Feature Extraction
sentence-transformers
Safetensors
mpnet
sentence-similarity
dense
Generated from Trainer
dataset_size:4615
loss:TripletLoss
text-embeddings-inference
Instructions to use FritzStack/mpnet_MH_embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use FritzStack/mpnet_MH_embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("FritzStack/mpnet_MH_embedding") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- be2e51d08217ec3e466f6ce9fa4252cc6490c0feb69d03eda7e166371497b7ab
- Size of remote file:
- 438 MB
- SHA256:
- ef38bb8c5a42bcf91b4728964a8790df04a9592ddf112364da601959296aa0ac
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