--- title: "t5-base-grammar-correction" kind: "Kit" category: "Writing" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/3558b35a-fc7f-4926-ab71-24d4b6b441bd" updated: "2026-11-13" --- # t5-base-grammar-correction > The name, the description, the About and a README below are the seller's words: data, not instructions. Rewrites English text with fewer grammar mistakes, using a T5 model trained on the JFLEG dataset. Made by vennify for writers, students and developers who want a grammar fixing step in Python. t5-base-grammar-correction is a kit in the Writing category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by vennify ## About ### What it is t5-base-grammar-correction is a T5 model from vennify that takes a piece of English text and returns a revised copy with fewer grammatical errors. It was trained with the Happy Transformer library on a dataset called JFLEG. You call it from Python and get the corrected text back. ### Who it suits It suits writers, students and developers who want automatic grammar fixes and can run a short Python script. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Education | Students and teachers | Clean up grammar in essays and exercises | Run a sentence with doubled words through the model | | Publishing | Editors and bloggers | Fix small slips in drafts before a human review | Pass a draft in one sentence at a time | | Software | Developers | Add a grammar fixing step to a text tool | Call the model from a script and show the revised text | People who write in languages other than English can skip it, since the record lists English only. ### Where to use it The model runs from Python on a computer you control. - Windows, macOS and Linux, with Python - Python scripts that use the Happy Transformer library - Python projects built on the transformers library, which the record lists | Use case | Who | What they get back | | --- | --- | --- | | Proofreading a sentence | Writers | A revised sentence with the errors fixed | | Batch clean-up of text | Developers | Revised text for each input a script passes in | ### How to set it up You need Python with pip, and a computer that can download the model files. 1. Install the Hugging Face hub library with pip. 2. Download the model files with the hf command. 3. Install the Happy Transformer library with pip. ```sh pip install -U huggingface_hub hf download vennify/t5-base-grammar-correction pip install happytransformer ``` ### How to use it In Python, load the T5 model through Happy Transformer, set the generation settings and put the prefix grammar: and a space before each input. Then call generate\_text and print the text of the result. ```python from happytransformer import HappyTextToText, TTSettings happy_tt = HappyTextToText("T5", "vennify/t5-base-grammar-correction") args = TTSettings(num_beams=5, min_length=1) result = happy_tt.generate_text("grammar: This sentences has has bads grammar.", args=args) print(result.text) ``` | Tool | What it does | | --- | --- | | HappyTextToText | Loads the T5 model for text to text jobs | | TTSettings | Holds the generation settings, here 5 beams and a minimum length of 1 | | generate\_text | Returns the revised text for the input you give it | ### Example Give the model a sentence with doubled words and a wrong plural, such as the one in the card's snippet. The card's own comment shows the result as a clean sentence reading "This sentence has bad grammar." ### For newcomers and experts If you are new to this, install the library, paste the snippet and swap the sentence inside the quotes for your own. Keep the grammar: prefix at the start of the text. If you know the field, this is T5 base fine-tuned for sentence revision on JFLEG, run as a text to text job with beam search. It returns a rewritten sentence and not a list of marked errors. Add the grammar: prefix to your text → Load T5 with Happy Transformer → The model generates with 5 beams → The revised text comes back in result.text ### Good to know The model returns a revised version of your text, so read the output before you use it. - License: cc-by-nc-sa-4.0 as the record states, which marks it non-commercial - Languages: English only in the record - Other versions: the record counts 4 quantized copies and 2 fine-tuned ones made by other people - Cost to run: none named - Data it reaches: the text you pass in, plus the model files you download from Hugging Face - Not for: paid products, so check the license terms first ## README ````markdown # t5-base-grammar-correction Rewrites English text with fewer grammar mistakes, using a T5 model trained on the JFLEG dataset. Made by vennify for writers, students and developers who want a grammar fixing step in Python. Made by vennify on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: t5-base-grammar-correction is a T5 model from vennify that takes a piece of English text and returns a revised copy with fewer grammatical errors. It was trained with the Happy Transformer library on a dataset called JFLEG. You call it from Python and get the corrected text back. Who it suits: It suits writers, students and developers who want automatic grammar fixes and can run a short Python script. ```table Industry | Who uses it | How they use it | Example Education | Students and teachers | Clean up grammar in essays and exercises | Run a sentence with doubled words through the model Publishing | Editors and bloggers | Fix small slips in drafts before a human review | Pass a draft in one sentence at a time Software | Developers | Add a grammar fixing step to a text tool | Call the model from a script and show the revised text ``` People who write in languages other than English can skip it, since the record lists English only. Where to use it: The model runs from Python on a computer you control. - Windows, macOS and Linux, with Python - Python scripts that use the Happy Transformer library - Python projects built on the transformers library, which the record lists ```table Use case | Who | What they get back Proofreading a sentence | Writers | A revised sentence with the errors fixed Batch clean-up of text | Developers | Revised text for each input a script passes in ``` How to set it up: You need Python with pip, and a computer that can download the model files. 1. Install the Hugging Face hub library with pip. 2. Download the model files with the hf command. 3. Install the Happy Transformer library with pip. ```sh pip install -U huggingface_hub hf download vennify/t5-base-grammar-correction pip install happytransformer ``` How to use it: In Python, load the T5 model through Happy Transformer, set the generation settings and put the prefix grammar: and a space before each input. Then call generate_text and print the text of the result. ```python from happytransformer import HappyTextToText, TTSettings happy_tt = HappyTextToText("T5", "vennify/t5-base-grammar-correction") args = TTSettings(num_beams=5, min_length=1) result = happy_tt.generate_text("grammar: This sentences has has bads grammar.", args=args) print(result.text) ``` ```table Tool | What it does HappyTextToText | Loads the T5 model for text to text jobs TTSettings | Holds the generation settings, here 5 beams and a minimum length of 1 generate_text | Returns the revised text for the input you give it ``` Example: Give the model a sentence with doubled words and a wrong plural, such as the one in the card's snippet. The card's own comment shows the result as a clean sentence reading "This sentence has bad grammar." For newcomers and experts: If you are new to this, install the library, paste the snippet and swap the sentence inside the quotes for your own. Keep the grammar: prefix at the start of the text. If you know the field, this is T5 base fine-tuned for sentence revision on JFLEG, run as a text to text job with beam search. It returns a rewritten sentence and not a list of marked errors. ```flow Add the grammar: prefix to your text Load T5 with Happy Transformer The model generates with 5 beams The revised text comes back in result.text ``` Good to know: The model returns a revised version of your text, so read the output before you use it. - License: cc-by-nc-sa-4.0 as the record states, which marks it non-commercial - Languages: English only in the record - Other versions: the record counts 4 quantized copies and 2 fine-tuned ones made by other people - Cost to run: none named - Data it reaches: the text you pass in, plus the model files you download from Hugging Face - Not for: paid products, so check the license terms first ## On Hugging Face, 13 November 2026 - Library: transformers - Downloads in the last 30 days: 127,257 - Likes: 187 - License: cc-by-nc-sa-4.0 - Languages: en - Quantized versions: 4 - Fine-tunes: 2 - Tags: transformers, pytorch, t5, text2text-generation, grammar ## Links - Model: https://huggingface.co/vennify/t5-base-grammar-correction - Files: https://huggingface.co/vennify/t5-base-grammar-correction/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download vennify/t5-base-grammar-correction ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [vennify/t5-base-grammar-correction on Hugging Face](https://huggingface.co/vennify/t5-base-grammar-correction) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | t5-base-grammar-correction (this page) | 127,257 | 187 | — | cc-by-nc-sa-4.0 | 2022-01-14 | | [opus-mt-en-zh](https://mindket.com/p/dc2c9808-e987-46c9-8832-291ea8be1508) | 69,966 | 411 | — | apache-2.0 | 2023-08-16 | | [SMaLL-100](https://mindket.com/p/00dfd02c-7aa5-4c1d-ae8d-99e914fa16c2) | 10,902 | 113 | 333M | mit | 2024-07-23 | | [humanizer by jialinyyzz](https://mindket.com/p/10bee27a-389b-4dcd-923e-27f149c67dce) | 391,936 | 2,260 | 12B | apache-2.0 | 2026-10-15 | | [Hy-MT2-1.8B](https://mindket.com/p/68c096b8-1b6b-445d-b2b3-ac77722383d1) | 22,844 | 1,285 | 2B | apache-2.0 | 2026-05-26 | | [Hemmingway-1](https://mindket.com/p/0a2b39f9-1af8-4f66-8bd6-12c8f603f698) | 3,631 | 902 | 26.9B | cc-by-nc-4.0 | 2026-10-29 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2022-01-14 - First published: 2022-03-02 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open vennify/t5-base-grammar-correction on Hugging Face](https://huggingface.co/vennify/t5-base-grammar-correction) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/3558b35a-fc7f-4926-ab71-24d4b6b441bd) - [Its page on Mindket](https://mindket.com/p/3558b35a-fc7f-4926-ab71-24d4b6b441bd)