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-base-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ivanleomk/finetuned-bge-base-en with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ivanleomk/finetuned-bge-base-en") sentences = [ "\nName : Casa del Camino\nCategory: Boutique Hotel, Travel Services\nDepartment: Marketing\nLocation: Laguna Beach, CA\nAmount: 842.67\nCard: Team Retreat Planning\nTrip Name: Annual Strategy Offsite\n", "\nName : Gartner & Associates\nCategory: Consulting, Business Services\nDepartment: Legal\nLocation: San Francisco, CA\nAmount: 5000.0\nCard: Legal Consultation Fund\nTrip Name: unknown\n", "\nName : SkillAdvance Academy\nCategory: Online Learning Platform, Professional Development\nDepartment: Engineering\nLocation: Austin, TX\nAmount: 1875.67\nCard: Continuous Improvement Initiative\nTrip Name: unknown\n", "\nName : Innovative Patents Co.\nCategory: Intellectual Property Services, Legal Services\nDepartment: Legal\nLocation: New York, NY\nAmount: 3250.0\nCard: Patent Acquisition Fund\nTrip Name: unknown\n" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ce4b367bb5106ed33133a9c3f471f1678b29dfb595a5ab83f27b665feda2f61f
- Size of remote file:
- 438 MB
- SHA256:
- 97e6bfe80a4590dcf864959ba8a6dc1d8b6528a13cfb645a01060f078a852265
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