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--- |
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datasets: |
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- mshojaei77/PersianTelegramChannels |
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language: |
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- fa |
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library_name: transformers |
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license: mit |
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pipeline_tag: text-generation |
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tags: |
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- 'Tokenizer ' |
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- persian |
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- bpet |
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--- |
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# PersianBPETokenizer Model Card |
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## Model Details |
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### Model Description |
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The `PersianBPETokenizer` is a custom tokenizer specifically designed for the Persian (Farsi) language. It leverages the Byte-Pair Encoding (BPE) algorithm to create a robust vocabulary that can effectively handle the unique characteristics of Persian text. This tokenizer is optimized for use with advanced language models like BERT and RoBERTa, making it a valuable tool for various Persian NLP tasks. |
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### Model Type |
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- **Tokenization Algorithm**: Byte-Pair Encoding (BPE) |
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- **Normalization**: NFD, StripAccents, Lowercase, Strip, Replace (ZWNJ) |
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- **Pre-tokenization**: Whitespace |
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- **Post-processing**: TemplateProcessing for special tokens |
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### Model Version |
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- **Version**: 1.0 |
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- **Date**: September 6, 2024 |
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### License |
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- **License**: MIT |
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### Developers |
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- **Developed by**: Mohammad Shojaei |
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- **Contact**: [email protected] |
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### Citation |
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If you use this tokenizer in your research, please cite it as: |
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``` |
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Mohammad Shojaei. (2024). PersianBPETokenizer [Software]. Available at https://huggingface.co./mshojaei77/PersianBPETokenizer. |
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``` |
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## Model Use |
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### Intended Use |
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- **Primary Use**: Tokenization of Persian text for NLP tasks such as text classification, named entity recognition, machine translation, and more. |
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- **Secondary Use**: Integration with pre-trained language models like BERT and RoBERTa for fine-tuning on Persian datasets. |
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### Out-of-Scope Use |
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- **Non-Persian Text**: This tokenizer is not designed for languages other than Persian. |
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- **Non-NLP Tasks**: It is not intended for use in non-NLP tasks such as image processing or audio analysis. |
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## Data |
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### Training Data |
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- **Dataset**: `mshojaei77/PersianTelegramChannels` |
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- **Description**: A rich collection of Persian text extracted from various Telegram channels. This dataset provides a diverse range of language patterns and vocabulary, making it suitable for training a general-purpose Persian tokenizer. |
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- **Size**: 60,730 samples |
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### Data Preprocessing |
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- **Normalization**: Applied NFD Unicode normalization, removed accents, converted text to lowercase, stripped leading and trailing whitespace, and removed ZWNJ characters. |
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- **Pre-tokenization**: Used whitespace pre-tokenization. |
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## Performance |
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### Evaluation Metrics |
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- **Tokenization Accuracy**: The tokenizer has been tested on various Persian sentences and has shown high accuracy in tokenizing and encoding text. |
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- **Compatibility**: Fully compatible with Hugging Face Transformers, ensuring seamless integration with advanced language models. |
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### Known Limitations |
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- **Vocabulary Size**: The current vocabulary size is based on the training data. For very specialized domains, additional fine-tuning or training on domain-specific data may be required. |
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- **Out-of-Vocabulary Words**: Rare or domain-specific words may be tokenized as unknown tokens (`[UNK]`). |
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## Training Procedure |
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### Training Steps |
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1. **Environment Setup**: Installed necessary libraries (`datasets`, `tokenizers`, `transformers`). |
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2. **Data Preparation**: Loaded the `mshojaei77/PersianTelegramChannels` dataset and created a batch iterator for efficient training. |
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3. **Tokenizer Model**: Initialized the tokenizer with a BPE model and applied normalization and pre-tokenization steps. |
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4. **Training**: Trained the tokenizer on the Persian text corpus using the BPE algorithm. |
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5. **Post-processing**: Set up post-processing to handle special tokens. |
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6. **Saving**: Saved the tokenizer to disk for future use. |
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7. **Compatibility**: Converted the tokenizer to a `PreTrainedTokenizerFast` object for compatibility with Hugging Face Transformers. |
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### Hyperparameters |
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- **Special Tokens**: `[UNK]`, `[CLS]`, `[SEP]`, `[PAD]`, `[MASK]` |
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- **Batch Size**: 1000 samples per batch |
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- **Normalization Steps**: NFD, StripAccents, Lowercase, Strip, Replace (ZWNJ) |
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## How to Use |
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### Installation |
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To use the `PersianBPETokenizer`, first install the required libraries: |
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```bash |
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pip install -q --upgrade datasets tokenizers transformers |
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``` |
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### Loading the Tokenizer |
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You can load the tokenizer using the Hugging Face Transformers library: |
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```python |
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from transformers import AutoTokenizer |
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persian_tokenizer = AutoTokenizer.from_pretrained("mshojaei77/PersianBPETokenizer") |
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``` |
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### Tokenization Example |
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```python |
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test_sentence = "سلام، چطور هستید؟ امیدوارم روز خوبی داشته باشید" |
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tokens = persian_tokenizer.tokenize(test_sentence) |
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print("Tokens:", tokens) |
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encoded = persian_tokenizer(test_sentence) |
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print("Input IDs:", encoded["input_ids"]) |
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print("Decoded:", persian_tokenizer.decode(encoded["input_ids"])) |
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``` |
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## Acknowledgments |
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- **Dataset**: `mshojaei77/PersianTelegramChannels` |
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- **Libraries**: Hugging Face `datasets`, `tokenizers`, and `transformers` |
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## References |
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- [Hugging Face Tokenizers Documentation](https://huggingface.co./docs/tokenizers/index) |
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- [Hugging Face Transformers Documentation](https://huggingface.co./docs/transformers/index) |
