Bangla NER — Named Entity Recognition for Bengali

A fine-tuned token classification model for Bengali (Bangla) Named Entity Recognition using the BIO tagging scheme. Built on top of csebuetnlp/banglabert (ELECTRA-based).

Entity Types

All tags follow BIO format: B- (beginning), I- (inside), O (outside).

Training Details

Test Set Results (Overall)

Per-Entity Results (Test Set)

Usage

With pipeline (recommended)

from transformers import pipeline

ner = pipeline(
    "ner",
    model="arafatfahim/BanglaTag",
    aggregation_strategy="simple",
)

text = "একেএম শহীদুল হক বাংলাদেশে কক্সবাজার এলাকায় সোমবার সংবাদ সম্মেলন করেন"
print(ner(text))

Manual inference

from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch

model_name = "arafatfahim/BanglaTag"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)
model.eval()

tokens = ["একেএম", "শহীদুল", "হক", "বাংলাদেশে", "এসেছেন"]
inputs = tokenizer(tokens, is_split_into_words=True, return_tensors="pt")

with torch.no_grad():
    logits = model(**inputs).logits

predictions = logits.argmax(-1)[0]
word_ids = inputs.word_ids()

prev = None
for word_id, pred_id in zip(word_ids, predictions):
    if word_id is None or word_id == prev:
        continue
    print(f"{tokens[word_id]:20s} → {model.config.id2label[pred_id.item()]}")
    prev = word_id

Citation

If you use this model, please cite:

@misc{bangla-ner-2026,
  title  = {Bangla NER: Fine-tuned BanglaBERT for Bengali Named Entity Recognition},
  year   = {2026},
  url    = {https://huggingface.co/arafatfahim/BanglaTag}
}