| \--- | |
| 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 | |