---
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:
- BlackKakapo/stsb-xlm-r-multilingual-ro
---

# 🔥 cupidon-base-ro

Don’t be shy — cupidon-base-ro is here to charm your embeddings into alignment 💘. Based on the solid foundations of `BlackKakapo/stsb-xlm-r-multilingual-ro` and lovingly fine-tuned on Romanian STS data, this model brings more than just good looks to the table.
Because in the end, true meaning isn’t just in the words... it's in how you embed them. 🧠💕
## 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-base-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-base-ro')
model = AutoModel.from_pretrained('BlackKakapo/cupidon-base-ro')
```

## License
This dataset is licensed under **Apache 2.0**.

## Citation
If you use BlackKakapo/cupidon-tiny-ro in your research, please cite this model as follows:
```
@misc{cupidon-base-ro,
  title={BlackKakapo/cupidon-base-ro},
  author={BlackKakapo},
  year={2025},
}
```