| --- |
| license: mit |
| task_categories: |
| - text-generation |
| - translation |
| language: |
| - en |
| - hi |
| tags: |
| - stories |
| - parallel-corpus |
| - hindi |
| - english |
| - simple-stories |
| - synthetic |
| size_categories: |
| - 1M<n<10M |
| --- |
| |
| # Simple-Stories-Eng-Hin |
|
|
| A parallel English–Hindi dataset of short, simple stories. Each row contains |
| the same story written in English and its Hindi counterpart, making it useful |
| for training or evaluating small language models, translation systems, and |
| Hindi text generation models on simple narrative text. |
|
|
| ## Dataset Details |
|
|
| - **Rows:** ~1.72M |
| - **Format:** JSON (auto-converted to Parquet) |
| - **Split:** `train` (single split) |
| - **Size:** ~9.38 GB |
| - **License:** MIT |
|
|
| ### Columns |
|
|
| | Column | Type | Description | |
| |---|---|---| |
| | `english` | string | A short story in English (~231–2,570 characters) | |
| | `hindi` | string | The corresponding story in Hindi (~186–3,040 characters) | |
|
|
| ## Dataset Structure |
|
|
| Each example is a story pair covering everyday, fairy-tale, and |
| adventure-style narratives — talking animals, curious children, magical |
| objects, treasure maps, and similar simple, self-contained plots. Stories are |
| short (roughly a paragraph to a few paragraphs) and written in |
| easy-to-follow language, similar in spirit to the TinyStories / SimpleStories |
| style of datasets. |
|
|
| Example (truncated): |
|
|
| ```json |
| { |
| "english": "Key turned in the old lock. A girl named Lily held her breath...", |
| "hindi": "चाबी पुराने ताले में घूम गई। लिली नाम की एक लड़की ने अपनी सांस रोकी..." |
| } |
| ``` |
|
|
| ## Applications & Use Cases |
|
|
| **Language modeling** |
| - Pretraining or fine-tuning small/efficient LLMs (SimpleStories/TinyStories-style) for Hindi or bilingual generation |
| - Studying how small models acquire grammar and narrative coherence in a low-resource language like Hindi |
| - Distillation: using story pairs as a compact training signal for smaller student models |
|
|
| **Machine translation** |
| - Training or fine-tuning English↔Hindi translation models |
| - Evaluating translation quality on simple, unambiguous sentence structures where errors are easy to spot |
| - Building baselines before scaling to harder, domain-specific parallel corpora |
|
|
| **Evaluation & benchmarking** |
| - Benchmarking Hindi text generation fluency and coherence |
| - Testing cross-lingual transfer (e.g. English-pretrained model fine-tuned on Hindi) |
| - Sanity-checking tokenizer or vocabulary coverage for Devanagari script |
|
|
| **Education & accessibility** |
| - Bilingual reading practice or language-learning apps (parallel sentence/story display) |
| - Generating graded reading material for Hindi learners |
| - Text-to-speech or read-along tools needing simple, clean narrative text |
|
|
| **Other** |
| - Data augmentation for children's-story or creative-writing generators in Hindi |
| - Seed data for synthetic dataset generation (e.g. prompting larger models to write more stories in this style) |
|
|
| ## Notes |
|
|
| - The Hindi text is a translation of the English story rather than an |
| independently authored story, so both columns describe the same narrative. |
| - Given the simple, formulaic story style, users working with this data for |
| translation quality benchmarks should be aware the vocabulary and sentence |
| structure are intentionally simple. |
|
|
| ## Loading the Dataset |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("bhasha-labs/Simple-Stories-Eng-Hin") |
| print(ds["train"][0]) |
| ``` |
|
|
| ## License |
|
|
| Released under the MIT License. |
