gpt2_small / README.md
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metadata
license: mit
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: gpt2_small
    results: []

gpt2_small

This model is a fine-tuned version of gpt2 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 4.0095
  • Accuracy: 0.8
  • Precision: 0.25
  • Recall: 0.0882
  • F1: 0.1304
  • D-index: 1.4492

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: 5e-05
  • train_batch_size: 4
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 1600
  • num_epochs: 20
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1 D-index
No log 1.0 200 1.4996 0.79 0.1667 0.0588 0.0870 1.4242
No log 2.0 400 0.6186 0.79 0.2143 0.0882 0.125 1.4354
2.445 3.0 600 0.8246 0.825 0.0 0.0 0.0 1.4500
2.445 4.0 800 0.5725 0.81 0.0 0.0 0.0 1.4293
0.6727 5.0 1000 1.1303 0.82 0.25 0.0294 0.0526 1.4543
0.6727 6.0 1200 1.3270 0.82 0.3333 0.0588 0.1 1.4655
0.6727 7.0 1400 2.4838 0.82 0.0 0.0 0.0 1.4431
0.2813 8.0 1600 2.2778 0.79 0.2143 0.0882 0.125 1.4354
0.2813 9.0 1800 2.8120 0.82 0.25 0.0294 0.0526 1.4543
0.1174 10.0 2000 2.6462 0.795 0.1818 0.0588 0.0889 1.4312
0.1174 11.0 2200 3.1627 0.795 0.2308 0.0882 0.1277 1.4423
0.1174 12.0 2400 3.3766 0.795 0.2308 0.0882 0.1277 1.4423
0.0319 13.0 2600 3.6674 0.8 0.2857 0.1176 0.1667 1.4603
0.0319 14.0 2800 3.4900 0.78 0.1875 0.0882 0.12 1.4216
0.0136 15.0 3000 3.7351 0.795 0.1818 0.0588 0.0889 1.4312
0.0136 16.0 3200 3.8282 0.8 0.25 0.0882 0.1304 1.4492
0.0136 17.0 3400 3.9465 0.8 0.25 0.0882 0.1304 1.4492
0.0002 18.0 3600 4.0329 0.8 0.25 0.0882 0.1304 1.4492
0.0002 19.0 3800 3.9581 0.795 0.2308 0.0882 0.1277 1.4423
0.0015 20.0 4000 4.0095 0.8 0.25 0.0882 0.1304 1.4492

Framework versions

  • Transformers 4.28.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.12.0
  • Tokenizers 0.13.3