| --- |
| license: other |
| language: |
| - en |
| tags: |
| - synthetic |
| - english |
| --- |
| # Synthetic English Language Acquisition Dataset (3GB) |
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| A structured, 3GB synthetic CSV dataset generated to assist in pretraining or fine-tuning Language Models (LLMs) on core English syntax, vocabulary, narrative structures, and explicit grammar rules. |
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| ## Dataset Structure |
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| The dataset contains four primary columns: |
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| | Column Name | Data Type | Description | |
| | :--- | :--- | :--- | |
| | `data_type` | `string` | Categorises the entry (`sentence`, `narrative_chunk`, or `grammar_instruction`). | |
| | `text_content` | `string` | The target sentence, paragraph, or example usage. | |
| | `grammar_rule` | `string` | The named linguistic/grammatical rule (where applicable, otherwise `N/A`). | |
| | `explanation` | `string` | Detailed explanation of the underlying grammar principle (otherwise `N/A`). | |
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| --- |
|
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| ## Data Composition |
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| The records are distributed across three distinct record types: |
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| 1. **Sentences (60%):** Structured single sentences testing varied syntactic patterns (Subject-Verb-Object, Adverbial modifications, and Complex Connectives). |
| 2. **Narrative Chunks (30%):** Multi-sentence paragraphs designed to demonstrate contextual flow and discourse continuity. |
| 3. **Grammar Instructions (10%):** Explicit instruction pairs providing rules, examples, and detailed explanations (e.g., Subject-Verb Agreement, Article Usage). |
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| --- |
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| ## Usage |
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| ### Loading with Hugging Face `datasets` |
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| ```python |
| from datasets import load_dataset |
| |
| # Load the entire dataset |
| dataset = load_dataset("{REPO_ID}") |
| |
| # Inspect a sample |
| print(dataset['train'][0]) |
