Instructions to use Parallia/Fairly-Multilingual-ModernBERT-Embed-BE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Parallia/Fairly-Multilingual-ModernBERT-Embed-BE with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Parallia/Fairly-Multilingual-ModernBERT-Embed-BE") sentences = [ "These three mysterious men came to our help.", "Three strange guys helped us then.", "These three black birds came in our garden.", "Some people are helpful.", "One, two, three... Who can guess the next digits?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [5, 5] - Notebooks
- Google Colab
- Kaggle
Evaluation Dataset
Are there any public evaluation results on these models. I was unable to find the linked paper on arXiv ?
Hi! Not yet, 2501.99999 is indeed just a placeholder. I aim to publish some tangible results sometime in January, but I cannot make precise promises.
Which datasets were you looking forward to find evaluations for?
I'm interested in MedSTS and any benchmark to for multilingual STS such as.
STSB makes a lot of sense, but it also isn't cross-lingual which is a bit of a shame. I wonder if I could make my own cross-lingual eval there but I have very limited bandwidth for this unfortunately.
MedSTS I'm definitely interested ^_^ but I would want a totally different model to achieve decent scores here, I think the scope of this model is too general for MedSTS to perform well.
I think STSB would be a good starting point even if it's not cross lingual at least it will give the per language scores. I understand your bandwidth is limited. I just want to say this is really awesome and I'm really impressed by how fast you adapted everything.