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/test9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ostoveland/test9 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ostoveland/test9") sentences = [ "Fjerne trapp i mur utvendig", "query: Montere nytt dusjkabinett", "query: installasjon av beslag på portaldører", "query: fjerne utvendig trapp i mur" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from ostoveland/test9: direct link, hf CLI and curl.
- Browser
- Download file 297 Bytes
-
https://huggingface.co/ostoveland/test9/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://ostoveland/test9/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/ostoveland/test9/resolve/main/1_Pooling/config.json
297 Bytes
| { | |
| "word_embedding_dimension": 1024, | |
| "pooling_mode_cls_token": true, | |
| "pooling_mode_mean_tokens": false, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |