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README.md
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# bafia Tokenizer for NLP tasks
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## Model Description
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This tokenizer was developed for bafia, a language from the fula[ksf] family of languages in Cameroon. The tokenizer is based on the WordPiece model architecture and has been fine-tuned to handle the unique phonetic and diacritical features of the Fulfulde language.
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- **Developed by**: DS4H-ICTU Research Group in Cooperation with the
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- **Language(s)**: bafia (bafia[ksf] language from Cameroon)
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- **License**: Apache 2.0 (or specify if different)
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- **Model Type**: Tokenizer (WordPiece)
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## Model Sources
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- **Repository**: [Your repository URL]
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- **Paper**: [Link to related paper if available]
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- **Demo**: [Optional: link to demo]
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## Uses
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- **Direct Use**: This tokenizer is designed for NLP tasks such as Named Entity Recognition (NER), translation, and text generation in the bafia language.
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- **Downstream Use**: Can be used as a foundation for models processing bafia text.
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## Bias, Risks, and Limitations
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- **Biases**: The tokenizer might not perfectly capture linguistic nuances due to the limited size of the bafia corpus.
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- **Out-of-Scope Use**: The tokenizer may not perform well for non-bafia languages.
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## Training Details
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- **Training Data**: Extracted from bafia Bible text corpus (bafia_DATASET.xlsx).
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- **Training Procedure**: Preprocessing of text involved normalization of diacritics, tokenization using WordPiece, and post-processing to handle special tokens.
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- **Training Hyperparameters**:
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- Vocabulary Size: 19076
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- Special Tokens: "[UNK]", "[PAD]", "[CLS]", "[SEP]", "[MASK]", "[BOS]", "[EOS]"
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## Evaluation
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- **OOV Rate**: 0.00%
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- **Tokenization Efficiency**: Average tokens per sentence: 27.585227817745803
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- **Special Character Handling**: Successfully handles diacritics and tone markers in bafia.
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## Environmental Impact
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- **Hardware Type**: Google Colab GPU
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- **Hours Used**: 4 hours (training time)
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- **Cloud Provider**: Google Cloud
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- **Carbon Emitted**: Estimated using [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700) calculator
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## Citation
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If you use this tokenizer in your work, please cite it using the following format:
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```
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@misc{bafia_tokenizer,
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title = {bafia Tokenizer},
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author = {Ing. Zingui Fred Mike},
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year = {2024},
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publisher = {Hugging Face},
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url = {https://huggingface.co/FredMike23/tokenizer-Bafia}
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}
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```
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## Contact Information
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For more information, contact the developers at: philiptamla@gmail.com
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