Sentence Similarity
sentence-transformers
Safetensors
English
bert
sparse-encoder
sparse
splade
Generated from Trainer
loss:SpladeLoss
loss:SparseMultipleNegativesRankingLoss
loss:FlopsLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use NeuML/pubmedbert-base-splade with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NeuML/pubmedbert-base-splade with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NeuML/pubmedbert-base-splade") 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_pearson,cosine_spearman,active_dims,sparsity_ratio | |
| 0.15855147373594838,10000,0.9305500683984587,0.8662759073144441,74.58932876586914,0.9975562109702553 | |
| 0.31710294747189677,20000,0.9343322626967523,0.8742193615544332,54.046302795410156,0.9982292673220821 | |
| 0.4756544212078451,30000,0.9333958913897029,0.8842119466991207,37.624534606933594,0.9987672978636087 | |
| 0.6342058949437935,40000,0.9361898560379817,0.8862825807212503,35.35245227813721,0.9988417386711835 | |
| 0.7927573686797419,50000,0.9422040027533072,0.8842805139950729,35.53547668457031,0.9988357421962988 | |
| 0.9513088424156902,60000,0.9422980731390805,0.8870061609483617,34.0018196105957,0.9988859897906233 | |