metadata
base_model: lvwerra/gpt2-imdb
tags:
- generated_from_trainer
model-index:
- name: gpt-imdb-alpha_0.3-beta_0.1
results: []
gpt-imdb-alpha_0.3-beta_0.1
This model is a fine-tuned version of lvwerra/gpt2-imdb on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 25.4567
- Rewards/chosen: -0.2859
- Rewards/rejected: -1.2893
- Rewards/accuracies: 0.8458
- Rewards/margins: 1.0034
- Logps/rejected: -276.5780
- Logps/chosen: -238.1245
- Logits/rejected: -31.6823
- Logits/chosen: -32.1973
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: 1e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 150
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.3872 | 0.21 | 500 | 0.9032 | -0.0063 | -0.4921 | 0.7833 | 0.4858 | -268.6066 | -235.3286 | -32.2910 | -32.9554 |
| 0.937 | 0.42 | 1000 | 0.5782 | 0.3739 | -0.2273 | 0.7667 | 0.6012 | -265.9586 | -231.5264 | -33.2571 | -33.9060 |
| 1.6799 | 0.63 | 1500 | 3.1537 | 0.2527 | -0.4167 | 0.7729 | 0.6694 | -267.8524 | -232.7385 | -33.1089 | -33.5974 |
| 0.8141 | 0.83 | 2000 | 1.8978 | 0.1800 | -0.6646 | 0.7917 | 0.8446 | -270.3312 | -233.4657 | -32.3310 | -32.9275 |
| 0.4758 | 1.04 | 2500 | 7.5225 | 0.0635 | -0.8693 | 0.8188 | 0.9329 | -272.3785 | -234.6298 | -32.0571 | -32.5700 |
| 0.5184 | 1.25 | 3000 | 2.2710 | 0.3736 | -0.5136 | 0.8021 | 0.8872 | -268.8213 | -231.5289 | -33.9791 | -34.4883 |
| 0.3571 | 1.46 | 3500 | 12.0724 | 0.0389 | -0.9119 | 0.8125 | 0.9507 | -272.8040 | -234.8766 | -32.0986 | -32.6149 |
| 1.8478 | 1.67 | 4000 | 14.8072 | 0.0021 | -0.9754 | 0.8229 | 0.9775 | -273.4396 | -235.2442 | -32.4363 | -32.9745 |
| 0.6874 | 1.88 | 4500 | 5.9952 | 0.0487 | -0.9284 | 0.8167 | 0.9771 | -272.9694 | -234.7781 | -32.9101 | -33.4694 |
| 0.2233 | 2.08 | 5000 | 11.0797 | -0.2853 | -1.2611 | 0.8479 | 0.9758 | -276.2962 | -238.1182 | -31.8450 | -32.3602 |
| 0.1784 | 2.29 | 5500 | 7.9899 | -0.1567 | -1.1325 | 0.8375 | 0.9757 | -275.0099 | -236.8327 | -32.0292 | -32.5741 |
| 0.2919 | 2.5 | 6000 | 29.0523 | -0.3295 | -1.3283 | 0.8500 | 0.9988 | -276.9686 | -238.5604 | -31.4315 | -31.9371 |
| 2.011 | 2.71 | 6500 | 28.3221 | -0.2974 | -1.3018 | 0.8458 | 1.0044 | -276.7031 | -238.2393 | -31.6565 | -32.1763 |
| 1.7899 | 2.92 | 7000 | 25.4567 | -0.2859 | -1.2893 | 0.8458 | 1.0034 | -276.5780 | -238.1245 | -31.6823 | -32.1973 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1
- Datasets 2.15.0
- Tokenizers 0.15.0