Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:662
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use taxstreem/numens-finbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use taxstreem/numens-finbert with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("taxstreem/numens-finbert") sentences = [ "credit", "credit_from_customer_for_wholesale_pharmaceuticals", "credit_from_customer_for_wholesale_electronics_purchase", "pos_debit_at_the_place_restaurant_(lunch)" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle