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
Associated model license
#3
by Jonas-ML - opened
Hi Mukaj,
Interesting work you have done here on the fin-mpnet-base embedding model :)
I saw the model is based on the all-mpnet-base-v2, which is licensed under apache 2.0. Would I assume correctly that the fin-mpnet-base model is hence also under the apache 2.0 license?
Regards
Hi Mukaj,
I am impressed by the great result your model gets on the FiQA test.
I am still wondering about the licensing topic I mentioned earlier. If you could spare a minute and answer my earlier message, I would be very grateful :)
Hi,
Thank you for the interest, sorry for the slow response. The model licence should not be changed from apache 2.0 you are correct.