Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
dense
Generated from Trainer
dataset_size:208
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use aaa961/bge-base-en-cost-categories-3sets with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use aaa961/bge-base-en-cost-categories-3sets with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("aaa961/bge-base-en-cost-categories-3sets") sentences = [ "\nName : Gastronomia Italia\nCategory: Dining Services, Business Meetings\nDepartment: Sales\nLocation: Milan, Italy\nAmount: 143.27\nCard: EU Client Engagement\nTrip Name: Milan Networking Event\n", "Professional Services", "Travel: Meals & Entertainment", "Advertising & Marketing" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 282 Bytes
1cfbd00 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | {
"__version__": {
"sentence_transformers": "5.3.0",
"transformers": "5.3.0",
"pytorch": "2.5.1+cu121"
},
"model_type": "SentenceTransformer",
"prompts": {
"query": "",
"document": ""
},
"default_prompt_name": null,
"similarity_fn_name": "cosine"
} |