Sentence Similarity
sentence-transformers
ONNX
Safetensors
Morisyen
English
French
xlm-roberta
feature-extraction
mauritian-creole
kreol-morisien
matryoshka
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use Singaraj/morisien-embed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Singaraj/morisien-embed with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Singaraj/morisien-embed") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse filesAdd MTEB results and paper DOI to model card
README.md
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- mauritian-creole
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- kreol-morisien
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- matryoshka
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base_model: intfloat/multilingual-e5-base
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datasets:
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- prajdabre/KreolMorisienMT
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`scripts/evaluate.py --truncate-dim`). The Haitian-proximity and case-sensitivity figures under
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Limitations come from an internal adversarial audit of the released checkpoint.
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## Training
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- **Data:** 35,064 unique, leak-free Creole↔{English, French} pairs — effectively all publicly
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## Citation
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If you use this model, please cite the data sources it builds on:
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[MorisienMT](https://arxiv.org/abs/2206.02421) (Dabre & Sukhoo, 2022) and
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[Kreyòl-MT](https://arxiv.org/abs/2405.05376) (Robinson et al., NAACL 2024).
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```bibtex
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@misc{morisien-embed,
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author
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title
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year
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}
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```
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- mauritian-creole
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- kreol-morisien
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- matryoshka
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- mteb
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base_model: intfloat/multilingual-e5-base
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datasets:
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- prajdabre/KreolMorisienMT
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`scripts/evaluate.py --truncate-dim`). The Haitian-proximity and case-sensitivity figures under
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Limitations come from an internal adversarial audit of the released checkpoint.
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## MTEB
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The held-out MorisienMT test split is now a task in
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[MTEB](https://github.com/embeddings-benchmark/mteb), `MorisienMTBitextMining` — the first Mauritian
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Creole task in the benchmark. This model is registered in MTEB and its scores are on the
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[leaderboard](https://huggingface.co/spaces/mteb/leaderboard).
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Bitext-mining F1 across the four directional subsets:
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| Model | mfe→eng | eng→mfe | mfe→fra | fra→mfe | avg |
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| intfloat/multilingual-e5-small | 0.358 | 0.454 | 0.475 | 0.495 | 0.446 |
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| sentence-transformers/LaBSE | 0.882 | 0.845 | 0.886 | 0.779 | 0.848 |
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| **morisien-embed** | **0.927** | **0.909** | **0.939** | **0.924** | **0.925** |
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This is bitext-mining F1, a different metric from the ndcg@10 retrieval numbers above. The model is
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trained on the MorisienMT corpus this split is drawn from, so MTEB records the result as in-domain
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(via `training_datasets`), not zero-shot.
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## Training
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- **Data:** 35,064 unique, leak-free Creole↔{English, French} pairs — effectively all publicly
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## Citation
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If you use this model, please cite the accompanying report along with the data sources it builds on:
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[MorisienMT](https://arxiv.org/abs/2206.02421) (Dabre & Sukhoo, 2022) and
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[Kreyòl-MT](https://arxiv.org/abs/2405.05376) (Robinson et al., NAACL 2024).
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```bibtex
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@misc{morisien-embed,
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author = {Singaraj B},
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title = {morisien-embed: A Dedicated Text Embedding Model and Benchmark for Mauritian Creole (Kreol Morisien)},
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year = {2026},
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publisher = {Zenodo},
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doi = {10.5281/zenodo.21877805},
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url = {https://doi.org/10.5281/zenodo.21877805}
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}
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```
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