nahid-hub/BLUGE-bengali-ner

BLUGE-NER: Bengali Named Entity Recognition

BLUGE-NER is the Named Entity Recognition task of BLUGE (Bengali Language UnderstandinG Evaluation), a 7-task benchmark for evaluating Bengali natural language understanding. See the full BLUGE collection for all 7 tasks, and the B-CORE pretraining corpus and BnLM model suite released alongside it.

Dataset Description

This task identifies named entities — persons, organizations, locations, and objects — using the IOB tagging scheme with 9 tags. In the dataset, each tag is stored as the integer ID shown below:

ID	Tag	Meaning
0	O	Outside any entity
1	B-PER	Beginning of a Person entity
2	I-PER	Inside a Person entity
3	B-ORG	Beginning of an Organization entity
4	I-ORG	Inside an Organization entity
5	B-LOC	Beginning of a Location entity
6	I-LOC	Inside a Location entity
7	B-OBJ	Beginning of an Object entity
8	I-OBJ	Inside an Object entity

Dataset Structure

Fields

Each example has two fields:

Field	Type	Description
tokens	list[string]	The tokenized sentence, one token per element.
ner_tags	list[int]	Integer label per token, aligned one-to-one with tokens (see the tag table above).

Splits

Balanced 80 / 10 / 10 split, standardized to fix the inconsistent splits found in prior Bengali benchmarks:

Split	Samples
Train	11,580
Validation	1,447
Test	1,449
Total	14,476

Usage

from datasets import load_dataset
# Load all splits
ds = load_dataset("nahid-hub/BLUGE-bengali-ner")
# Access a specific split
train_ds = ds["train"]
val_ds   = ds["validation"]
test_ds  = ds["test"]
print(train_ds[0])

Load a single split directly:

from datasets import load_dataset
test_ds = load_dataset("nahid-hub/BLUGE-bengali-ner", split="test")

Or read the Parquet files directly with pandas, without the datasets library:

import pandas as pd
train_df = pd.read_parquet(
    "hf://datasets/nahid-hub/BLUGE-bengali-ner/train.parquet"
)

Please refer to each original source's license and citation terms in addition to this dataset's license.

License

Released under CC BY 4.0. You are free to share and adapt this dataset for any purpose, including commercially, provided you give appropriate credit. Note that certain merged source components may carry their own attribution requirements — see Source Data above.

Citation

If you use this dataset, please cite:

@ARTICLE{BnLM-BLUGE-B-CORE,
  author={Hossain, Nahid and Faisal Kabir, Md.},
  journal={IEEE Access}, 
  title={Efficient Monolingual Pretraining in Low-Resource Settings Through Morphology-Aware Tokenization, Principled Corpus Denoising, and Benchmark-Driven Evaluation}, 
  year={2026},
  volume={14},
  number={},
  pages={91979-92003},
  keywords={Modeling;Multilingual;Training;Cleaning;Vocabulary;Labeling;Tokenization;Computational linguistics;Pipelines;Conferences;B-CORE;BLUGE;BnLM;corpus;evaluation benchmark;pretrained models},
  doi={10.1109/ACCESS.2026.3701520}
}