Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
dense
Generated from Trainer
dataset_size:6300
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use deter3/bge-base-financial-matryoshka with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use deter3/bge-base-financial-matryoshka with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("deter3/bge-base-financial-matryoshka") sentences = [ "The total lease payments for 2023 were initially valued at $1,008 million, but after incorporating $43 million for interest, the final amount totaled $1,051 million.", "What percentage of Kenvue's shares did Johnson & Johnson own after the exchange offer on August 23, 2023?", "What was the increase in total lease payments from the base amount to the final amount including interest in 2023?", "What is the primary use of Global Business Services within Procter & Gamble?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle