finance embeddings investopedia EU AI Act Compliance Profile

FinLang/finance-embeddings-investopedia

This is the Investopedia embedding for finance application by the FinLang team. The model is trained using our open-sourced finance dataset from https://huggingface.co/datasets/FinLang/investopedia-embedding-dataset

This is a finetuned embedding model on top of BAAI/bge-base-en-v1.5. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search in RAG applications.

This project is for research purposes only. Third-party datasets may be subject to additional terms and conditions under their associated licenses.

Plans

• The research paper will be published soon.
• We are working on a v2 version of the model where we are increasing the training corpus of financial data and using improved techniques for training embeddings.

Usage (LLamaIndex)

Simply specify the Finlang embedding during the indexing procedure for your Financial RAG applications.

Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed (see https://huggingface.co/sentence-transformers):

Then you can use the model like this:

from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('FinLang/investopedia_embedding')
embeddings = model.encode(sentences)
print(embeddings)

Example code testing:

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