Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
dense
Generated from Trainer
dataset_size:9236
loss:CosineSimilarityLoss
text-embeddings-inference
Instructions to use Whitzz/spymaster-codenames with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Whitzz/spymaster-codenames with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Whitzz/spymaster-codenames") sentences = [ "hatbox (neutral)", "incident (red)", "lentil (neutral)", "moose (neutral)" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- be62c44730500ed4851deafce309cffeefd791b83d5f796f808f5547674c277e
- Size of remote file:
- 438 MB
- SHA256:
- db0cee2c87569eae72b3ceae077c7f3e2a5469fcb01030b77967d89b92f8a359
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