Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
mteb
financial
fiqa
finance
retrieval
rag
esg
fixed-income
equity
Eval Results (legacy)
text-embeddings-inference
Instructions to use mukaj/fin-mpnet-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mukaj/fin-mpnet-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mukaj/fin-mpnet-base") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
-
Initial commit + new eval table
Julian Mukaj2c5f432 -
Initial commit + new eval
Julian Mukaj5560c81 -
Initial commit + new tags
Julian Mukaj7ab7e81 -
Initial commit + eval table
Julian Mukaje6af3f6 -
Initial commit + 2 eval
Julian Mukaj2de8139 -
Initial commit
Julian Mukaj40283d5 -
Initial commit
Julian Mukajd7f79b2 -
Initial commit
Julian Mukaj01ef4a7 -
Initial commit
Julian Mukaj0a13784 -
initial commit
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