CAT-LM Training Language Models on Aligned Code And Tests

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Well, look no further because we’ve got the scoop on CAT-LM (Code And Tests for Language Models) training.

First things first: what is CAT-LM? It’s a framework that allows you to easily create and train your own language models using Python code and tests. No more messing around with complicated command line tools or dealing with confusing configuration files! With CAT-LM, everything is right there in front of you, ready for you to customize and tweak as needed.

So how does it work? Well, let’s say you want to train a language model on some text data. First, you write your code (in Python) that preprocesses the data, splits it into training/validation/test sets, and loads it into memory. Then, you define your tests (also in Python) that check whether your model is working correctly or not. Finally, you run CAT-LM to train your model using your code and tests!

Here’s an example of what this might look like:

# preprocess_data.py
import pandas as pd
from sklearn.model_selection import train_test_split

def load_data():
    # read in the data from a CSV file
    df = pd.read_csv('my_dataset.csv') # reads in the data from a CSV file and stores it in a pandas dataframe called 'df'
    
    # split into training/validation/test sets
    X, y = df['text'], df['label'] # separates the features (X) and labels (y) from the dataframe
    X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2) # splits the data into training, validation, and test sets with a 80/20 ratio
    
    return (X_train, y_train), (X_val, y_val) # returns the training and validation sets as tuples for later use in the model

# The script reads in data from a CSV file, splits it into training/validation/test sets, and returns the sets as tuples for later use in the model.

# tests/preprocess_data_tests.py
import unittest
from preprocess_data import load_data

class TestPreprocessing(unittest.TestCase):
    def test_load_data(self):
        # This test checks that the function load_data() returns a tuple with two lists (X, y) and their corresponding labels
        X_train, y_train = load_data()[0] # The function returns a tuple with two lists, so we need to unpack it into two variables
        self.assertIsInstance(X_train, list) # Asserts that X_train is an instance of the list class
        self.assertIsInstance(y_train, list) # Asserts that y_train is an instance of the list class
        
    def test_split_data(self):
        # This test checks that the function split_data() splits the data into training/validation sets with a 80:20 ratio
        X, y = load_data()[0] # The function returns a tuple with two lists, so we need to unpack it into two variables
        X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2) # Splits the data into training and validation sets with a 80:20 ratio
        self.assertAlmostEqual((len(X_train)/len(X))*100, 80) # Asserts that the length of X_train is 80% of the total length of X
        
    def test_load_data_with_errors(self):
        # This test checks that the function load_data() raises an error if the CSV file doesn't exist or is in a different format
        with self.assertRaises(FileNotFoundError): # Asserts that the function raises a FileNotFoundError
            load_data('my_dataset.csv') # Calls the function with a non-existent CSV file
        
    def test_load_data_with_errors2(self):
        # This test checks that the function load_data() raises an error if the CSV file has incorrect formatting (e.g., missing columns)
        with self.assertRaises(ValueError): # Asserts that the function raises a ValueError
            load_data('my_dataset_wrong.csv') # Calls the function with a CSV file with incorrect formatting

And here’s how you would run CAT-LM to train your model:

# This line runs the CAT-LM training script using the python module "catlm"
python -m catlm \
# This line specifies the training data and testing data files to be used
--train preprocess_data.py tests/preprocess_data_tests.py \
# This line specifies the output file name for the trained model
--model my_language_model.h5 \
# This line specifies the number of training epochs to be used
--epochs 10 \
# This line specifies the batch size for training
--batch_size 32

That’s it! With CAT-LM, you can easily train your own language models using Python code and tests. No more messing around with complicated command line tools or dealing with confusing configuration files! Give it a try today and see how easy it is to get started with CAT-LM training.

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