Update README.md

README.md

@@ -1,3 +1,129 @@
 ---
+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** (**B**engali **L**anguage **U**nderstandin**G** **E**valuation), a balanced benchmark for evaluating Bengali natural language understanding. See the full [BLUGE collection](https://huggingface.co/collections/nahid-hub/bluge) for all 7 tasks, and the [B-CORE](https://huggingface.co/datasets/nahid-hub/B-CORE-bengali-corpus) 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
+
+```python
+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:
+
+```python
+from datasets import load_dataset
+
+test_ds = load_dataset("nahid-hub/BLUGE-bengali-nli", split="test")
+```
+
+Or read the Parquet files directly with pandas:
+
+```python
+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:
+
+```bibtex
+@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}
+}
+```