Kowsher/bangla-bert

Bangla BERT Base

Here we published a pretrained Bangla bert language model as bangla-bert! which is now available in huggingface model hub. Here we described bangla-bert which is a pretrained Bangla language model based on mask language modeling described in BERT and the GitHub repository

Corpus Details

We trained the Bangla bert language model using BanglaLM dataset from kaggle BanglaLM. There is 3 version of dataset which is almost 40GB. After downloading the dataset, we went on the way to mask LM.

bangla-bert Tokenizer

from transformers import AutoTokenizer, AutoModel
bnbert_tokenizer = AutoTokenizer.from_pretrained("Kowsher/bangla-bert")
text = "খাঁটি সোনার চাইতে খাঁটি আমার দেশের মাটি"
bnbert_tokenizer.tokenize(text)
# output: ['খাটি', 'সে', '##ানার', 'চাইতে', 'খাটি', 'আমার', 'দেশের', 'মাটি']

MASK Generation here, we can use bert base bangla model as for masked language modeling:

from transformers import BertForMaskedLM, BertTokenizer, pipeline
model = BertForMaskedLM.from_pretrained("Kowsher/bangla-bert")
tokenizer = BertTokenizer.from_pretrained("Kowsher/bangla-bert")

nlp = pipeline('fill-mask', model=model, tokenizer=tokenizer)
for pred in nlp(f"আমি বাংলার গান {nlp.tokenizer.mask_token}"):
  print(pred)
# {'sequence': 'আমি বাংলার গান লিখি', 'score': 0.17955434322357178, 'token': 24749, 'token_str': 'লিখি'}


nlp = pipeline('fill-mask', model=model, tokenizer=tokenizer)
for pred in nlp(f"তুই রাজাকার তুই {nlp.tokenizer.mask_token}"):
  print(pred)
# {'sequence': 'তুই রাজাকার তুই রাজাকার', 'score': 0.9975168704986572, 'token': 13401, 'token_str': 'রাজাকার'}


nlp = pipeline('fill-mask', model=model, tokenizer=tokenizer)
for pred in nlp(f"বাংলা আমার {nlp.tokenizer.mask_token}"):
  print(pred)
# {'sequence': 'বাংলা আমার অহংকার', 'score': 0.5679506063461304, 'token': 19009, 'token_str': 'অহংকার'}  

Cite this work M. Kowsher, A. A. Sami, N. J. Prottasha, M. S. Arefin, P. K. Dhar and T. Koshiba, "Bangla-BERT: Transformer-based Efficient Model for Transfer Learning and Language Understanding," in IEEE Access, 2022, doi: 10.1109/ACCESS.2022.3197662.

Author

Kowsher