Cupidon Mini Ro

🚀 cupidon-mini-ro

Say hello to cupidon-mini-ro, the larger sibling of the tiny model, yet still lightweight at approximately 90MB. Fine-tuned from sentence-transformers/all-MiniLM-L6-v2, this sentence-transformers model effectively maps Romanian sentences into sleek dense vectors. It can be used for tasks such as semantic search, clustering, and textual similarity.

This model proves that a slightly larger size can be just right. It remains fast, efficient, and is fully capable of meeting your STS needs without overburdening your hardware. 😎💡

🚀 Quick Start

✨ Features

• Based on sentence-transformers/all-MiniLM-L6-v2, fine - tuned for Romanian sentence processing.
• Maps Romanian sentences into dense vectors for various NLP tasks.
• Lightweight (~90MB), fast, and efficient.

📦 Installation

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage (Sentence-Transformers)

from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('BlackKakapo/cupidon-mini-ro')
embeddings = model.encode(sentences)
print(embeddings)

Advanced Usage (HuggingFace Transformers)

Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling - operation on - top of the contextualized word embeddings.

from transformers import AutoTokenizer, AutoModel
import torch


#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)


# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('BlackKakapo/cupidon-mini-ro')
model = AutoModel.from_pretrained('BlackKakapo/cupidon-mini-ro')

📄 License

This dataset is licensed under Apache 2.0.

📚 Documentation

Citation

If you use BlackKakapo/cupidon-mini-ro in your research, please cite this model as follows:

@misc{cupidon-mini-ro,
  title={BlackKakapo/cupidon-mini-ro},
  author={BlackKakapo},
  year={2025},
}

Information Table