Sentence Similarity
sentence-transformers
Safetensors
Korean
English
xlm-roberta
feature-extraction
Korean
financial-nlp
nmixx
multilingual
text-embeddings-inference
Instructions to use nmixx-fin/nmixx-bge-m3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nmixx-fin/nmixx-bge-m3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nmixx-fin/nmixx-bge-m3") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
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# NMIXX-BGE-M3
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This repository contains a bge-m3-based SentenceTransformer model fine-tuned with a triplet-loss setup on the `nmixx-fin/NMIXX_train` dataset. It produces high-quality sentence embeddings for Korean financial text with multilingual capabilities, optimized for semantic similarity tasks in the finance domain.
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# NMIXX-BGE-M3
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This repository contains a bge-m3-based SentenceTransformer model fine-tuned with a triplet-loss setup on the `nmixx-fin/NMIXX_train` dataset. It produces high-quality sentence embeddings for Korean financial text with multilingual capabilities, optimized for semantic similarity tasks in the finance domain.
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