This repository contains code for a text classifier that uses a combination of a Long Short-Term Memory (LSTM) layer and a GPT-2 XL model. The classifier was trained using the Autokeras library and TensorFlow, and is capable of making predictions on new input text. Additionally, the model can be fine-tuned using reinforcement learning to improve its accuracy.
The model was trained on a text file that contains input text and corresponding labels. The text file is preprocessed using the open function to read in the file and the readlines method to split the text into lines. Then, the text data is cleaned and preprocessed by removing any whitespace or newline characters. Finally, the data is split into training and testing data using the train_test_split function.
The model architecture consists of an input layer, an LSTM layer, a GPT-2 XL model, a concatenation layer, and an output layer. The input layer takes in the text data, while the LSTM layer processes the text data and extracts relevant features. The GPT-2 XL model is used to generate additional text data based on the input text. The concatenation layer combines the output of the LSTM layer and the GPT-2 XL model, and the output layer produces a binary classification.
The model is trained using the TextClassifier function from the Autokeras library. The trained model is then saved to a file. The processed text data and labels are also saved to a file. The saved model can be loaded from the file if it already exists. The model can be queried by calling the predict function with a text input. The function generates additional text using the GPT-2 XL model and then uses the concatenated output to make a prediction.
The model can be fine-tuned using reinforcement learning to improve its accuracy. The for loop in the code contains the reinforcement learning algorithm. The user inputs a text sample and a reward value between 0 and 1. The algorithm then generates additional text using the GPT-2 XL model, uses the concatenated output to make a prediction, and adjusts the model's weights based on the reward value.
This text classifier is an effective and flexible way to classify input text. By combining an LSTM layer and a GPT-2 XL model, the classifier can learn to classify a wide range of text inputs. Additionally, the model can be fine-tuned using reinforcement learning to improve its accuracy.
This code assumes that you have already prepared and tokenized your text data. If you have not done so, it is recommended that you clean and tokenize your data prior to using this code. Properly preprocessed text data can help improve the accuracy and effectiveness of natural language processing models.
If you are not familiar with text processing or need guidance on how to preprocess your data, I have created a separate repository on my GitHub account that focuses specifically on text processing. You can refer to that repository to learn more about how to clean and tokenize your text data for use with machine learning models. Once you have preprocessed your text data, you can use this code to train and evaluate a GPT-2 and LSTM mashup model for your natural language processing needs.