Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:1000
loss:CoSENTLoss
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use perticarari/omniembedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use perticarari/omniembedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("perticarari/omniembedding") sentences = [ "test", "\" it 's a major victory for maine , and it 's a major victory for other states .", "doctors say one or both boys may die , and that some brain damage is possible if they survive .", "doctors said that one or both of the boys may die and that if they survive , some brain damage is possible ." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "cust_transformer.CustTrans" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] |