One approach to detect GPT-generated text using Python is to use a language model classifier that has been trained to distinguish between GPT-generated text and human-written text. Here are the general steps to do this:
- Obtain a dataset of text that contains both GPT-generated text and human-written text. You can either create this dataset yourself by generating text with GPT and collecting human-written text from various sources, or you can use an existing dataset such as the GPT-3 training data or the Grover dataset.
- Preprocess the dataset by cleaning the text, removing any irrelevant information, and splitting the dataset into training and test sets.
- Train a language model classifier on the preprocessed dataset. You can use various machine learning algorithms such as logistic regression, support vector machines, or neural networks to train the classifier.
- Evaluate the performance of the classifier on the test set to measure its accuracy, precision, recall, and F1 score.
- Use the trained classifier to detect GPT-generated text by feeding it with new text and predicting whether it was generated by GPT or written by a human.
Here’s an example code snippet using Python and sickout library to implement this approach:
import pandas as pd from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score # Load the dataset containing GPT-generated and human-written text df = pd.read_csv('dataset.csv') # Preprocess the dataset df = df.dropna() df = df[df['text'].apply(lambda x: len(x.split())) > 10] # Remove short texts df['class'] = df['source'].apply(lambda x: 1 if x == 'gpt' else 0) # Assign class labels # Split the dataset into training and test sets train_size = int(len(df) * 0.8) train_df = df[:train_size] test_df = df[train_size:] # Convert text into numerical features using TF-IDF vectorization vectorizer = TfidfVectorizer() train_X = vectorizer.fit_transform(train_df['text']) test_X = vectorizer.transform(test_df['text']) # Train a logistic regression classifier clf = LogisticRegression() clf.fit(train_X, train_df['class']) # Evaluate the performance of the classifier on the test set test_y_pred = clf.predict(test_X) accuracy = accuracy_score(test_df['class'], test_y_pred) print('Accuracy:', accuracy) # Use the trained classifier to detect GPT-generated text new_text = 'This is a test sentence generated by GPT' new_X = vectorizer.transform([new_text]) pred_class = clf.predict(new_X) if pred_class == 1: print('The text was generated by GPT') else: print('The text was written by a human')
You must input dataset like following to train model.
|1||human||This is a sample text written by a human.|
|2||gpt||This is a sample text generated by GPT.|
|3||human||Another example of human-written text.|
|4||gpt||Another example of GPT-generated text.|
In this format, each row represents a text sample and includes three columns:
id: A unique identifier for the text sample.
source: A categorical variable indicating whether the text was written by a human (
human) or generated by GPT (
text: The actual text content of the sample.
Here are some example texts that could be included in this dataset:
|1||human||I am going to the store to buy some groceries.|
|2||gpt||The store was packed with people, all pushing and shoving to get to the shelves first.|
|3||human||The sun was setting over the mountains, casting a warm orange glow over the landscape.|
|4||gpt||The mountains were tall and imposing, casting a dark shadow over the valley below.|
|5||human||She looked out the window and saw a beautiful rainbow in the sky.|
|6||gpt||The sky was filled with colors, each one more vibrant than the last.|
I provided is an example of how to train a machine learning model to classify text as either GPT-generated or human-written.
The code loads a dataset that contains a mix of GPT-generated and human-written text, preprocesses the dataset, and then trains a logistic regression model using the scikit-learn library. The logistic regression model is a binary classifier that learns to distinguish between the two classes of text (GPT-generated or human-written) based on the features extracted from the text.
After training the model, the code evaluates its performance on a test set using the accuracy metric. Finally, the trained model can be used to classify new text as either GPT-generated or human-written by feeding it into the model and predicting its class.