Sentence Similarity
sentence-transformers
TensorBoard
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:3696
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use romain125/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use romain125/model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("romain125/model") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] 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 | |
| 1.0,3,0.8,0.6908704042434692,0.888888888888889,1.0,0.8,0.6908704042434692,1.0,0.0 | |
| 2.0,6,0.8,0.652718186378479,0.888888888888889,1.0,0.8,0.652718186378479,1.0,0.0 | |
| 2.0,6,0.8,0.652718186378479,0.888888888888889,1.0,0.8,0.652718186378479,1.0,0.0 | |