce434e8bd3e57afc2874261e784c87dba7f8161bb7b8d2a12853afb8ea
- Size of remote file:
- 90.9 MB
- SHA256:
- e97884b3f01e7d86fa8114d394ce3a4ceae03351916dbf6b988bddcb3d6a34b8
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