Datasets:

Gugu8
/

English-Mini

license: other
license_name: open-data-attribution-training-disclosure-license-odatl-1.0
license_link: LICENSE
language:
  - en
tags:
  - tiny
  - english

LLM-English-100MB — Compact & Dense English Teaching Corpus

A 100MB, extremely clean CSV designed to teach an LLM English from scratch via instruction-tuning. No noise, no HTML, no duplicates — just pure grammar, vocabulary, and syntax transformations.

Generated with a single paste-and-run Python script in Google Colab.

Why this teaches English

Instead of raw text, the dataset is instruction -> input -> output pairs that force the model to learn rules:

Grammar Mechanics: tense conversion, negation, question formation, active/passive, subject-verb agreement, article & preposition usage, contraction
Vocabulary: pluralization, synonyms, antonyms, definitions, comparatives
Error Correction: common ESL mistakes with corrected form + rule

Each row includes an explicit rule column so the model learns the pattern, not just memorizes.

Dataset Schema

Extremely clean: UTF-8, LF (\n), QUOTE_MINIMAL, no empty fields, no newlines inside fields.

Column	Type	Description
`id`	int	Unique row ID
`level`	string	CEFR level: A1, A2, B1, B2
`category`	string	`grammar`, `vocabulary`, `syntax`
`task_type`	string	e.g. `tense_past`, `pluralization`, `negation`, `question_formation`, `comparative`, `active_passive`, `article_usage`, `synonym`, `antonym`
`instruction`	string	What the model must do
`input_text`	string	Input sentence / prompt
`output_text`	string	Correct target
`rule`	string	Short linguistic rule

Example:

instruction: Convert to simple past tense.
input_text: We build the house.
output_text: We built the house.
rule: build -> built

Stats

Size: 100 MB (configurable)
Rows: ~650k - 750k (avg ~150 bytes/row)
Format: CSV
Encoding: UTF-8
Generation Time: ~90-120 seconds on Colab

How to Use

Pandas:

import pandas as pd
df = pd.read_csv("/content/llm_english_100MB.csv")
df.head()

Hugging Face Datasets:

from datasets import load_dataset
ds = load_dataset("csv", data_files="/content/llm_english_100MB.csv")["train"]
# Format for instruction tuning: instruction + input -> output
def format_prompt(ex):
    return {"text": f"### Instruction: {ex['instruction']}\n### Input: {ex['input_text']}\n### Output: {ex['output_text']}"}
ds = ds.map(format_prompt)

Training Prompt Template:

Below is an instruction that teaches English.

### Instruction:
Convert to simple past tense.

### Input:
She eats the apple.

### Output:
She ate the apple.

Quality Guarantees — EXTREMELY CLEAN

No HTML, no URLs, no emojis
No nulls / NaNs
Stripped whitespace, no \r, no \n inside fields
Deterministic seed (42) for reproducibility
Validated with csv.DictReader
All rows are synthetic and license-free

Customize

Edit vocab lists at top of script:

NOUNS = [...]
VERBS = [...]

Add new generators to GENERATORS list to add new task types.

License

Open Data Attribution Training Disclosure License (ODATL‑1.0)

File Structure

/content/
  llm_english_100MB.csv  # 100MB corpus
  README.md              # this file