Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:6300
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use bhlim/bge-base-financial-matryoshka with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use bhlim/bge-base-financial-matryoshka with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("bhlim/bge-base-financial-matryoshka") sentences = [ "As of December 31, 2023, Hilton franchised 6,679 hotels and resorts, with 914,974 rooms.", "What does Google's new model 'Gemini' aim to achieve?", "What is the total number of rooms in Hilton's franchised hotels as of December 31, 2023?", "How much is the Company agreed to pay under the opioid settlement to resolve all lawsuits and future claims by government entities nationwide?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle