Nepalibert

🚀 NepaliBERT(Phase 1)

NepaliBERT is a state - of - the - art language model for Nepali, built upon the BERT model. It is trained using masked language modeling (MLM), aiming to understand and generate Nepali language effectively.

🚀 Quick Start

📦 Installation

1. Clone the model repository:

git lfs install
git clone https://huggingface.co/Rajan/NepaliBERT

💻 Usage Examples

Basic Usage

Loading the Tokenizer

from transformers import BertTokenizer
vocab_file_dir = './NepaliBERT/' 
tokenizer = BertTokenizer.from_pretrained(vocab_file_dir,
                                        strip_accents=False,
                                         clean_text=False )

Loading the model

from transformers import BertForMaskedLM
model = BertForMaskedLM.from_pretrained('./NepaliBERT')

Using the FillMaskPipeline

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 token (here, [mask]) and return a list of the most probable filled sequences, with their probabilities.

from transformers import pipeline

fill_mask = pipeline(
    "fill-mask",
    model=model,
    tokenizer=tokenizer
)

💡 Usage Tip

For more information, visit the GITHUB🤗.