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)[0]
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.
id | source | text |
---|---|---|
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 (gpt
).text
: The actual text content of the sample.
Here are some example texts that could be included in this dataset:
id | source | text |
---|---|---|
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.