{"id":27121088,"url":"https://github.com/yassin522/english-grammar-error-correction","last_synced_at":"2026-04-26T12:31:51.573Z","repository":{"id":220465664,"uuid":"751712031","full_name":"Yassin522/English-Grammar-Error-Correction","owner":"Yassin522","description":"The project focuses on leveraging state-of-the-art natural language processing techniques, including the T5 model and a custom Encoder-Decoder architecture, to automatically detect and correct grammatical errors in written English text.","archived":false,"fork":false,"pushed_at":"2024-02-02T07:48:48.000Z","size":2541,"stargazers_count":3,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-07T10:51:41.251Z","etag":null,"topics":["encoder-decoder","grammar","grammar-error-correction","jupyter-notebook","python","t5-model"],"latest_commit_sha":null,"homepage":"https://english-grammar-error-correction.vercel.app","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Yassin522.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-02-02T06:48:07.000Z","updated_at":"2024-09-19T06:28:36.000Z","dependencies_parsed_at":null,"dependency_job_id":"71cdef04-edbc-4293-b67c-e51c537c8608","html_url":"https://github.com/Yassin522/English-Grammar-Error-Correction","commit_stats":null,"previous_names":["yassin522/english-grammar-error-correction"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Yassin522/English-Grammar-Error-Correction","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Yassin522%2FEnglish-Grammar-Error-Correction","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Yassin522%2FEnglish-Grammar-Error-Correction/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Yassin522%2FEnglish-Grammar-Error-Correction/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Yassin522%2FEnglish-Grammar-Error-Correction/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Yassin522","download_url":"https://codeload.github.com/Yassin522/English-Grammar-Error-Correction/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Yassin522%2FEnglish-Grammar-Error-Correction/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32297893,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-26T09:34:17.070Z","status":"ssl_error","status_checked_at":"2026-04-26T09:34:00.993Z","response_time":129,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["encoder-decoder","grammar","grammar-error-correction","jupyter-notebook","python","t5-model"],"created_at":"2025-04-07T10:51:35.897Z","updated_at":"2026-04-26T12:31:51.566Z","avatar_url":"https://github.com/Yassin522.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# English Grammar Error Correction Project\n\n## Overview\nThis project focuses on the development of an English grammar error correction system using the T5 model and implementing an Encoder-Decoder architecture from scratch. The goal is to create a robust and efficient tool that can automatically detect and correct grammatical errors in written English text.\n\n## Features\n- T5 Model Integration: The project leverages the Transformer-based T5 (Text-to-Text Transfer Transformer) model, known for its ability to handle a wide range of natural language processing tasks. The T5 model is fine-tuned specifically for English grammar error correction.\n\n- Encoder-Decoder Architecture: In addition to the pre-trained T5 model, we have implemented an Encoder-Decoder architecture from scratch. This architecture enhances the model's understanding of contextual information and aids in generating accurate corrections for grammatical errors.\n\n- User-Friendly Interface: The system is designed with a user-friendly interface, allowing users to input text and receive corrected output seamlessly. The interface provides a simple yet effective way to interact with the correction system.\n\n## Model Training\n- Fine-Tuning T5 Model:\nThe T5 model is fine-tuned on a dataset containing annotated examples of grammatical errors. This ensures that the model is tailored to the specific task of English grammar correction.\n\n- Encoder-Decoder Training:\nThe Encoder-Decoder architecture is trained on a parallel corpus of correct and incorrect sentences. The training process involves optimizing the model's parameters to minimize the difference between the predicted corrected sentence and the ground truth.\n\n- Embedding Model:\nWe utilized the wiki-news-300d-1M.vec pre-trained embedding model to enhance the representation of words in the input text.\n```\nhttps://www.kaggle.com/datasets/pablomarino/wikinews300d1msubwordvec\n```\n\n- Encoder-Decoder Training Results:\n```\n311/311 [==============================] - 8213s 26s/step - loss: 0.1656 - f_beta_score: 0.6820 - val_loss: 0.1498 - val_f_beta_score: 0.6787\n```\n- T5 Training Results:\n\n| step | Training Loss | Validation Loss | Gleu    |\n|------|---------------|-----------------|---------|\n| 250  | No log        | 0.732383        | 10.8529 |\n| 500  | 0.841700      | 0.699691        | 12.1853 |\n| 1000 | 0.742300      | 0.676036        | 13.4657 |\n| 1250 | 0.742300      | 0.670769        | 13.6931 |\n| 1500 | 0.729500      | 0.668988        | 13.7441 |\n\n\n## Evaluation\nThe performance of the grammar correction system is evaluated using metrics such as precision, recall, and F1 score, Gleu. \n\n## Dataset\nThe training dataset can be found here:\n```\nhttps://www.kaggle.com/datasets/studentramya/lang-8?select=lang8.train.auto.bea19.m2\n```\nIt includes annotated examples of grammatical errors for optimizing the model's performance in English grammar correction\n\n![Screenshot (2212)](https://github.com/Yassin522/English-Grammar-Error-Correction/assets/88105077/a0b498e5-5f8e-4fb3-b53d-56f5f15543db)\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyassin522%2Fenglish-grammar-error-correction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fyassin522%2Fenglish-grammar-error-correction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyassin522%2Fenglish-grammar-error-correction/lists"}