Sentence Similarity
sentence-transformers
Safetensors
German
modernbert
feature-extraction
Generated from Trainer
dataset_size:8066634
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use Parallia/Fairly-Multilingual-ModernBERT-Embed-BE-DE 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-DE with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Parallia/Fairly-Multilingual-ModernBERT-Embed-BE-DE") sentences = [ "Diese drei geheimnisvollen Männer kamen uns dann zu Hilfe.", "Drei ziemlich seltsame Typen halfen uns danach.", "Diese drei schwarzen Vögel sahen dann in unseren Garten.", "Einige Leute sind hilfsbereit.", "Un, zwei, drei... Wer kann die nächsten Zahlen erraten?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [5, 5] - Notebooks
- Google Colab
- Kaggle
Update README.md
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by aari1995 - opened
README.md
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- **Similarity Function:** Cosine Similarity
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- **Training Dataset:**
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- parallel-sentences
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- **Languages:**
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- **License:** apache-2.0
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### Full Model Architecture
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- **Similarity Function:** Cosine Similarity
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- **Training Dataset:**
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- parallel-sentences
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- **Languages:** de
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- **License:** apache-2.0
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### Full Model Architecture
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