GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences. However, it acheives very good results on Text Classification tasks.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-5
train_batch_size: 16
eval_batch_size: 16
seed: 123
optimizer: epsilon=1e-08
num_epochs: 3

Training results

Epoch	Training Loss	Training Accuracy	Validation Loss	Validation Accuracy
1	0.35743	0.83391	0.28496	0.87549
2	0.26334	0.88814	0.26964	0.89030
3	0.21890	0.91252	0.27717	0.89416

PavanNeerudu/gpt2-finetuned-qqp

gpt2-finetuned-qqp

Model Details

Training procedure

Training hyperparameters

Training results

PavanNeerudu/
gpt2-finetuned-qqp