---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
language:
- ro
language_creators:
- machine-generated
dataset:
- ro_sts
license: apache-2.0
datasets:
- BlackKakapo/RoSTSC
base_model:
- sentence-transformers/all-MiniLM-L6-v2
---

# 🔥 cupidon-mini-ro

Say hello to cupidon-mini-ro — the bigger sibling of tiny, but still on the lightweight side at just ~90MB. Fine-tuned from `sentence-transformers/all-MiniLM-L6-v2`, this sentence-transformers model smoothly maps Romanian sentences into sleek dense vectors for tasks like semantic search, clustering, and textual similarity.
It’s living proof that sometimes, a little more size is just right — still fast, still efficient, and definitely charming enough to handle your STS needs without hogging your hardware. 😎💡

## Usage (Sentence-Transformers)

Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

```bash
pip install -U sentence-transformers
```

Then you can use the model like this:

```python
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)
```

## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), 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.

```python
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**.

## 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},
}
```