Create README.md

README.md

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+---
+license: other
+language:
+- en
+tags:
+- synthetic
+- english
+---
+# Synthetic English Language Acquisition Dataset (3GB)
+
+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.
+
+## Dataset Structure
+
+The dataset contains four primary columns:
+
+| 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`). |
+
+---
+
+## Data Composition
+
+The records are distributed across three distinct record types:
+
+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).
+
+---
+
+## Usage
+
+### Loading with Hugging Face `datasets`
+
+```python
+from datasets import load_dataset
+
+# Load the entire dataset
+dataset = load_dataset("{REPO_ID}")
+
+# Inspect a sample
+print(dataset['train'][0])