--- 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](https://onnxruntime.ai/docs/tutorials/web/), so nothing you type is sent anywhere. ## What it does

The tinyfix demo correcting “I recieve the report tomorow and we saw there new house.” as it is typed

| 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 `` 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 ``.

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](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu), [SmolTalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk) and [OpenAssistant](https://huggingface.co/datasets/OpenAssistant/oasst1), 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.}

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

94%

31%

~3 ms

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