README.md

metadata

library_name: onnx
language: en
tags:
  - autocorrect
  - spelling-correction
  - browser
  - onnx
  - offline-page
datasets:
  - HuggingFaceFW/fineweb-edu
  - HuggingFaceTB/smoltalk
  - OpenAssistant/oasst1

Spell check can't tell that “form” was meant to be “from”,
since both are real words. This 2.1 MB model can.

Try the demo →   ·   Write with it in offline.page →

tinyfix reads the words on both sides of a correctly spelled word and picks the best fit from nearby real words. It runs on the page with ONNX Runtime Web, so nothing you type is sent anywhere.

What it does

You type	It becomes
I will send it form home tomorrow.	I will send it from home tomorrow.
Let me now if that works.	Let me know if that works.
We saw there new house.	We saw their new house.
Please fill out the form and return it.	No change. “form” is right here.

How well it works

94% of its fixes are right

31% of real-word typos fixed in Eager mode

~3 ms per check in Chrome

Measured on 2,460 real-word typos from GitHub commits and 45,000 correct words of email and documentation that it never trained on.

Setting	Real-word typos fixed	Fixes that were right	Correct email words changed	Correct GitHub words changed
Careful (default)	19%	94%	about 1 in 2,500	about 1 in 1,700
Eager (demo and offline.page)	31%	94%	about 1 in 910	about 1 in 430

It also helps choose the right fix for misspelled words, raising that fix rate from 56% to 61.5% while 96.6% of fixes stay right.

What it misses

• It misses more real-word typos than it catches, including “don” typed for “do”.
• It can only judge the 20,000 words it knows, so a rarer word like “ware” typed for “were” is left alone.
• It needs the words that come after, so these fixes land a couple of words later.
• English only.

Using the model

The model scores candidate words for one position in a sentence. Higher scores fit better, and comparing each candidate to the typed word tells you whether a swap is worth making.

Inputs and outputs

Name	Shape	What it holds
left (input)	[batch, 16] int64	The words before, left-padded with 0, starting with <s> when there are fewer than 16
right (input)	[batch, 4] int64	The words after, nearest word last, left-padded with 0
candidates (input)	[batch, 9] int64	The typed word first, then up to 8 nearby words, padded with 0 at the end
scores (output)	[batch, 9] float	One score per candidate

IDs are line numbers in wrong-word-vocab.txt, starting from 0. Words are lowercased, and capitalized words in the middle of a sentence become <name>.

Files

File	What it is
wrong-word.onnx	The quantized model
wrong-word-vocab.txt	The 20,000 words it knows, one per line
wrong-word-confusions.txt	Mix-ups like “their” and “there” that are far apart on the keyboard
train_realword.py	The training script

Training

A word-level GRU that reads 16 words before and up to 4 words after, trained for 25 minutes on an A10G. It learned from 118M tokens of FineWeb-Edu, SmolTalk and OpenAssistant, by swapping real words for nearby real words and learning to swap them back.

_{tinyfix is one of two tiny models behind offline.page, a writing app that works in airplane mode.
Its partner, tinynext, suggests the next few words.}