{"id":28311981,"url":"https://github.com/georgiosioannoucoder/mindscanner","last_synced_at":"2026-04-10T22:38:15.525Z","repository":{"id":291610374,"uuid":"978214934","full_name":"GeorgiosIoannouCoder/mindscanner","owner":"GeorgiosIoannouCoder","description":"Deep learning models and fine-tuned transformers for detecting mental illness from user content on Reddit. 🧠","archived":false,"fork":false,"pushed_at":"2025-05-13T10:34:59.000Z","size":9241,"stargazers_count":1,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-06-01T01:14:41.002Z","etag":null,"topics":["artificial-intelligence","bidirectional-gru","bidirectional-lstm","bigru","bilstm","bilstm-model","classification","cnn","cnn-model","data-science","deep-learning","machine-learning","mental-illness","mistral-7b","pytorch","reddit","sentiment-analysis","tensorflow2","transformers"],"latest_commit_sha":null,"homepage":"","language":"Python","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/GeorgiosIoannouCoder.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,"zenodo":null}},"created_at":"2025-05-05T16:37:47.000Z","updated_at":"2025-05-29T17:46:10.000Z","dependencies_parsed_at":"2025-05-06T07:47:14.955Z","dependency_job_id":null,"html_url":"https://github.com/GeorgiosIoannouCoder/mindscanner","commit_stats":null,"previous_names":["georgiosioannoucoder/mindscanner"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/GeorgiosIoannouCoder/mindscanner","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GeorgiosIoannouCoder%2Fmindscanner","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GeorgiosIoannouCoder%2Fmindscanner/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GeorgiosIoannouCoder%2Fmindscanner/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GeorgiosIoannouCoder%2Fmindscanner/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/GeorgiosIoannouCoder","download_url":"https://codeload.github.com/GeorgiosIoannouCoder/mindscanner/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GeorgiosIoannouCoder%2Fmindscanner/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":261452886,"owners_count":23160410,"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":["artificial-intelligence","bidirectional-gru","bidirectional-lstm","bigru","bilstm","bilstm-model","classification","cnn","cnn-model","data-science","deep-learning","machine-learning","mental-illness","mistral-7b","pytorch","reddit","sentiment-analysis","tensorflow2","transformers"],"created_at":"2025-05-24T14:18:18.873Z","updated_at":"2026-04-10T22:38:15.515Z","avatar_url":"https://github.com/GeorgiosIoannouCoder.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003ca name=\"readme-top\"\u003e\u003c/a\u003e\n\n[![Contributors][contributors-shield]][contributors-url]\n[![Forks][forks-shield]][forks-url]\n[![Stargazers][stars-shield]][stars-url]\n[![Issues][issues-shield]][issues-url]\n[![MIT License][license-shield]][license-url]\n[![LinkedIn][linkedin-shield]][linkedin-url]\n[![GitHub][github-shield]][github-url]\n\n\u003ch1 align=\"center\"\u003eAnalyzing Mental Health Posts on Social Media: A Deep Learning Approach to Reddit Post Classification\u003c/h1\u003e\n\n\u003cbr /\u003e\n\u003cdiv align=\"center\"\u003e\n    \u003cimg src=\"./logo/logo.png\" alt=\"Logo\" width=\"250\" height=\"250\"\u003e\n\n\u003ch3 align=\"center\"\u003eMINDSCANNER\u003c/h3\u003e\n\n  \u003cp align=\"center\"\u003e\nThis project aims to predict a user’s potential \u003cb\u003emental health condition\u003c/b\u003e based on the sentiment and linguistic cues found in \u003cb\u003eReddit posts\u003c/b\u003e. Social media platforms, particularly Reddit, are widely used for expressing emotions and seeking support, often anonymously. Users frequently share personal experiences that may indicate underlying mental health issues such as \u003cb\u003eautism, anxiety, bipolar disorder, borderline personality disorder, dpression, schizophrenia\u003c/b\u003e.\n\nTo address this, we developed and evaluated \u003cb\u003edeep learning models\u003c/b\u003e, including both models built from scratch and \u003cb\u003efine-tuned transformer-based models\u003c/b\u003e. These models were trained on labeled data from dedicated mental health subreddits and optimized for classifying the specific condition associated with each post. We trained and evaluated our models and fine-tuned transformer-based models on two versions of the original data. The first version where we did the cleaning which can found by running the code \u003ca href=\"https://github.com/GeorgiosIoannouCoder/mindscanner/tree/main/datasets/clean\"\u003ehere\u003c/a\u003e and and second version where we apply the transformation on the clean version of the data which can be found by running the code \u003ca href=\"https://github.com/GeorgiosIoannouCoder/mindscanner/tree/main/datasets/transform\"\u003ehere\u003c/a\u003e.\n\nOur work contributes to the field of public mental health by leveraging user-generated content to identify potential mental health conditions. Beyond academic value, this project has real-world implications for \u003cb\u003eimproving online mental health support\u003c/b\u003e. By accurately classifying Reddit posts according to specific mental health conditions, our models can \u003cb\u003ehelp guide users toward the most appropriate subreddit communities\u003c/b\u003e, where they are more likely to receive relevant support, shared experiences, and information. This can \u003cb\u003eenhance early detection, reduce miscommunication, and improve users’ chances of accessing the right resources or treatment\u003c/b\u003e pathways within these online communities.\n\nIn total, \u003cb\u003efour models\u003c/b\u003e from scratch were developed and \u003cb\u003etwo transformer models\u003c/b\u003e were fine-tuned and compared to identify the most effective architecture for detecting a user’s potential \u003cb\u003emental health condition\u003c/b\u003e based on the sentiment and linguistic cues found in \u003cb\u003eReddit posts\u003c/b\u003e. Our best-performing model, a \u003cb\u003eBERT\u003c/b\u003e fine-tuned model, achieved an accuracy of \u003cb\u003e87.3%\u003c/b\u003e on the test set, demonstrating that fine-tuned models can perform better than models build from scratch with minimal code.\n    \u003cbr /\u003e\n    \u003ca href=\"https://github.com/GeorgiosIoannouCoder/mindscanner\"\u003e\u003cstrong\u003eExplore the docs »\u003c/strong\u003e\u003c/a\u003e\n    \u003cbr /\u003e\n    \u003cbr /\u003e\n    \u003ca href=\"https://github.com/GeorgiosIoannouCoder/mindscanner/issues\"\u003eReport Bug\u003c/a\u003e\n    ·\n    \u003ca href=\"https://github.com/GeorgiosIoannouCoder/mindscanner/issues\"\u003eRequest Feature\u003c/a\u003e\n  \u003c/p\u003e\n\u003c/div\u003e\n\n\u003cdetails\u003e\n  \u003csummary\u003eTable of Contents\u003c/summary\u003e\n  \u003col\u003e\n    \u003cli\u003e\n      \u003ca href=\"#about-the-project\"\u003eAbout The Project\u003c/a\u003e\n      \u003cul\u003e\n        \u003cli\u003e\u003ca href=\"#tasks\"\u003eTasks\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#dataset\"\u003eDataset\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#cleaning\"\u003eCleaning\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#Transformation\"\u003eTransformation\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#models-built-with-version-2-of-the-original-data\"\u003eModels Built With Version 2 of The Original Data\u003c/a\u003e\u003c/li\u003e  \n        \u003cli\u003e\u003ca href=\"#models-built-with-version-2-of-the-original-data\"\u003eModels Built With Version 2 of The Original Data\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#models-performance\"\u003eModels Performance\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#evaluation-metrics-used\"\u003eEvaluation Metrics Used\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#models-weights\"\u003eModels Weights\u003c/a\u003e\u003c/li\u003e\n         \u003cli\u003e\u003ca href=\"#inference-example\"\u003eInference Example\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#built-with\"\u003eBuilt With\u003c/a\u003e\u003c/li\u003e\n      \u003c/ul\u003e\n    \u003c/li\u003e\n    \u003cli\u003e\n      \u003ca href=\"#getting-started-with-georgios-ioannou-code\"\u003eGetting Started With Georgios Ioannou Code\u003c/a\u003e\n      \u003cul\u003e\n        \u003cli\u003e\u003ca href=\"#prerequisites\"\u003ePrerequisites\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#setup\"\u003eSetup\u003c/a\u003e\u003c/li\u003e\n      \u003c/ul\u003e\n    \u003c/li\u003e\n    \u003cli\u003e\n      \u003ca href=\"#getting-started-with-zechen-yang-code\"\u003eGetting Started With Zechen Yang Code\u003c/a\u003e\n      \u003cul\u003e\n        \u003cli\u003e\u003ca href=\"#prerequisites\"\u003ePrerequisites\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#setup\"\u003eSetup\u003c/a\u003e\u003c/li\u003e\n      \u003c/ul\u003e\n    \u003c/li\u003e\n    \u003cli\u003e\u003ca href=\"#usage\"\u003eUsage\u003c/a\u003e\u003c/li\u003e\n    \u003cli\u003e\u003ca href=\"#code-with-plotly-graphs\"\u003eCode With Plotly Graphs\u003c/a\u003e\u003c/li\u003e\n    \u003cli\u003e\u003ca href=\"#paper\"\u003ePaper\u003c/a\u003e\u003c/li\u003e\n    \u003cli\u003e\u003ca href=\"#contributing\"\u003eContributing\u003c/a\u003e\u003c/li\u003e\n    \u003cli\u003e\u003ca href=\"#license\"\u003eLicense\u003c/a\u003e\u003c/li\u003e\n    \u003cli\u003e\u003ca href=\"#contact\"\u003eContact\u003c/a\u003e\u003c/li\u003e\n  \u003c/ol\u003e\n\u003c/details\u003e\n\n## About The Project\n\n  \u003cimg src=\"./logo/logo.png\" alt=\"Logo\" width=\"200\" height=\"200\" style=\"display: block; margin-left: auto; margin-right: auto;\"\u003e\n\n### Tasks\n\n| Tasks |\n| ----- |\n| Create Github Repository |\n| Brainstorm Project |\n| Find Dataset |\n| EDA |\n| Data Preprocessing and Cleaning |\n| Data Modeling: BiLSTM, CNN+BiLSTM, CNN, BiGRU |\n| Fine-Tune: BERT, MISTRAL-7B |\n| Model Evaluation |\n| Write Paper |\n\n\u003cp align=\"right\"\u003e\u003ca href=\"#readme-top\"\u003eBack to top\u003c/a\u003e\u003c/p\u003e\n\n### Dataset\n\n**[`Subreddits Dataset For NLP`](https://github.com/GeorgiosIoannouCoder/mindscanner/blob/main/datasets/original/original_dataset.csv)**\n- 3 Columns:\n  - Title\n  - Text\n  - Subreddit\n- 488,472 rows\n- 7 Classes:\n  - depression: 58496\n  - anxiety: 86243\n  - bipolar: 41493\n  - mentalhealth: 39373\n  - bpd: 38216\n  - schizophrenia: 17506\n  - autism: 7142\n- 2 Versions of Data:\n  - Version 1: Clean\n  - Version 2: Clean + Transform\n\n\u003cp align=\"right\"\u003e\u003ca href=\"#readme-top\"\u003eBack to top\u003c/a\u003e\u003c/p\u003e\n\n### Cleaning\n\n***Please note that in our pre-processing we excluded the class \"mentalhealth\" as it is a more generic class and our models performed better without it.***\n\n***Moreover, we combine the title and text to form our X and use bureddit as our y.***\n\n\u003cimg src=\"./datasets/clean/data_cleaning_diagram.png\" alt=\"Data Cleaning Diagram\" height=\"500\"\u003e\n\n\u003cp align=\"right\"\u003e\u003ca href=\"#readme-top\"\u003eBack to top\u003c/a\u003e\u003c/p\u003e\n\n### Transformation\n\n***Please note that in our pre-processing we excluded the class \"mentalhealth\" as it is a more generic class and our models performed better without it.***\n\n***Moreover, we combine the title and text to form our X and use bureddit as our y.***\n\n**Transformation Steps:**\n- Synonym Replacement\n- For words longer than 3 characters, there's a 10% chance they'll be replaced with synonyms\n- Random Typos\n- For words longer than 3 characters, there's a 10% chance of introducing a typing error\n\n\u003cp align=\"right\"\u003e\u003ca href=\"#readme-top\"\u003eBack to top\u003c/a\u003e\u003c/p\u003e\n\n### Models Built With Version 1 of The Original Data\n\n1. [Bidirectional Long Short-Term Memory (BiLSTM)](https://github.com/GeorgiosIoannouCoder/mindscanner/tree/main/neural_network_models/bilstm_model_1)\n2. [Convolutional Neural Network (CNN)](https://github.com/GeorgiosIoannouCoder/mindscanner/tree/main/neural_network_models/cnn_model_1)\n3. [CNN + BiLSTM](https://github.com/GeorgiosIoannouCoder/mindscanner/tree/main/neural_network_models/cnn_and_bilstm_model_1)\n4. [Bidirectional Gated Recurrent Unit (BiGRU)](https://github.com/GeorgiosIoannouCoder/mindscanner/tree/main/neural_network_models/bigru_model_1)\n\n\u003cp align=\"right\"\u003e\u003ca href=\"#readme-top\"\u003eBack to top\u003c/a\u003e\u003c/p\u003e\n\n### Models Built With Version 2 of The Original Data\n\n1. [Bidirectional Long Short-Term Memory (BiLSTM)](https://github.com/GeorgiosIoannouCoder/mindscanner/tree/main/neural_network_models/bilstm_model_2)\n2. [Convolutional Neural Network (CNN)](https://github.com/GeorgiosIoannouCoder/mindscanner/tree/main/neural_network_models/cnn_model_2)\n3. [CNN + BiLSTM](https://github.com/GeorgiosIoannouCoder/mindscanner/tree/main/neural_network_models/cnn_and_bilstm_model_2)\n4. [Bidirectional Gated Recurrent Unit (BiGRU)](https://github.com/GeorgiosIoannouCoder/mindscanner/tree/main/neural_network_models/bigru_model_2)\n\n\u003cp align=\"right\"\u003e\u003ca href=\"#readme-top\"\u003eBack to top\u003c/a\u003e\u003c/p\u003e\n\n### Models Performance\n\n## Performance Metrics on Version 1 of Data\n\n| Model          | Accuracy (%) | Precision (%) | Recall (%) | F1 (%) |\n|----------------|--------------|---------------|------------|-------|\n| BiLSTM         | 85.3         | 85.3          | 85.3       | 85.3  |\n| CNN + BiLSTM   | 84.7         | 84.7          | 84.7       | 84.7  |\n| CNN            | 84.1         | 84.1          | 84.1       | 84.1  |\n| BiGRU          | 85.2         | 85.2          | 85.2       | 85.2  |\n| **BERT**       | **86.4**     | **86.4**      | **86.4**   | **86.1** |\n\n## Performance Metrics on Version 2 of Data\n\n| Model          | Accuracy (%) | Precision (%) | Recall (%) | F1 (%) |\n|----------------|--------------|---------------|------------|-------|\n| BiLSTM         | 84.5         | 84.5          | 84.5       | 84.5  |\n| CNN + BiLSTM   | 83.8         | 83.8          | 83.8       | 83.8  |\n| CNN            | 83.3         | 83.3          | 83.3       | 83.3  |\n| **BiGRU**      | **84.6**     | **84.6**      | **84.6**   | **84.6** |\n| BERT           | 83           | 81            | 79         | 80    |\n\n## Performance Metrics on Version 2 of Data With Balanced Classes\n\n| Model     | Accuracy (%) | Precision (%) | Recall (%) | F1 (%) |\n|----------|--------------|---------------|------------|-------|\n| **BERT** | **87.3**     | **87.5**      | **87.1**   | **87.3** |\n| Mistral7b | 69.9         | 76.6          | 69.5       | 70.8  |\n\n\n\u003cp align=\"right\"\u003e\u003ca href=\"#readme-top\"\u003eBack to top\u003c/a\u003e\u003c/p\u003e\n\n### Evaluation Metrics Used\n\n1. Classification Report\n     - Precision\n     - Recall\n     - F1-score\n     - Accuracy\n     - Macro average\n     - Weighted average\n2. Confusion Matrix\n3. ROC Curve\n4. Precision-Recall Curve\n\n\u003cp align=\"right\"\u003e\u003ca href=\"#readme-top\"\u003eBack to top\u003c/a\u003e\u003c/p\u003e\n\n### Models Weights\n\nPlease contact me at gi2100@nyu.edu to get the models weights as they were too large to be uploaded in this GitHub repository.\n\n\u003cp align=\"right\"\u003e\u003ca href=\"#readme-top\"\u003eBack to top\u003c/a\u003e\u003c/p\u003e\n\n### Inference Example\n\n\u003cimg src=\"./inference_example/inference_example.png\" alt=\"Inference Example\"\u003e\n\n\u003cp align=\"right\"\u003e\u003ca href=\"#readme-top\"\u003eBack to top\u003c/a\u003e\u003c/p\u003e\n\n### Built With\n\n[![Python][Python]][Python-url]\n[![Pandas][Pandas]][Pandas-url]\n[![Numpy][Numpy]][Numpy-url]\n[![scikitlearn][scikitlearn]][scikitlearn-url]\n[![Nltk][Nltk]][Nltk-url]\n[![Pytorch][Pytorch]][Pytorch-url]\n[![Tensorflow][Tensorflow]][Tensorflow-url]\n[![Matplotlib][Matplotlib]][Matplotlib-url]\n[![Seaborn][Seaborn]][Seaborn-url]\n[![Plotly][Plotly]][Plotly-url]\n[![Tensordock][Tensordock]][Tensordock-url]\n\n\u003cp align=\"right\"\u003e\u003ca href=\"#readme-top\"\u003eBack to top\u003c/a\u003e\u003c/p\u003e\n\n## Getting Started With Georgios Ioannou Code\n\nNOTE: This code was run on a cloud GPU server from [Tensordock](https://tensordock.com) with these specifications:\n- 1x h100-sxm5-80gb\n- 20x vCPUs\n- 64GB RAM\n- 300GB NVME STORAGE\n\n**To get a local copy of MINDSCANNER Georgios Ioannou code up and running locally follow these simple example steps:**\n\n### Prerequisites\n\n**NOTE:** How to check if Python is installed and what is its version\n\n```sh\n  python --version\n```\n\n**NOTE:** How to check if Git is installed and what is its version\n\n```sh\n  git -v\n```\n\n1. Please make sure you have pyenv installed and use Python3 version 3.12.7:\n\n   - You can use pyenv to switch between different Python versions:\n     - Windows: [https://www.youtube.com/watch?v=HTx18uyyHw8](https://github.com/pyenv-win/pyenv-win)\n     - Mac: [https://www.youtube.com/watch?v=31WU0Dhw4sk](https://github.com/pyenv/pyenv)\n     - Linux: [https://www.youtube.com/watch?v=1Zgo8M9yUtM](https://github.com/pyenv/pyenv)\n\n2. Please make sure you have git installed\n\n   - Windows: [https://git-scm.com/download/win](https://git-scm.com/download/win)\n   - Mac: [https://git-scm.com/download/mac](https://git-scm.com/download/mac)\n   - Linux: [https://git-scm.com/download/linux](https://git-scm.com/download/linux)\n\n3. Please make sure you have Anaconda installed\n\n   - Windows: [https://www.anaconda.com/docs/getting-started/anaconda/install#windows-installation](https://www.anaconda.com/docs/getting-started/anaconda/install#windows-installation)\n   - Mac: [https://www.anaconda.com/docs/getting-started/anaconda/install#macos-linux-installation](https://www.anaconda.com/docs/getting-started/anaconda/install#macos-linux-installation)\n   - Linux: [https://www.anaconda.com/docs/getting-started/anaconda/install#linux-installer](https://www.anaconda.com/docs/getting-started/anaconda/install#linux-installer)\n\n### Setup\n\n1. Navigate to the directory where you want to clone/run/save the application:\n\n   ```sh\n   cd your_selected_directory\n   ```\n\n2. Clone this repository:\n\n   ```sh\n   git clone https://github.com/GeorgiosIoannouCoder/mindscanner.git\n   ```\n\n3. Navigate to the mindscanner git repository:\n\n   ```sh\n   cd mindscanner\n   ```\n\n4. Create a new conda environment in the cloned repository folder:\n\n   ```sh\n   conda env create -f environment.yml\n   ```\n\n5. Activate the conda environment:\n\n   ```sh\n   conda activate mindscanner-venv\n   ```\n\n6. To Run The Jupyter Notebook and Code Files on GPU (2 Main Options):\n   1. Use [Kaggle](https://www.kaggle.com) - **Free**\n   2. Rent a cloud GPU server like [Tensordock](https://tensordock.com) and SSH to [Jupyter Notebook Extension for VS Code](https://code.visualstudio.com/docs/datascience/jupyter-notebooks) - **NOT Free**\n\n7. The Jupyter Notebook [here](https://github.com/GeorgiosIoannouCoder/mindscanner/blob/main/code/georgios_ioannou/bilstm_cnnbilstm_cnn_gru.ipynb) can be run step by step as it include the entire code for the follwoing models: BiLSTM, CNN + BiLSTM, CNN, and BiGRU.\n   \n8. In case you just want to clean the data use the code [here](https://github.com/GeorgiosIoannouCoder/mindscanner/blob/main/datasets/clean/clean_data.py) and in case you aslo want to transform it, then use the code [here](https://github.com/GeorgiosIoannouCoder/mindscanner/blob/main/datasets/transform/transform_data.py). Please note that you first have to clean adn then transform as the transformation hasppens on the cleaned data. \n\n\u003cp align=\"right\"\u003e\u003ca href=\"#readme-top\"\u003eBack to top\u003c/a\u003e\u003c/p\u003e\n\n## Getting Started With Zechen Yang Code\n\n- To get a local copy of MINDSCANNER Zechen Yang **BERT** code up and running locally follow these simple example steps [here](https://github.com/GeorgiosIoannouCoder/mindscanner/tree/main/code/zechen_yang/bert).\n\n- To get a local copy of MINDSCANNER Zechen Yang **MISTRAL-7B** code up and running locally follow these simple example steps [here](https://github.com/GeorgiosIoannouCoder/mindscanner/tree/main/code/zechen_yang/mistral7b).\n\n\u003cp align=\"right\"\u003e\u003ca href=\"#readme-top\"\u003eBack to top\u003c/a\u003e\u003c/p\u003e\n\n## Code With Plotly Graphs\n\nThe full code for BiLSTM, CNN + BiLSTM, CNN, and BiGRU with all the Plotly interactive graphs can be found [here](https://nbviewer.org/github/GeorgiosIoannouCoder/mindscanner/blob/main/code/georgios_ioannou/bilstm_cnnbilstm_cnn_gru.ipynb). In case you have issues opening it is due to the fact that there is a lot of output making the .ipynb file size large. That is why it was uploaded with git lfs. The solution is to download the .ipynb [here](https://github.com/GeorgiosIoannouCoder/mindscanner/blob/main/code/georgios_ioannou/bilstm_cnnbilstm_cnn_gru.ipynb) and open it locally.\n\n\u003cp align=\"right\"\u003e\u003ca href=\"#readme-top\"\u003eBack to top\u003c/a\u003e\u003c/p\u003e\n\n## Paper\n\nProject Final Paper is located [here](https://github.com/GeorgiosIoannouCoder/mindscanner/blob/main/paper/Final_Project_Paper_Team_5_gi2100_zy3398.pdf).\n\n\u003cp align=\"right\"\u003e\u003ca href=\"#readme-top\"\u003eBack to top\u003c/a\u003e\u003c/p\u003e\n\n## Contributing\n\nContributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are **greatly appreciated**.\n\nIf you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag \"enhancement\".\nDon't forget to give the project a star! Thanks again!\n\n1. Fork the Project\n2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)\n3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)\n4. Push to the Branch (`git push origin feature/AmazingFeature`)\n5. Open a Pull Request\n\n\u003cp align=\"right\"\u003e\u003ca href=\"#readme-top\"\u003eBack to top\u003c/a\u003e\u003c/p\u003e\n\n## License\n\nDistributed under the MIT License. See [LICENSE](https://github.com/GeorgiosIoannouCoder/mindscanner/blob/master/LICENSE) for more information.\n\nMIT License\n\nCopyright (c) 2025 Georgios Ioannou\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n\n\u003cp align=\"right\"\u003e\u003ca href=\"#readme-top\"\u003eBack to top\u003c/a\u003e\u003c/p\u003e\n\n## Contact\n\nGeorgios Ioannou - [@LinkedIn](https://linkedin.com/in/georgiosioannoucoder)\n\nProject Link: [https://github.com/GeorgiosIoannouCoder/mindscanner](https://github.com/GeorgiosIoannouCoder/mindscanner)\n\n\u003cp align=\"right\"\u003e\u003ca href=\"#readme-top\"\u003eBack to top\u003c/a\u003e\u003c/p\u003e\n\n[contributors-shield]: https://img.shields.io/github/contributors/GeorgiosIoannouCoder/mindscanner.svg?style=for-the-badge\n[contributors-url]: https://github.com/GeorgiosIoannouCoder/mindscanner/graphs/contributors\n\n[forks-shield]: https://img.shields.io/github/forks/GeorgiosIoannouCoder/mindscanner.svg?style=for-the-badge\n[forks-url]: https://github.com/GeorgiosIoannouCoder/mindscanner/network/members\n\n[stars-shield]: https://img.shields.io/github/stars/GeorgiosIoannouCoder/mindscanner.svg?style=for-the-badge\n[stars-url]: https://github.com/GeorgiosIoannouCoder/mindscanner/stargazers\n\n[issues-shield]: https://img.shields.io/github/issues/GeorgiosIoannouCoder/mindscanner.svg?style=for-the-badge\n[issues-url]: https://github.com/GeorgiosIoannouCoder/mindscanner/issues\n\n[license-shield]: https://img.shields.io/github/license/GeorgiosIoannouCoder/mindscanner.svg?style=for-the-badge\n[license-url]: https://github.com/GeorgiosIoannouCoder/mindscanner/blob/master/LICENSE\n\n[linkedin-shield]: https://img.shields.io/badge/-LinkedIn-black.svg?style=for-the-badge\u0026logo=linkedin\u0026colorB=0077B5\n[linkedin-url]: https://linkedin.com/in/georgiosioannoucoder\n\n[github-shield]: https://img.shields.io/badge/-GitHub-black.svg?style=for-the-badge\u0026logo=github\u0026colorB=000\n[github-url]: https://github.com/GeorgiosIoannouCoder/\n\n[Python]: https://img.shields.io/badge/python-FFDE57?style=for-the-badge\u0026logo=python\u0026logoColor=4584B6\n[Python-url]: https://www.python.org/\n\n[Pandas]: https://img.shields.io/badge/pandas-150458?style=for-the-badge\u0026logo=pandas\u0026logoColor=white\n[Pandas-url]: https://pandas.pydata.org/\n\n[Numpy]: https://img.shields.io/badge/numpy-%23013243.svg?style=for-the-badge\u0026logo=numpy\u0026logoColor=white\n[Numpy-url]: https://numpy.org/\n\n[scikitlearn]: https://img.shields.io/badge/scikitlearn-000000?style=for-the-badge\u0026logo=scikitlearn\u0026logoColor=ffa500\n[scikitlearn-url]: https://scikit-learn.org/stable/\n\n[Nltk]: https://img.shields.io/badge/nltk-154f5b?style=for-the-badge\u0026logo=nltk\u0026logoColor=ffa500\n[Nltk-url]: https://www.nltk.org/\n\n[Pytorch]: https://img.shields.io/badge/pytorch-000000?style=for-the-badge\u0026logo=pytorch\u0026logoColor=ee4c2c\n[Pytorch-url]: https://www.pytorch.org/\n\n[Tensorflow]: https://img.shields.io/badge/tensorflow-000000?style=for-the-badge\u0026logo=tensorflow\u0026logoColor=ffa500\n[Tensorflow-url]: https://www.tensorflow.org/\n\n[Matplotlib]: https://img.shields.io/badge/matplotlib-3761a3?style=for-the-badge\u0026logo=matplotlib\u0026logoColor=white\n[Matplotlib-url]: https://matplotlib.org/\n\n[Seaborn]: https://img.shields.io/badge/seaborn-7db0bc?style=for-the-badge\u0026logo=seaborn\u0026logoColor=white\n[Seaborn-url]: https://seaborn.pydata.org/\n\n[Plotly]: https://img.shields.io/badge/plotly-000000?style=for-the-badge\u0026logo=plotly\u0026logoColor=white\n[Plotly-url]: https://plotly.com/\n\n[Tensordock]: https://img.shields.io/badge/tensordock-6cbc64?style=for-the-badge\u0026logo=tensordock\u0026logoColor=000000\n[Tensordock-url]: https://www.tensordock.com/","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgeorgiosioannoucoder%2Fmindscanner","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgeorgiosioannoucoder%2Fmindscanner","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgeorgiosioannoucoder%2Fmindscanner/lists"}