Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:70323
loss:CosineSimilarityLoss
text-embeddings-inference
Instructions to use dpshade22/e5-base-john-10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use dpshade22/e5-base-john-10 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("dpshade22/e5-base-john-10") sentences = [ "Suffer me that I may speak; and after that I have spoken, mock on.", "And Peleg lived after he begat Reu two hundred and nine years, and begat sons and daughters.", "And to offer a sacrifice according to that which is said in the law of the Lord, A pair of turtledoves, or two young pigeons.", "As for me, is my complaint to man? and if it were so, why should not my spirit be troubled?" ] 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" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
| } | |
| ] |