Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:208
loss:BatchSemiHardTripletLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use ivanleomk/finetuned-bge-bai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ivanleomk/finetuned-bge-bai with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ivanleomk/finetuned-bge-bai") sentences = [ "\nName : Nordiska Hosting Collective\nCategory: Cloud Storage Solutions, Data Security Services\nDepartment: IT Operations\nLocation: Helsinki, Finland\nAmount: 1439.57\nCard: Annual Data Management Plan\nTrip Name: unknown\n", "\nName : Allegro Integrations\nCategory: Payment Processing Solutions, Financial Technology Services\nDepartment: Finance\nLocation: Dublin, Ireland\nAmount: 1298.75\nCard: Bi-annual Financial Systems Audit\nTrip Name: unknown\n", "\nName : FastLane Transport\nCategory: Logistics & Transport, Vehicle Services\nDepartment: Sales\nLocation: Miami, FL\nAmount: 158.25\nCard: Sales Travel Expenses\nTrip Name: unknown\n", "\nName : Aperio Global Insights\nCategory: Strategic Business Consulting, Data Analytics Services\nDepartment: Finance\nLocation: Chicago, IL\nAmount: 3456.78\nCard: Global Market Expansion Evaluation\nTrip Name: unknown\n" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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