---
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])