A custom model2vec model, trained using a modified version of the tokenlearn library.
Base model is nomic-ai/nomic-embed-text-v2-moe.
The output dimension is 256, and the vocabulary size is 249.999
The training process used a mix of English (10%) and Portuguese (90%) texts.
from model2vec import StaticModel
# Load a pretrained Sentence Transformer model
model = StaticModel.from_pretrained("cnmoro/custom-model2vec-tokenlearn-medium")
# Compute text embeddings
embeddings = model.encode(["Example sentence"])
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from sentence_transformers import SentenceTransformer model = SentenceTransformer("cnmoro/custom-model2vec-tokenlearn-medium") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3]