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---
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tags:
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- khmer
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- nlp
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- punctuation-restoration
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- inverse-text-normalization
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- asr
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- xlm-roberta
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license: mit
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language:
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- km
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---
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# KhmerTagger: Inverse Text Normalization for Khmer ASR
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KhmerTagger is a model for inverse text normalization (ITN) of Khmer Automatic Speech Recognition (ASR) outputs. It performs punctuation restoration and number recognition to improve readability of raw ASR text.
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## Model Description
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The model is based on XLM-RoBERTa as the encoder, with a bidirectional LSTM layer and two classification heads:
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- **Punctuation head**: Predicts punctuation marks (space, comma, question mark, exclamation mark, etc.)
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- **Number head**: Identifies and tags numeric entities in the text
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## Usage
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```python
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from transformers import XLMRobertaTokenizer
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import torch
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from model import KhmerTagger
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# Load tokenizer
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tokenizer = XLMRobertaTokenizer.from_pretrained("FacebookAI/xlm-roberta-base")
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# Load model
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model = KhmerTagger(n_punct_features=5, n_num_features=3)
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model.load_state_dict(torch.load("pytorch_model.bin", map_location="cpu", weights_only=True))
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model.eval()
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# Your inference code here...
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```
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## Training
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The model was trained on 1.5 million tokens of Khmer news data and achieved 97.2% accuracy on the validation set.
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## Citation
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```bibtex
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@misc{khmertagger2025,
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author = {Seanghay Yath},
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title = {KhmerTagger: Inverse Text Normalization for Khmer Automatic Speech Recognition},
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year = {2025},
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publisher = {GitHub},
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journal = {GitHub repository},
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howpublished = {\url{https://github.com/seanghay/khmertagger}},
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note = {Open source project for Khmer punctuation restoration and number recognition using XLM-ROBERTa}
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}
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```
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