Update README.md

README.md

@@ -4,4 +4,322 @@ language:
 - hi
 - te
 pretty_name: Bengali-Hindi-Telugu
----
+---
+# BHT25: Bengali-Hindi-Telugu Parallel Corpus for Literary Machine Translation
+
+[![License: CC BY 4.0](https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)
+[![Dataset on HF](https://huggingface.co/datasets/huggingface/badges/resolve/main/dataset-on-hf-sm.svg)](https://huggingface.co/datasets/sudeshna84/BHT25)
+
+## Overview
+
+BHT25 is a high-quality trilingual parallel corpus comprising 25,000 sentence triplets across Bengali (BN), Hindi (HI), and Telugu (TE) languages. This dataset fills a critical gap in resources for cross-family Indian language machine translation, particularly for literary and culturally rich content.
+
+**Key Features:**
+- 🌐 **Three Indian Languages**: Bengali (Indo-Aryan), Hindi (Indo-Aryan), Telugu (Dravidian)
+- 📚 **Literary Domain**: Sourced from works of renowned authors including Rabindranath Tagore and Sarat Chandra Chattopadhyay
+- ✨ **Archaic Varieties**: Includes traditional Bengali Sadhu Bhasha
+- ✅ **Human-Verified**: 75.8% semantic alignment accuracy with expert validation
+- 🔍 **Unique Identifiers**: Each triplet has a unique ID for reproducibility
+- 📖 **High Quality**: Composite fluency score of 4.08/5.0
+
+## Dataset Description
+
+### Languages
+
+- **Bengali (bn)**: Eastern Indo-Aryan language, ~265M speakers
+- **Hindi (hi)**: Central Indo-Aryan language, ~600M speakers  
+- **Telugu (te)**: South-Central Dravidian language, ~95M speakers
+
+### Dataset Statistics
+
+| Metric | Bengali | Hindi | Telugu |
+|--------|---------|-------|--------|
+| **Total Sentences** | 25,000 | 25,000 | 25,000 |
+| **Total Tokens** | ~XXX,XXX | ~XXX,XXX | ~XXX,XXX |
+| **Avg. Tokens/Sentence** | ~XX | ~XX | ~XX |
+| **Vocabulary Size** | ~XX,XXX | ~XX,XXX | ~XX,XXX |
+| **Min Sentence Length** | X tokens | X tokens | X tokens |
+| **Max Sentence Length** | XXX tokens | XXX tokens | XXX tokens |
+
+### Content Characteristics
+
+The corpus covers diverse literary genres:
+- Short stories and narratives
+- Poetry and prose
+- Folk literature
+- Contemporary literary works
+- Traditional Sadhu Bhasha (classical Bengali)
+
+### Quality Metrics
+
+- **Alignment Accuracy**: 75.8% (human-validated)
+- **Semantic Consistency**: 0.873 (Bengali-Telugu), 0.71 (Hindi-based pairs via CLWE)
+- **Translation Fluency**: 4.08/5.0 (composite expert score)
+- **Inter-Annotator Agreement**: κ = 0.89
+
+## Dataset Structure
+
+### Data Format
+
+The dataset is provided in Parquet format with the following schema:
+
+```python
+{
+    'id': string,        # Unique identifier (BHT25_00001 to BHT25_25000)
+    'bn': string,        # Bengali sentence
+    'hi': string,        # Hindi sentence
+    'te': string         # Telugu sentence
+}
+```
+
+### Example
+
+```python
+{
+    'id': 'BHT25_00001',
+    'bn': 'আমি তোমাকে ভালোবাসি।',
+    'hi': 'मैं तुमसे प्यार करता हूँ।',
+    'te': 'నేను నిన్ను ప్రేమిస్తున్నాను।'
+}
+```
+
+### Data Splits
+
+The dataset is provided as a **single unified corpus** without pre-defined train/dev/test splits. This design choice allows researchers maximum flexibility to partition the data according to their specific experimental requirements. The unique identifiers enable deterministic and reproducible splitting across different studies.
+
+**Suggested Split Strategy** (for standardization):
+- Train: 80% (20,000 triplets)
+- Development: 10% (2,500 triplets)
+- Test: 10% (2,500 triplets)
+
+Users can implement splits using the unique IDs with their preferred strategy (random, stratified, etc.).
+
+## Usage
+
+### Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+# Load the full dataset
+dataset = load_dataset("sudeshna84/BHT25")
+
+# Access data
+print(f"Total samples: {len(dataset['train'])}")
+print(f"First example: {dataset['train'][0]}")
+```
+
+### Creating Train/Dev/Test Splits
+
+```python
+from datasets import load_dataset
+
+# Load dataset
+dataset = load_dataset("sudeshna84/BHT25", split="train")
+
+# Create 80-10-10 split
+train_test = dataset.train_test_split(test_size=0.2, seed=42)
+train_dataset = train_test['train']
+temp_dataset = train_test['test']
+
+# Further split test into dev and test
+dev_test = temp_dataset.train_test_split(test_size=0.5, seed=42)
+dev_dataset = dev_test['train']
+test_dataset = dev_test['test']
+
+print(f"Train: {len(train_dataset)}, Dev: {len(dev_dataset)}, Test: {len(test_dataset)}")
+```
+
+### Accessing Specific Language Pairs
+
+```python
+# Extract Bengali-Hindi pairs
+bn_hi_pairs = [(item['bn'], item['hi']) for item in dataset['train']]
+
+# Extract Bengali-Telugu pairs
+bn_te_pairs = [(item['bn'], item['te']) for item in dataset['train']]
+
+# Extract Hindi-Telugu pairs
+hi_te_pairs = [(item['hi'], item['te']) for item in dataset['train']]
+```
+
+### Integration with Translation Models
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from datasets import load_dataset
+
+# Load dataset
+dataset = load_dataset("sudeshna84/BHT25", split="train")
+
+# Load model (example: IndicTrans2)
+tokenizer = AutoTokenizer.from_pretrained("ai4bharat/indictrans2-bn-hi")
+model = AutoModelForSeq2SeqLM.from_pretrained("ai4bharat/indictrans2-bn-hi")
+
+# Prepare data for training
+def preprocess_function(examples):
+    inputs = examples['bn']
+    targets = examples['hi']
+    model_inputs = tokenizer(inputs, max_length=128, truncation=True, padding='max_length')
+    labels = tokenizer(targets, max_length=128, truncation=True, padding='max_length')
+    model_inputs["labels"] = labels["input_ids"]
+    return model_inputs
+
+tokenized_dataset = dataset.map(preprocess_function, batched=True)
+```
+
+### Data Analysis Example
+
+```python
+import pandas as pd
+from datasets import load_dataset
+
+# Load dataset
+dataset = load_dataset("sudeshna84/BHT25", split="train")
+
+# Convert to pandas for analysis
+df = pd.DataFrame(dataset)
+
+# Analyze sentence lengths
+df['bn_length'] = df['bn'].str.split().str.len()
+df['hi_length'] = df['hi'].str.split().str.len()
+df['te_length'] = df['te'].str.split().str.len()
+
+print("Average sentence lengths:")
+print(f"Bengali: {df['bn_length'].mean():.2f} tokens")
+print(f"Hindi: {df['hi_length'].mean():.2f} tokens")
+print(f"Telugu: {df['te_length'].mean():.2f} tokens")
+
+# Length distribution
+import matplotlib.pyplot as plt
+df[['bn_length', 'hi_length', 'te_length']].hist(bins=50, figsize=(15, 5))
+plt.suptitle('Sentence Length Distribution')
+plt.show()
+```
+
+## Applications
+
+This corpus can be used for various NLP tasks:
+
+1. **Machine Translation**
+   - Neural machine translation training and evaluation
+   - Cross-family translation (Indo-Aryan ↔ Dravidian)
+   - Low-resource language pair modeling
+
+2. **Cross-Lingual Analysis**
+   - Cross-lingual word embeddings evaluation
+   - Semantic similarity studies
+   - Syntactic divergence analysis
+
+3. **Multilingual NLP**
+   - Multilingual language model fine-tuning
+   - Zero-shot translation experiments
+   - Transfer learning for Indian languages
+
+4. **Linguistic Research**
+   - Comparative morphology and syntax
+   - Literary translation analysis
+   - Archaic language preservation (Sadhu Bhasha)
+
+5. **Quality Estimation**
+   - Automatic alignment algorithm evaluation
+   - Translation quality metrics benchmarking
+
+## Methodology
+
+### Data Collection
+
+The corpus was constructed through:
+1. **Source Selection**: Literary texts from renowned Indian authors
+2. **Sentence Extraction**: Language-specific boundary detection
+3. **Alignment**: Hybrid Gale-Church algorithm enhanced with cross-lingual word embeddings (CLWE)
+4. **Validation**: Manual verification by native speakers
+
+### Quality Assurance
+
+Each sentence triplet underwent:
+- Automated preprocessing and alignment scoring
+- Manual verification for semantic equivalence
+- Expert review for fluency and naturalness
+- Inter-annotator agreement measurement (κ = 0.89)
+
+### Alignment Algorithm
+
+A hybrid approach combining:
+- Gale-Church length-based alignment
+- Cross-lingual word embedding (FastText) similarity
+- Threshold-based filtering (score ≥ 0.7)
+- Human expert correction
+
+## Citation
+
+If you use this dataset in your research, please cite:
+
+```bibtex
+@article{sani2024bht25,
+  title={A Bengali-Hindi-Telugu Parallel Corpus for Enhanced Literary Machine Translation},
+  author={Sani, Sudeshna and Gangashetty, Suryakanth V and Samudravijaya, K and Nandi, Anik and Priya, Aruna and Kumar, Vineeth and Dubey, Akhilesh Kumar},
+  journal={Data in Brief},
+  year={2024},
+  publisher={Elsevier}
+}
+```
+
+**Related Research:**
+```bibtex
+@article{sani2024esa,
+  title={Emotion-Semantic-Aware Neural Machine Translation for Bengali-Hindi-Telugu},
+  author={Sani, Sudeshna and others},
+  journal={IEEE Access},
+  year={2024},
+  doi={10.1109/ACCESS.2024.XXXXXXX}
+}
+```
+
+## License
+
+This dataset is released under the [Creative Commons Attribution 4.0 International License (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/).
+
+**You are free to:**
+- Share — copy and redistribute the material in any medium or format
+- Adapt — remix, transform, and build upon the material for any purpose, even commercially
+
+**Under the following terms:**
+- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made
+
+## Ethical Considerations
+
+- All source texts are either in the public domain or used with explicit permission
+- Author attributions are maintained in metadata
+- No personal or sensitive information is included
+- The dataset supports linguistic diversity and cultural preservation
+
+## Authors and Affiliations
+
+**Sudeshna Sani**¹, Suryakanth V Gangashetty¹, Samudravijaya K¹*, Anik Nandi², Aruna Priya³, Vineeth Kumar³, Akhilesh Kumar Dubey¹
+
+¹ Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India  
+² School of Business, Woxsen University, Hyderabad, Telangana, India  
+³ School of Technology, Woxsen University, Hyderabad, Telangana, India
+
+*Corresponding author
+
+## Contact
+
+For questions, issues, or collaborations, please:
+- Open an issue in this repository
+- Contact: [your.email@klef.edu.in]
+
+## Acknowledgments
+
+We thank the native speakers who participated in the manual verification process and the literary estates for permission to use source materials.
+
+## Dataset Card
+
+For more detailed information about the dataset construction, evaluation metrics, and validation procedures, please refer to our paper in *Data in Brief*.
+
+---
+
+**Last Updated**: December 2024  
+**Version**: 1.0  
+**DOI**: [Will be added upon publication]