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
| epoch,steps,cosine_accuracy,cosine_accuracy_threshold,cosine_f1,cosine_precision,cosine_recall,cosine_f1_threshold,cosine_ap,cosine_mcc | |
| 1.0,2758,0.8921052631578947,0.8806893504197968,0.8506188647033718,0.912963811268896,0.9348496837429896,0.7817241689872801 | |
| 2.0,5516,0.9172064777327935,0.9061946902654867,0.8763371844244758,0.9381584974805314,0.9543142650820976,0.8288923423316907 | |
| 3.0,8274,0.9257085020242914,0.9172118204376268,0.9035555555555556,0.9312872194228127,0.9580228650892402,0.8501826095361721 | |
| 4.0,11032,0.9277327935222672,0.9206490331184708,0.8942141623488774,0.9486944571690334,0.9612681828396534,0.8556704322534656 | |