Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
dataset_size:100K<n<1M
loss:MSELoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use w601sxs/b1ade-embed-distilled-from-gte-large-en-v1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use w601sxs/b1ade-embed-distilled-from-gte-large-en-v1.5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("w601sxs/b1ade-embed-distilled-from-gte-large-en-v1.5") sentences = [ "A man is jumping.", "The man is jumping off something.", "Two people are posing for a photograph.", "two women sing opera" ] 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" | |
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| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
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