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Bangla Bert

Bangla Bert is an open-source pretrained BERT language model for Bengali, trained with masked language modeling and distributed through the Hugging Face model hub. Its repository includes tokenizer and fill-mask usage examples, a vocabulary file, and reported evaluations on Bengali NLP tasks.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Bangla Bert — OpenViST project profile in AI Models.
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Understand the project

Practical overview

When it fits

Use Bangla Bert when a Bengali NLP experiment calls for a pretrained BERT model, especially for tokenization or masked-word prediction, or when evaluating a model on a task-specific dataset.

Who it is for

  • Developers building Bengali NLP prototypes who need a pretrained language model.
  • Researchers and practitioners evaluating Bengali text classification or named-entity recognition.
  • Users experimenting with masked-word prediction in Bengali.

Documented uses

  • Tokenize Bengali text using the model's vocabulary.
  • Generate candidate words for a masked position in Bengali text.
  • Use a pretrained Bengali model as a starting point for NLP task experiments and evaluation.
Before you choose

Requirements & limits

The repository is public and lists the MIT license. The pretrained model is linked from the README on Hugging Face.

  • The documented model is Bangla BERT Base, with a 12-layer, 768-hidden, 12-head architecture and 110 million parameters.
  • The README links to a PyTorch model on Hugging Face; its TensorFlow-version cell is marked with dashes.
  • The project publishes benchmark results, but outcomes may differ by dataset and setup.
  • The supplied examples use Hugging Face Transformers; runtime platform and version requirements are not specified.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Load the Bengali tokenizer and inspect tokens

Tokenize a Bengali sentence with the pretrained Bangla Bert tokenizer.

Before you start

  • Python environment with the Hugging Face Transformers package available.
  • Access to the pretrained model identifier sagorsarker/bangla-bert-base.

Steps

  1. Load AutoTokenizer with from_pretrained("sagorsarker/bangla-bert-base").

  2. Pass Bengali text to the tokenizer's tokenize method.

  3. Inspect the returned token sequence.

Expected outcome

A Bengali sentence is represented as the model's token sequence; the README illustrates this with “আমি বাংলায় গান গাই।”

Documentation 1 ↗
Source-based practical guide

Generate masked-word predictions

Use Bangla Bert to obtain candidate tokens for a masked position in Bengali text.

Before you start

  • Python environment with the Hugging Face Transformers package available.
  • Access to the pretrained model identifier sagorsarker/bangla-bert-base.

Steps

  1. Load BertForMaskedLM and BertTokenizer from the pretrained model identifier.

  2. Create a Transformers pipeline using the fill-mask task, model and tokenizer.

  3. Place the tokenizer's mask token in a Bengali sentence and pass the sentence to the pipeline.

  4. Inspect the returned predictions.

Expected outcome

The pipeline returns candidate completions and scores for the masked position.

Guide limitations

  • The repository provides an example but does not specify library versions, runtime platform requirements or performance expectations for other text and environments.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
16 October 2026
Original website title
sagorbrur/bangla-bert · Official homepage title · retained in its source language
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