Sentence Similarity
sentence-transformers
TensorBoard
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:100
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use leonweber/checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use leonweber/checkpoints with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("leonweber/checkpoints") sentences = [ "<start> FTYGHYHHYHGGTTGRREEEEEEEEDEEEE <end>", "on", "later", "The" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0fa15190517337984cd1bcd5dbc5f1158cfa52f32fc512e0ab1489f8e7f0a5a0
- Size of remote file:
- 5.97 kB
- SHA256:
- 1762c950240851dbfbe4aa34848965ecfc1203552f84ba62d02df00ba0c5f815
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