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:
- df0a403c6f09ea8dd2a898ecfac149ffb27153d0f50a39de8de23aabaf6d6974
- Size of remote file:
- 438 MB
- SHA256:
- 4ca6eeae92e6efc831bf6a82a7584f012f0a9004a86fd9fcaa7ec41dd8f5295e
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