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
Upload config.json
Browse files- 1_Pooling/config.json +10 -0
1_Pooling/config.json
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{
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"word_embedding_dimension": 384,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false,
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": false,
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"include_prompt": true
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}
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