How to use sugiv/modernbert-us-stablecoin-encoder with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("sugiv/modernbert-us-stablecoin-encoder", device_map="auto")
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]
How to use sugiv/modernbert-us-stablecoin-encoder with PEFT:
Task type is invalid.
https://huggingface.co/sugiv/modernbert-us-stablecoin-encoder/resolve/main/tokenizer.json
hf download hf://sugiv/modernbert-us-stablecoin-encoder/tokenizer.json
curl -L -o tokenizer.json https://huggingface.co/sugiv/modernbert-us-stablecoin-encoder/resolve/main/tokenizer.json
