metadata
license: mit
task_categories:
- text-generation
language:
- en
tags:
- spelling
- grammar
- typo-correction
- phonetic
- evaluation
size_categories:
- n<1K
pretty_name: Open Spell Evaluation Set
gated: manual
extra_gated_prompt: >
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Open Spell — Evaluation Dataset
A small, hand-curated set of misspelled / corrected pairs used to validate the Open Spell correction kit across four categories: plain spell, grammar (segmentation), phonetic, and domain.
Not a training set. SymSpell is non-parametric and does not need one. This dataset is for regression testing and reporting top-1 accuracy.
Schema
Each line of samples.jsonl is one example:
{
"id": "spell-001",
"category": "spell",
"original": "I lik aples",
"corrected": "I like apples",
"domain_terms": []
}
| Field | Type | Description |
|---|---|---|
id |
string | {category}-{index} |
category |
enum | spell, grammar, phonetic, or domain |
original |
string | Input text with errors |
corrected |
string | Expected output |
domain_terms |
string[] | Vocabulary to load before correcting (domain category only) |
Loading
import json
from pathlib import Path
samples = [json.loads(l) for l in Path("samples.jsonl").read_text().splitlines()]
Or with datasets:
from datasets import load_dataset
ds = load_dataset("json", data_files="samples.jsonl")
Categories
| Category | Count | What it tests |
|---|---|---|
spell |
15 | Single-word typos, capitalization, trailing punctuation |
grammar |
15 | Missing spaces, extra spaces, compound segmentation |
phonetic |
10 | Sounds-like substitutions (fone → phone) |
domain |
10 | Words only correctable with injected custom vocabulary |
License
MIT. Examples written from scratch — no scraped or copyrighted material.