\---

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



```python

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