Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:57306
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use wwydmanski/specter2_pubmed-v0.7-full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use wwydmanski/specter2_pubmed-v0.7-full with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("wwydmanski/specter2_pubmed-v0.7-full") sentences = [ "UCLR RTS timing", "Timing without a timer. ", "Global structural changes in annexin 12. The roles of phospholipid, Ca2+, and pH. ", "Length of time between surgery and return to sport after ulnar collateral ligament reconstruction in Major League Baseball pitchers does not predict need for revision surgery. " ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] |