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GItaf/PELM-JointGPT

sourceHugging Faceupdated 3y agoView on Hugging Face

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Model Card

PELM-JointGPT2

This model is based on PELM framework and initialised from genGPT-2, then fine-tuned on the MBTI dataset. It achieves the following results on the evaluation set:

—Loss: 4.3556
—Cls loss: 1.5778
—Lm loss: 3.9609
—Cls Accuracy: 0.6202
—Cls F1: 0.6126
—Cls Precision: 0.6216
—Cls Recall: 0.6202
—Perplexity: 52.50

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

—learning_rate: 2e-05
—trainbatchsize: 2
—evalbatchsize: 2
—seed: 42
—optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
—lrschedulertype: linear
—num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Cls loss	Lm loss	Cls Accuracy	Cls F1	Cls Precision	Cls Recall	Perplexity
4.2735	1.0	3470	4.3562	1.5844	3.9598	0.5833	0.5708	0.5928	0.5833	52.45
4.0754	2.0	6940	4.3295	1.4806	3.9590	0.6196	0.6113	0.6332	0.6196	52.41
3.985	3.0	10410	4.3556	1.5778	3.9609	0.6202	0.6126	0.6216	0.6202	52.50

Framework versions

—Transformers 4.21.2
—Pytorch 1.12.1
—Datasets 2.4.0
—Tokenizers 0.12.1