add model card

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README.md

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+---
+library_name: onnx
+language: en
+tags:
+  - autocorrect
+  - spelling-correction
+  - browser
+  - onnx
+datasets:
+  - HuggingFaceFW/fineweb-edu
+  - HuggingFaceTB/smoltalk
+  - OpenAssistant/oasst1
+---
+
+# tinyfix
+
+**Spell check can't tell that "form" was meant to be "from", since both are real words. This 2.1 MB model can.**
+
+![The tinyfix demo correcting "I recieve the report tomorow and we saw there new house." as it is typed](tinyfix.png)
+
+**[Try it in your browser](https://huggingface.co/spaces/cfahlgren1/tinyfix)**
+
+It 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.
+
+| Parameters | Model size | Gzipped | Time per check |
+|---|---|---|---|
+| **1.9M** | **2.1 MB** | **1.6 MB** | **About 3 ms** in Chrome |
+
+## 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 |
+
+## How well it works
+
+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 | **19%** | **94%** | About 1 in 2,500 | About 1 in 1,700 |
+| Eager | **31%** | **94%** | About 1 in 910 | About 1 in 430 |
+
+Careful is the default. The demo runs Eager, so you see more fixes.
+
+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.
+
+| 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](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.