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# NepaliBERT(Phase 1) NEPALIBERT is a state-of-the-art language model for Nepali based on the BERT model. The model is trained using a masked language modeling (MLM). # Loading the model and tokenizer 1. clone the model repo ``` git lfs install git clone https://huggingface.co/Rajan/NepaliBERT ``` 2. Loading the Tokenizer ``` from transformers import BertTokenizer vocab_file_dir = './NepaliBERT/' tokenizer = BertTokenizer.from_pretrained(vocab_file_dir, strip_accents=False, clean_text=False ) ``` 3. Loading the model: ``` from transformers import BertForMaskedLM model = BertForMaskedLM.from_pretrained('./NepaliBERT') ``` The easiest way to check whether our language model is learning anything interesting is via the ```FillMaskPipeline```. Pipelines are simple wrappers around tokenizers and models, and the 'fill-mask' one will let you input a sequence containing a masked to…14d5daa
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NepaliBERTv1
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