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--- |
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license: mit |
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task_categories: |
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- translation |
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language: |
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- en |
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- hi |
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tags: |
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- machine-translation |
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- english-hindi |
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- parallel-corpus |
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- synthetic-data |
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- large-scale |
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- nlp |
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- benchmark |
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- seq2seq |
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- huggingface-dataset |
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size_categories: |
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- 1M<n<10M |
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--- |
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# 📘 README.md |
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👉 Copy everything below into your repository `README.md` |
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--- |
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# English–Hindi Massive Synthetic Translation Dataset |
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## 🧠 Overview |
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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. |
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The corpus contains **10 million aligned sentence pairs** generated using a high-entropy template engine with: |
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* 100+ subjects |
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* 100+ verbs |
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* 100+ objects |
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* 100+ adjectives, adverbs, metrics, conditions, and scales |
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* Structured bilingual phrase composition |
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* Deterministic alignment between English and Hindi |
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This produces **trillions of possible combinations**, ensuring minimal repetition even at massive scale. |
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--- |
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## 📦 Dataset Structure |
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``` |
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hf_translation_dataset/ |
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├── train.jsonl (8,000,000 sentence pairs) |
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├── test.jsonl (2,000,000 sentence pairs) |
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└── README.md |
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``` |
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Split ratio: |
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* **Training:** 80% |
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* **Testing:** 20% |
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--- |
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## 🧾 Data Format |
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Each line is a JSON object: |
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```json |
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{ |
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"id": 934221, |
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"en": "AI engineer efficiently_42 build systems condition_17 metric_88 remains optimized_12 and optimized_91 scale_55", |
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"hi": "एआई इंजीनियर सिस्टम को कुशलता_42 निर्माण करते हैं स्थिति_17 मेट्रिक_88 अनुकूलित_12 और अनुकूलित_91 पैमाना_55" |
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} |
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``` |
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### Fields |
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| Field | Type | Description | |
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| -------- | ------- | ------------------------ | |
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| `id` | Integer | Unique sample identifier | |
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| `en` | String | English sentence | |
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| `hi` | String | Hindi translation | |
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| Encoding | UTF-8 | Unicode safe | |
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--- |
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## 📊 Dataset Characteristics |
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* ✔️ Total samples: **10,000,000** |
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* ✔️ Language pair: **English → Hindi** |
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* ✔️ Vocabulary size: **100+ per lexical category** |
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* ✔️ Combinatorial space: **>10¹⁴ unique pairs** |
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* ✔️ Grammar-driven generation |
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* ✔️ Balanced template distribution |
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* ✔️ Deterministic alignment |
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* ✔️ Streaming-friendly JSONL format |
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--- |
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## 🎯 Intended Use |
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This dataset is suitable for: |
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* Machine translation benchmarking |
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* Seq2Seq model stress testing |
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* Tokenizer robustness analysis |
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* Curriculum learning experiments |
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* Large-scale distributed training validation |
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* Synthetic data research |
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* Parallel corpus augmentation |
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--- |
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## ⚠️ Limitations |
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* Synthetic grammar (not natural conversational Hindi). |
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* No discourse-level coherence. |
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* No idiomatic expressions or cultural nuance. |
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* Artificial tokens (`optimized_42`, etc.) are symbolic placeholders. |
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* Not suitable for production translation systems. |
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This dataset is intended for **algorithmic benchmarking and scaling research**. |
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--- |
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## 🤗 How to Load |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("NNEngine/your-dataset-name") |
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print(dataset) |
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``` |
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Streaming mode: |
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```python |
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dataset = load_dataset( |
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"NNEngine/your-dataset-name", |
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streaming=True |
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) |
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``` |
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--- |
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## 📜 License |
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MIT License |
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Free for research and educational usage. |
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--- |
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## ✨ Author |
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Created by **NNEngine** for large-scale NLP benchmarking and synthetic data research. |
