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 10.000.

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-small")

# Compute text embeddings
embeddings = model.encode(["Example sentence"])
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