README.md

metadata

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).

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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).

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Usage

Loading with Hugging Face datasets

from datasets import load_dataset

# Load the entire dataset
dataset = load_dataset("{REPO_ID}")

# Inspect a sample
print(dataset['train'][0])