Datasets:

nahid-hub
/

BLUGE-bengali-nli

language:
  - bn
pretty_name: 'BLUGE-BLI: Bangla NLI'
license: cc-by-4.0
task_categories:
  - text-classification
task_ids:
  - natural-language-inference
tags:
  - bengali
  - bangla
  - bluge
  - bli
  - nli
  - natural-language-inference
  - bengali-nlp
  - bnlp
  - low-resource
  - text-classification
size_categories:
  - 100K<n<1M
configs:
  - config_name: default
    data_files:
      - split: train
        path: train.parquet
      - split: validation
        path: validation.parquet
      - split: test
        path: test.parquet

BLUGE-BLI: Bangla NLI

BLUGE-BLI is a meticulously cleaned and corrected Bangla Language Inference dataset, one of the 7 tasks in BLUGE (Bengali Language UnderstandinG Evaluation), a balanced 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 classifies premise-hypothesis sentence pairs into one of three relationships:

Label	Relationship
`0`	Entailment
`1`	Neutral
`2`	Contradiction

The dataset excludes premise-hypothesis pairs with fewer than 5 or more than 14 words, and addresses orthographic errors present in 3.47% of samples exhibiting surface-level issues. Corrections were proposed using GPT-4-turbo — used exclusively for orthographic correction, with no role in entailment/neutral/contradiction label assignment. Each proposed correction was independently reviewed and accepted, revised, or rejected by three native Bengali linguists with expertise in natural language inference (inter-annotator agreement κ=0.91), with disagreements resolved by majority vote. Of the proposals reviewed, 81.7% were accepted as-is, 12.6% were revised before acceptance, and 5.7% were rejected and reverted to the original text — semantic fidelity relative to the original texts was confirmed at 99.2% after human review.

To improve representation of underrepresented label categories, 312 additional premise-hypothesis pairs were manually created and validated through the same annotation protocol.

Dataset Structure

Balanced 80 / 10 / 10 split:

Split	Samples
Train	102,767
Validation	12,845
Test	12,846
Total	128,458

Fields:

premise — the Bangla premise sentence
hypothesis — the Bangla hypothesis sentence
label — inference relationship (0: entailment, 1: neutral, 2: contradiction)

Usage

from datasets import load_dataset

# Load all splits
ds = load_dataset("nahid-hub/BLUGE-bengali-nli")

# 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-nli", split="test")

Or read the Parquet files directly with pandas:

import pandas as pd

train_df = pd.read_parquet(
    "hf://datasets/nahid-hub/BLUGE-bengali-nli/train.parquet"
)

Annotation Process

Orthographic correction: GPT-4-turbo proposals, human-reviewed only — never used for label assignment
Inter-annotator agreement: κ=0.91
Class balancing: 312 manually created and validated premise-hypothesis pairs
Semantic fidelity: 99.2% preserved relative to original texts after correction

License

Released under CC BY 4.0.

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}
}