How to use danielivanov/embedding-model-it-mmarco with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("danielivanov/embedding-model-it-mmarco") sentences = [ "Questa è una persona felice", "Questo è un cane felice", "Questa è una persona molto felice", "Oggi è una giornata di sole" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
How to use danielivanov/embedding-model-it-mmarco with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("danielivanov/embedding-model-it-mmarco") model = AutoModel.from_pretrained("danielivanov/embedding-model-it-mmarco", device_map="auto")
https://huggingface.co/danielivanov/embedding-model-it-mmarco/resolve/refs%2Fpr%2F1/tokenizer.json
hf download hf://danielivanov/embedding-model-it-mmarco@refs/pr/1/tokenizer.json
curl -L -o tokenizer.json https://huggingface.co/danielivanov/embedding-model-it-mmarco/resolve/refs%2Fpr%2F1/tokenizer.json
