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
| { | |
| "word_embedding_dimension": 768, | |
| "pooling_mode_cls_token": true, | |
| "pooling_mode_mean_tokens": false, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |