I am currently publishing venture capital news and debt deals every week on my company's news page.
I am not a robot always looking the articles to post and it is not even my job. I just build the machine let it able to do. Therefore, this is how the machine was built.
In this blog post, I’ll walk you through the process of creating a text classifier that determines whether an article is about venture capital or not. We’ll be using PyTorch, a popular deep learning framework, to build and train our model. This project covers data preprocessing, model training, evaluation, and final predictions. Let's dive in!
Project Setup
First, let's set up our project directory structure. We'll organize our files to keep everything neat and manageable. Here's what our project directory looks like:
CNN-Venture-Capital/ │ ├── data/ │ └── database.csv │ ├── tests/ │ ├── .gitignore ├── poetry.lock ├── pyproject.toml └── README.md The data directory contains our dataset. We're using Poetry to manage our dependencies. Make sure to add data/ to your .gitignore to prevent data files from being tracked by Git.
Step 1: Load and Preprocess the Data
We start by loading our dataset and preprocessing it. Our dataset contains articles categorized into 'capital' (venture capital) and 'debt'. We combine relevant text columns and encode the labels.
Loading Data: We load the CSV file containing our data. This includes the title, description, summary, and category of each article.
Preprocessing: We combine the title, description, and summary into a single text field for each article. Then, we encode the category labels into numerical values.
Splitting Data: We split our dataset into training and testing sets. This helps us train our model on one part of the data and evaluate it on another part.
Step 2: Tokenize and Prepare the Data
Tokenization is the process of converting text into individual words or tokens. We use a tokenizer to break down our text into tokens. Next, we build a vocabulary from these tokens, which is a mapping of each unique token to a numerical index.
Creating Pipelines: We define pipelines to convert raw text into numerical form and to convert labels into numerical values. We also create a function to pad our sequences to ensure they have a minimum length.
Step 3: Create the Dataset and DataLoader
PyTorch provides convenient tools for handling data. We create a custom dataset class to manage our text and labels. We also define a DataLoader to handle batching and shuffling of our data, making the training process more efficient.
Step 4: Define the Model
We define our Convolutional Neural Network (CNN) model. CNNs are typically used in image processing, but they can also be effective for text classification.
Model Architecture: Our model consists of an embedding layer, which converts our tokens into dense vectors, and several convolutional layers that capture local patterns in the text. Finally, we have a fully connected layer that outputs the class probabilities.
Step 5: Train the Model
Training the model involves several steps:
Defining Loss and Optimizer: We use the Cross-Entropy Loss, which is suitable for classification problems. We choose the Adam optimizer, which adjusts the learning rate dynamically during training.
Training Loop: We iterate over our training data in batches, passing each batch through the model, calculating the loss, and updating the model parameters using backpropagation.
Evaluation: After training, we evaluate our model on the test set to see how well it performs on unseen data. We calculate metrics like accuracy and loss to gauge the model's performance.
Step 6: Evaluate New Strings
Once our model is trained, we can use it to classify new articles. We preprocess the new text in the same way as our training data, pass it through the model, and get the prediction.
Making Predictions: We create a function to evaluate individual strings and determine if they are about venture capital. This function preprocesses the text, feeds it into the model, and outputs the predicted category.
EOD
Building a text classifier involves several steps, from data preprocessing to model training and evaluation. Using PyTorch, we created a CNN model to classify articles as either about venture capital or not. This project highlights the importance of data preparation and the effectiveness of convolutional networks in text classification tasks.
With our trained model, we can now classify new articles and potentially automate the process of categorizing large volumes of text. This can be incredibly useful for businesses and organizations that need to manage and organize content efficiently.
Feel free to explore and tweak the model further. Happy coding!