Instructions to use MLRS/mBERTu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLRS/mBERTu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="MLRS/mBERTu")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("MLRS/mBERTu") model = AutoModelForMaskedLM.from_pretrained("MLRS/mBERTu") - Notebooks
- Google Colab
- Kaggle
License.
Browse files
README.md
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args: macro
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name: Macro-averaged F1
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---
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# mBERTu
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A Maltese multilingual model pre-trained on the Korpus Malti v4.0 using multilingual BERT as the initial checkpoint.
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args: macro
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value: 76.79
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name: Macro-averaged F1
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license: cc-by-nc-sa-4.0
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widget:
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---
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# mBERTu
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A Maltese multilingual model pre-trained on the Korpus Malti v4.0 using multilingual BERT as the initial checkpoint.
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## License
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This work is licensed under a
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[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].
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Permissions beyond the scope of this license may be available at [https://mlrs.research.um.edu.mt/](https://mlrs.research.um.edu.mt/).
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[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]
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[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/
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[cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png
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