文件列表:

demo/
templates/
AG_News_Text_Classification.ipynb
app.py
model.pkl

# AG News Text Classification This repository contains code for text classification on the AG News dataset using various machine learning and deep learning techniques. The AG News dataset is a collection of news articles categorized into four classes: World, Sports, Business, and Science/Technology.

Result

## Dataset The AG News dataset consists of 120,000 training samples and 7,600 test samples, evenly distributed among the four classes. Each sample is a short news article accompanied by a title. You can download the dataset from [here](https://www.kaggle.com/datasets/amananandrai/ag-news-classification-dataset). ## Dependencies - Python - NumPy - Pandas - Scikit-learn - TensorFlow (for deep learning models) ## Models This repository includes implementations of the following models: - Multinomial Naive Bayes - SGD (Stochastic Gradient Descent) - Convolutional Neural Network (CNN) ## Usage All the necessary steps for data preparation, preprocessing, and model training are contained within the Jupyter Notebook provided. 1. **Data Preparation**: Download the AG News dataset and place it in the `data` directory within the same directory as the notebook. If you're using a different dataset, make sure it follows the same format or preprocess accordingly. 2. **Preprocessing**: Execute the preprocessing cells in the notebook. These cells will tokenize the text, remove stopwords, and convert the labels into numerical format. 3. **Model Training (Optional)**: If you want to train your own models, execute the training cells in the notebook. You can use the provided scripts or your custom implementations to train the models. - Ensure that you have the necessary dependencies installed in your Jupyter environment. 4. **Prediction App**: Use the provided prediction app to classify news articles. Here's how to run the app: - Ensure that you have the necessary dependencies installed. - Run the prediction app using the following command: ``` python app.py ``` - The app will prompt you to input the text of a news article. Enter the text and press enter. - The app will then predict the category of the news article based on the trained model. 5. **Evaluation**: If you want to evaluate the performance of the trained models, execute the evaluation cells in the notebook. This will output accuracy and other evaluation metrics. 6. **Demo**    ## Contributing Contributions are welcome! If you have ideas for improvements or new features, feel free to open an issue or submit a pull request. ## License This project is licensed under the MIT License - see the [LICENSE](https://choosealicense.com/licenses/mit/) file for details.

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