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 | |
| 1.0,25,0.08,0.1,0.21,0.32,0.08,0.08,0.03333333333333333,0.1,0.042,0.21,0.032,0.32,0.131797619047619,0.17534726235750558,0.17307788345984942 | |
| 2.0,50,0.1,0.18,0.21,0.36,0.1,0.1,0.06,0.18,0.042,0.21,0.036000000000000004,0.36,0.15984523809523804,0.2053888999109833,0.20180102624413773 | |
| 3.0,75,0.11,0.19,0.22,0.34,0.11,0.11,0.06333333333333332,0.19,0.044000000000000004,0.22,0.034,0.34,0.16101190476190472,0.20199186992553161,0.20712119291956754 | |
| None,0,0.0,0.0,0.0,0.29,0.0,0.0,0.0,0.0,0.0,0.0,0.029000000000000005,0.29,0.03209523809523811,0.08710139890053481,0.08907092907092908 | |
| 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 | |