Sentence Similarity
sentence-transformers
PyTorch
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:2400
loss:TripletLoss
loss:MultipleNegativesRankingLoss
loss:CoSENTLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use ostoveland/test13 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ostoveland/test13 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ostoveland/test13") sentences = [ "Flislegging av hall", "query: tapetsering av rom med grunnflate 4x4.5 meter minus tre dører", "query: fliser i hall", "query: fornye markiseduk" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 18f6edf2a9a6f0edc3caf335ae0b86b0276d692192f9a3a63c91f1a00c305083
- Size of remote file:
- 1.11 GB
- SHA256:
- c68be5d1fe76801bfcf00a8d6695664029ff2bb7ea32e51aa41f2209484233f0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.