| --- |
| 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 |
