Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:44114
loss:ContrastiveLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use laura2243/bert-sota with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use laura2243/bert-sota with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("laura2243/bert-sota") sentences = [ "The city is located in 1889 , along the Nehalem River and Nehalem Bay , near the Pacific Ocean .", "Incorporated in 1889 , the city lies along the Pacific Ocean near the Nehalem River and Nehalem Bay .", "Along the coast there are almost 2,000 islands , about three quarters of which are uninhabited .", "The mammalian fauna of Madagascar is largely endemic and highly distinctive ." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "word_embedding_dimension": 768, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |