{"id":25441936,"url":"https://github.com/mirzaazwad/tymbert","last_synced_at":"2026-04-09T02:31:20.776Z","repository":{"id":277912545,"uuid":"933895578","full_name":"mirzaazwad/TYMBert","owner":"mirzaazwad","description":"TYMBert is our submission for NCIM 2025, a spam classifier that makes use of knowledge distillation to compress the model while preserving accuracy","archived":false,"fork":false,"pushed_at":"2025-02-16T23:35:33.000Z","size":3896,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-05-15T19:09:52.844Z","etag":null,"topics":["bert","huggingface-transformers","knowledge-distillation","machine-learning","matplotlib","numpy","pandas","python3","scikit-learn","tiny-bert","torch"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mirzaazwad.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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":"2025-02-16T23:15:15.000Z","updated_at":"2025-02-16T23:35:36.000Z","dependencies_parsed_at":"2025-02-17T00:32:31.594Z","dependency_job_id":null,"html_url":"https://github.com/mirzaazwad/TYMBert","commit_stats":null,"previous_names":["mirzaazwad/tymbert"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mirzaazwad%2FTYMBert","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mirzaazwad%2FTYMBert/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mirzaazwad%2FTYMBert/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mirzaazwad%2FTYMBert/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mirzaazwad","download_url":"https://codeload.github.com/mirzaazwad/TYMBert/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254404345,"owners_count":22065641,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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":["bert","huggingface-transformers","knowledge-distillation","machine-learning","matplotlib","numpy","pandas","python3","scikit-learn","tiny-bert","torch"],"created_at":"2025-02-17T13:16:04.415Z","updated_at":"2026-04-09T02:31:20.750Z","avatar_url":"https://github.com/mirzaazwad.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Tymbert\n\n## Table of Contents\n\n- [Introduction](#introduction)\n- [Prerequisites](#prerequisites)\n- [Installation Steps](#installation-steps)\n- [Updating the Environment](#updating-the-environment)\n- [Deactivating and Removing the Environment](#deactivating-and-removing-the-environment)\n- [Jupyter Use](#jupyter-use)\n- [Datasets](#datasets)\n- [Additional References](#additional-references)\n- [License](#license)\n\n## Introduction\n\nTYMBert is our submission for NCIM 2025, a spam classifier that makes use of knowledge distillation to compress the model while preserving accuracy\n\nThis repository provides a Conda environment configuration file (`environment.yml`) for setting up the `tymbert` environment. Follow the steps below to install and configure it correctly on your system.\n\n## Prerequisites\n\n- Install [Miniconda](https://docs.conda.io/en/latest/miniconda.html) or [Anaconda](https://www.anaconda.com/products/distribution)\n- Ensure Conda is added to your system's PATH\n\n## Installation Steps\n\n1. **Clone the Repository**\n\n   ```bash\n   git clone https://github.com/mirzaazwad/TYMBert.git\n   cd TYMBert\n   ```\n\n2. **Create the Conda Environment**\n   Run the following command to create the `tymbert` environment from the `environment.yml` file:\n\n   ```bash\n   conda env create -f environment.yml\n   ```\n\n3. **Update the Environment Prefix**\n   The `environment.yml` file may contain an absolute path under the `prefix` field, which may not match your system's Conda installation directory. To fix this:\n\n   - Open the `environment.yml` file in a text editor\n   - Locate the `prefix:` field at the bottom of the file (if present)\n   - Change it to your own Conda environment path, which can be found using:\n     ```bash\n     conda info --envs\n     ```\n   - Alternatively, create the environment without using the prefix by running:\n     ```bash\n     conda env create --name tymbert --file environment.yml\n     ```\n\n4. **Activate the Environment**\n\n   ```bash\n   conda activate tymbert\n   ```\n\n5. **Verify Installation**\n   Check that the necessary dependencies are installed:\n   ```bash\n   conda list\n   ```\n\n## Updating the Environment\n\nIf you make changes to `environment.yml` and need to update the existing environment:\n\n```bash\nconda env update --name tymbert --file environment.yml --prune\n```\n\n## Deactivating and Removing the Environment\n\nTo deactivate the environment:\n\n```bash\nconda deactivate\n```\n\nTo remove the environment completely:\n\n```bash\nconda env remove --name tymbert\n```\n\n## Jupyter Use\n\nAfter this environment is setup, use this environment as your kernel and you can use it via Jupyter Notebook or VSCode with the Jupyter extension.\n\n## Datasets\n\n| Dataset Name                | Description                                                              | Link                                                                                              |\n| --------------------------- | ------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------- |\n| SPStudy                     | A dataset for spam research, containing various studies and data points. | [GitHub - SPStudy](https://github.com/smspamresearch/spstudy/tree/main)                           |\n| SMS Spam Collection Dataset | A dataset containing SMS messages labeled as spam or ham.                | [Kaggle - SMS Spam Collection](https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset) |\n\n## Additional References\n\nQuantization Logic and Code was used with the help of [GitHub - BERT-Quantization](https://github.com/srimoyee1212/BERT-Quantization/tree/main) by srimoyee1212\n\n## License\n\nThis project is licensed under the MIT License. See the `LICENSE` file for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmirzaazwad%2Ftymbert","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmirzaazwad%2Ftymbert","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmirzaazwad%2Ftymbert/lists"}