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@@ -14,7 +14,7 @@ Javanese BERT Small is a masked language model based on the [BERT model](https:/
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  The model was originally HuggingFace's pretrained [English BERT model](https://huggingface.co/bert-base-uncased) and is later fine-tuned on the Javanese dataset. It achieved a perplexity of 49.43 on the validation dataset (20% of the articles). Many of the techniques used are based on a Hugging Face tutorial [notebook](https://github.com/huggingface/notebooks/blob/master/examples/language_modeling.ipynb) written by [Sylvain Gugger](https://github.com/sgugger), and [fine-tuning tutorial notebook](https://github.com/piegu/fastai-projects/blob/master/finetuning-English-GPT2-any-language-Portuguese-HuggingFace-fastaiv2.ipynb) written by [Pierre Guillou](https://huggingface.co/pierreguillou).
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- Hugging Face's [Transformers]((https://huggingface.co/transformers)) library was used to train the model -- utilizing the base RoBERTa model and their `Trainer` class. PyTorch was used as the backend framework during training, but the model remains compatible with TensorFlow nonetheless.
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  ## Model
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  | Model | #params | Arch. | Training/Validation data (text) |
 
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  The model was originally HuggingFace's pretrained [English BERT model](https://huggingface.co/bert-base-uncased) and is later fine-tuned on the Javanese dataset. It achieved a perplexity of 49.43 on the validation dataset (20% of the articles). Many of the techniques used are based on a Hugging Face tutorial [notebook](https://github.com/huggingface/notebooks/blob/master/examples/language_modeling.ipynb) written by [Sylvain Gugger](https://github.com/sgugger), and [fine-tuning tutorial notebook](https://github.com/piegu/fastai-projects/blob/master/finetuning-English-GPT2-any-language-Portuguese-HuggingFace-fastaiv2.ipynb) written by [Pierre Guillou](https://huggingface.co/pierreguillou).
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+ Hugging Face's [Transformers]((https://huggingface.co/transformers)) library was used to train the model -- utilizing the base BERT model and their `Trainer` class. PyTorch was used as the backend framework during training, but the model remains compatible with TensorFlow nonetheless.
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  ## Model
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  | Model | #params | Arch. | Training/Validation data (text) |