Sentence Similarity
Transformers
Safetensors
sentence-transformers
PEFT
English
feature-extraction
lora
retrieval
embedding
finance
regulatory
compliance
Instructions to use sugiv/modernbert-us-stablecoin-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sugiv/modernbert-us-stablecoin-encoder with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("sugiv/modernbert-us-stablecoin-encoder", dtype="auto") - sentence-transformers
How to use sugiv/modernbert-us-stablecoin-encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sugiv/modernbert-us-stablecoin-encoder") 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] - PEFT
How to use sugiv/modernbert-us-stablecoin-encoder with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle