charanhu
/

kannada-tokenizer

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+# Kannada Tokenizer
+[![Hugging Face](https://img.shields.io/badge/HuggingFace-Model%20Card-orange)](https://huggingface.co/charanhu/kannada-tokenizer)
+This is a Byte-Pair Encoding (BPE) tokenizer trained specifically for the Kannada language using the `translated_output` column from the [Cognitive-Lab/Kannada-Instruct-dataset](https://huggingface.co/datasets/Cognitive-Lab/Kannada-Instruct-dataset). It is suitable for various Natural Language Processing (NLP) tasks involving Kannada text.
 ## Model Details
+- **Model Type:** Byte-Pair Encoding (BPE) Tokenizer
+- **Language:** Kannada (`kn`)
+- **Vocabulary Size:** 32,000
+- **Special Tokens:**
+  - `[UNK]` (Unknown token)
+  - `[PAD]` (Padding token)
+  - `[CLS]` (Classifier token)
+  - `[SEP]` (Separator token)
+  - `[MASK]` (Masking token)
+## Training Data
+The tokenizer was trained on the `translated_output` column from the [Cognitive-Lab/Kannada-Instruct-dataset](https://huggingface.co/datasets/Cognitive-Lab/Kannada-Instruct-dataset). This dataset contains translated instructions and responses in Kannada, providing a rich corpus for effective tokenization.
+- **Dataset Size:** The dataset includes a significant number of entries covering a wide range of topics and linguistic structures in Kannada.
+- **Data Preprocessing:** Text normalization was applied using NFKC normalization to standardize characters.
+## Training Procedure
+- **Normalization:** NFKC normalization was used to handle canonical decomposition and compatibility decomposition, ensuring that characters are represented consistently.
+- **Pre-tokenization:** The text was pre-tokenized using whitespace splitting.
+- **Tokenizer Algorithm:** Byte-Pair Encoding (BPE) was chosen for its effectiveness in handling subword units, which is beneficial for languages with rich morphology like Kannada.
+- **Training Library:** The tokenizer was built using the [Hugging Face Tokenizers](https://github.com/huggingface/tokenizers) library.
+## Intended Use
+This tokenizer is intended for NLP applications involving the Kannada language, such as:
+- Language Modeling
+- Text Classification
+- Machine Translation
+- Named Entity Recognition
+- Question Answering
+- Summarization
+## Usage
+You can load the tokenizer directly from the Hugging Face Hub:
+```python
+from transformers import PreTrainedTokenizerFast
+tokenizer = PreTrainedTokenizerFast.from_pretrained("charanhu/kannada-tokenizer")
+# Example usage
+text = "ನೀವು ಹೇಗಿದ್ದೀರಿ?"
+encoding = tokenizer.encode(text)
+tokens = tokenizer.convert_ids_to_tokens(encoding)
+decoded_text = tokenizer.decode(encoding)
+print("Original Text:", text)
+print("Tokens:", tokens)
+print("Decoded Text:", decoded_text)
+```
+**Output:**
+```
+Original Text: ನೀವು ಹೇಗಿದ್ದೀರಿ?
+Tokens: ['ನೀವು', 'ಹೇಗಿದ್ದೀರಿ', '?']
+Decoded Text: ನೀವು ಹೇಗಿದ್ದೀರಿ?
+```
+## Limitations
+- **Vocabulary Coverage:** While the tokenizer is trained on a diverse dataset, it may not include all possible words or phrases in Kannada.
+- **Biases:** The tokenizer inherits any biases present in the training data. Users should be cautious when applying it to sensitive or critical applications.
+- **OOV Words:** Out-of-vocabulary words may be broken into subword tokens or mapped to the `[UNK]` token.
+## Recommendations
+- **Fine-tuning:** For best results in specific applications, consider fine-tuning language models with this tokenizer on domain-specific data.
+- **Evaluation:** Users should evaluate the tokenizer in their specific context to ensure it meets their requirements.
+## License
+[MIT License](LICENSE)
+## Acknowledgments
+- **Dataset:** Thanks to [Cognitive-Lab](https://huggingface.co/Cognitive-Lab) for providing the [Kannada-Instruct-dataset](https://huggingface.co/datasets/Cognitive-Lab/Kannada-Instruct-dataset).
+- **Libraries:**
+  - [Hugging Face Tokenizers](https://github.com/huggingface/tokenizers)
+  - [Hugging Face Transformers](https://github.com/huggingface/transformers)
+## Citation
+If you use this tokenizer in your research or applications, please consider citing it:
+```bibtex
+@misc{kannada_tokenizer_2023,
+  title={Kannada Tokenizer},
+  author={charanhu},
+  year={2023},
+  howpublished={\url{https://huggingface.co/charanhu/kannada-tokenizer}},
+}
+```
+## Contact Information
+For questions or comments about the tokenizer, please contact [charanhu](https://huggingface.co/charanhu).