| # Mindspace AI Model - Prediction & Data Preparation | |
| This guide explains how to generate training and test data, and how to use the prediction script. | |
| ## 1. Generating Train and Test Data | |
| - Place your raw dataset (e.g., `sample.xlsx`) in the `data/` directory. | |
| - Run the following script to process the data and generate `train.csv` and `test.csv`: | |
| ```bash | |
| python src/dataset.py | |
| ``` | |
| This will: | |
| - Read the Excel file. | |
| - Prepare text features and labels. | |
| - Split the data into training and test sets. | |
| - Save `train.csv` and `test.csv` in the `data/` folder. | |
| ## 2. Running Predictions | |
| - Make sure your trained model is available in the `models/distilbert` directory. | |
| - Run the prediction script: | |
| ```bash | |
| python src/predict.py | |
| ``` | |
| This will: | |
| - Load the trained model. | |
| - Predict the label for a sample input. | |
| - Print the prediction result and available labels. | |
| --- | |
| For more details, check the respective Python scripts in the `src/` folder. |