Sentence Similarity
sentence-transformers
PyTorch
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:24000
loss:TripletLoss
loss:MultipleNegativesRankingLoss
loss:CoSENTLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use ostoveland/test11 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ostoveland/test11 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ostoveland/test11") sentences = [ "installere gulv på lite loft", "query: gjerdeoppsett", "query: støping av helleplass med skiferheller, 100 kvm", "query: legge nytt gulv på lite loft" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from ostoveland/test11: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/ostoveland/test11/resolve/main/tokenizer.json
- Command line
-
hf download hf://ostoveland/test11/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ostoveland/test11/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 5d747ca97fe6e145aef974d51b6921c06a4eed57a0513eb2427235a9af134752
- Size of remote file:
- 17.1 MB
- SHA256:
- e4f7e21bec3fb0044ca0bb2d50eb5d4d8c596273c422baef84466d2c73748b9c
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