Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:5749
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use jilangdi/all-mpnet-base-v2-sts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jilangdi/all-mpnet-base-v2-sts with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jilangdi/all-mpnet-base-v2-sts") sentences = [ "A chef is preparing some food.", "Five birds stand on the snow.", "A chef prepared a meal.", "There is no 'still' that is not relative to some other object." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 201 Bytes
1e3950e | 1 2 3 4 5 6 7 8 9 10 | {
"__version__": {
"sentence_transformers": "3.0.1",
"transformers": "4.41.2",
"pytorch": "2.3.1+cu121"
},
"prompts": {},
"default_prompt_name": null,
"similarity_fn_name": null
} |