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 labdmitriy/finetuned-bge-base-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use labdmitriy/finetuned-bge-base-en with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("labdmitriy/finetuned-bge-base-en") sentences = [ "\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 : Black Wolf\nCategory: Luxury Vehicle Rentals, Corporate Services\nDepartment: Executive\nLocation: Tokyo, Japan\nAmount: 1478.67\nCard: Execute Account\nTrip Name: Tokyo Summit 2023\n", "\nName : Kreutz & Partners\nCategory: Strategic Consulting\nDepartment: Marketing\nLocation: Zurich, Switzerland\nAmount: 982.75\nCard: Digital Growth Strategy\nTrip Name: unknown\n", "\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" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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