Unexpectedly Low Accuracy on Text Classification with FastText in Python 3.8
I've spent hours debugging this and I've been working on this all day and I've looked through the documentation and I'm still confused about I'm currently working on a text classification project using Facebook's FastText library in Python 3.8... I've been following the official [FastText documentation](https://fasttext.cc/docs/en/crawl.html) to train my model on a custom dataset that contains around 10,000 labeled samples. However, I'm experiencing unexpectedly low accuracy, around 50%, on my validation set, which is quite concerning given that my training set seems to have a clear class distribution. Here's the code snippet I used to train my model: ```python import fasttext # Training the model model = fasttext.train_supervised( input='training_data.txt', lr=0.1, epoch=25, wordNgrams=2, dim=100 ) ``` I preprocessed the dataset by lowercasing all text and removing punctuation. I also ensured that the training data is formatted correctly, with labels prefixed by `__label__`. However, when I evaluate my model using the validation set, I get the following output: ```python result = model.test('validation_data.txt') print(f'Precision: {result.precision:.2f}, Recall: {result.recall:.2f}, Number of examples: {result.nexamples}') ``` This results in: ``` Precision: 0.50, Recall: 0.50, Number of examples: 2000 ``` I've tried adjusting the learning rate and epoch values, but the accuracy remains the same. I also verified that there are no class imbalances, as the labels are evenly distributed. Additionally, I've checked for overfitting by monitoring loss on the training set during training, but the loss seems to decrease appropriately. Could there be any specific configurations or preprocessing steps that I'm missing? Are there best practices for training FastText models that I should be aware of? Any guidance on how to debug this scenario would be greatly appreciated! My development environment is macOS. What am I doing wrong? Hoping someone can shed some light on this. I'm coming from a different tech stack and learning Python.