Datasets:

NNEngine
/

English-Hindi_Translation

license: mit
task_categories:
  - translation
language:
  - en
  - hi
tags:
  - machine-translation
  - english-hindi
  - parallel-corpus
  - synthetic-data
  - large-scale
  - nlp
  - benchmark
  - seq2seq
  - huggingface-dataset
size_categories:
  - 1M<n<10M

📘 README.md

👉 Copy everything below into your repository README.md

English–Hindi Massive Synthetic Translation Dataset

🧠 Overview

This dataset is a large-scale synthetic parallel corpus for English → Hindi machine translation, designed to stress-test modern sequence-to-sequence models, tokenizers, and large-scale training pipelines.

The corpus contains 10 million aligned sentence pairs generated using a high-entropy template engine with:

100+ subjects
100+ verbs
100+ objects
100+ adjectives, adverbs, metrics, conditions, and scales
Structured bilingual phrase composition
Deterministic alignment between English and Hindi

This produces trillions of possible combinations, ensuring minimal repetition even at massive scale.

📦 Dataset Structure

hf_translation_dataset/
 ├── train.jsonl   (8,000,000 sentence pairs)
 ├── test.jsonl    (2,000,000 sentence pairs)
 └── README.md

Split ratio:

Training: 80%
Testing: 20%

🧾 Data Format

Each line is a JSON object:

{
  "id": 934221,
  "en": "AI engineer efficiently_42 build systems condition_17 metric_88 remains optimized_12 and optimized_91 scale_55",
  "hi": "एआई इंजीनियर सिस्टम को कुशलता_42 निर्माण करते हैं स्थिति_17 मेट्रिक_88 अनुकूलित_12 और अनुकूलित_91 पैमाना_55"
}

Fields

Field	Type	Description
`id`	Integer	Unique sample identifier
`en`	String	English sentence
`hi`	String	Hindi translation
Encoding	UTF-8	Unicode safe

📊 Dataset Characteristics

✔️ Total samples: 10,000,000
✔️ Language pair: English → Hindi
✔️ Vocabulary size: 100+ per lexical category
✔️ Combinatorial space: >10¹⁴ unique pairs
✔️ Grammar-driven generation
✔️ Balanced template distribution
✔️ Deterministic alignment
✔️ Streaming-friendly JSONL format

🎯 Intended Use

This dataset is suitable for:

Machine translation benchmarking
Seq2Seq model stress testing
Tokenizer robustness analysis
Curriculum learning experiments
Large-scale distributed training validation
Synthetic data research
Parallel corpus augmentation

⚠️ Limitations

Synthetic grammar (not natural conversational Hindi).
No discourse-level coherence.
No idiomatic expressions or cultural nuance.
Artificial tokens (optimized_42, etc.) are symbolic placeholders.
Not suitable for production translation systems.

This dataset is intended for algorithmic benchmarking and scaling research.

🤗 How to Load

from datasets import load_dataset

dataset = load_dataset("NNEngine/your-dataset-name")
print(dataset)

Streaming mode:

dataset = load_dataset(
    "NNEngine/your-dataset-name",
    streaming=True
)

📜 License

MIT License Free for research and educational usage.

✨ Author

Created by NNEngine for large-scale NLP benchmarking and synthetic data research.