Training data
The model serves as a sequence classifier based on RoBERTa base, initially trained with the RoBERTa base training data. Subsequently, it undergoes fine-tuning using the outputs of the 1.5B GPT-2 model.
Training Procedure
Preprocessing
According to the model developers, they constructed a sequence classifier leveraging RoBERTaBASE (125 million parameters) and fine-tuned it to differentiate between outputs from the 1.5B GPT-2 model and WebText, the dataset utilized for training the GPT-2 model. To ensure the detector model's robustness in accurately classifying generated texts across various sampling methods, they conducted an in-depth analysis of the model's transfer performance. Further details on the training procedure are available in the associated paper. Testing Data, Factors, and Metrics
Evaluation details extracted from the associated paper are as follows:
The model's primary purpose is to detect text generated by GPT-2 models. To assess its performance, the model developers test it on text datasets, measuring accuracy by evaluating:
510-token test examples, comprising 5,000 samples from the WebText dataset and 5,000 samples generated by a GPT-2 model. These examples were not utilized during the training phase.