Sentence Similarity
sentence-transformers
TensorBoard
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:100
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use leonweber/checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use leonweber/checkpoints with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("leonweber/checkpoints") sentences = [ "<start> FTYGHYHHYHGGTTGRREEEEEEEEDEEEE <end>", "on", "later", "The" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100 | |
| 1.0,25,0.0,0.03,0.2,0.34,0.0,0.0,0.01,0.03,0.04,0.2,0.034,0.34,0.06984920634920636,0.13224375948142458,0.10933917346843042 | |
| 2.0,50,0.09,0.13,0.17,0.3,0.09,0.09,0.04333333333333333,0.13,0.034,0.17,0.03,0.3,0.1310238095238095,0.1690811231378314,0.17563488227458815 | |
| 3.0,75,0.1,0.13,0.17,0.4,0.1,0.1,0.04333333333333333,0.13,0.034,0.17,0.04,0.4,0.14872619047619037,0.20487994694445105,0.18983214742773566 | |
| None,0,0.0,0.0,0.08,0.4,0.0,0.0,0.0,0.0,0.016,0.08,0.04,0.4,0.05428968253968255,0.129985584157983,0.10074305269893503 | |
| None,0,1.0,1.0,1.0,1.0,1.0,1.0,0.3333333333333334,1.0,0.19999999999999996,1.0,0.09999999999999998,1.0,1.0,1.0,1.0 | |
| None,0,1.0,1.0,1.0,1.0,1.0,1.0,0.3333333333333334,1.0,0.19999999999999996,1.0,0.09999999999999998,1.0,1.0,1.0,1.0 | |
| None,0,1.0,1.0,1.0,1.0,1.0,1.0,0.3333333333333334,1.0,0.19999999999999996,1.0,0.09999999999999998,1.0,1.0,1.0,1.0 | |