---
license: apache-2.0
base_model: t5-small
tags:
- grammar-correction
- t5
- text2text-generation
language:
- en
---
# Proximity Grammar Corrector (T5-small)
A small fine-tuned [T5-small](https://huggingface.co/t5-small) model for **English grammar correction**. Part of the [Proximity](https://github.com/) project — a lightweight background tool that fixes grammar via a global hotkey.
## What this model does
Takes a sentence with grammar mistakes and outputs a corrected version. Examples:
| Input | Output |
|---|---|
| She dont like going to the store. | She doesn't like going to the store. |
| He are moving here. | He is moving here. |
| I has went to the market yesterday. | I have gone to the market yesterday. |
| They was happy about they new house. | They were happy about their new house. |
## What this model does NOT do
- It is **not a chat model**. It will not hold a conversation or answer questions.
- It is **not a style/tone rewriter**. It targets grammatical correctness, not voice or wording improvements.
- It struggles with some irregular verb forms not well represented in training data (e.g. "drinked" instead of "drank" was not corrected in testing).
- It is trained on a small dataset (~6,000 sentence pairs), so coverage of rare or complex grammatical errors is limited.
## Training details
- **Base model:** t5-small (~60M parameters)
- **Dataset:** [Owishiboo/grammar-correction](https://huggingface.co/datasets/Owishiboo/grammar-correction) (~6,000 ungrammatical → grammatical sentence pairs)
- **Epochs:** 6
- **Final train loss:** 0.1685
- **Final eval loss:** ~0.186
## Usage
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from\_pretrained("EnderAir/proximity")
model = T5ForConditionalGeneration.from\_pretrained("EnderAir/proximity")
text = "grammar: He are moving here."
inputs = tokenizer(text, return\_tensors="pt")
outputs = model.generate(\*\*inputs, max\_length=64, num\_beams=5)
print(tokenizer.decode(outputs\[0], skip\_special\_tokens=True))
\# "He is moving here."
Note: always prefix input text with "grammar: " — this is the T5 task prefix convention used during training.
## License
Apache 2.0