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"])
- Downloads last month
- 12
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cnmoro/custom-model2vec-tokenlearn-small") 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]