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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
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: 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
