Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use mncai/financial_embedding_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mncai/financial_embedding_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mncai/financial_embedding_model") 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] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6c04a13e5301748639a0aff20dafcf438c1592eb3be82922c20828a946fdc6ef
- Size of remote file:
- 17.1 MB
- SHA256:
- c564888dbafbfebcc7a62d2f4049afe83e6fb5acd50a97082a84407c77ddd8ff
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