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gpt2_sm_cv_summarized_4

Text Classification
Transformers
PyTorch
Safetensors
gpt2
Generated from Trainer
text-generation-inference
Inference Endpoints
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gpt2_sm_cv_summarized_4 / README.md
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metadata
license: mit
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: gpt2_sm_cv_summarized_4
    results: []

gpt2_sm_cv_summarized_4

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

  • Loss: 2.4906
  • Accuracy: 0.743
  • Precision: 0.3038
  • Recall: 0.2462
  • F1: 0.2720
  • D-index: 1.4383

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: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 8000
  • num_epochs: 20
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1 D-index
No log 1.0 250 1.0827 0.673 0.2155 0.2564 0.2342 1.3419
2.5863 2.0 500 0.6038 0.761 0.2381 0.1026 0.1434 1.4124
2.5863 3.0 750 0.5238 0.794 0.3333 0.0564 0.0965 1.4421
0.5205 4.0 1000 0.5206 0.798 0.3158 0.0308 0.0561 1.4384
0.5205 5.0 1250 0.5068 0.802 0.4286 0.0462 0.0833 1.4495
0.4619 6.0 1500 0.5153 0.795 0.4074 0.1128 0.1767 1.4637
0.4619 7.0 1750 0.5246 0.795 0.3611 0.0667 0.1126 1.4472
0.3988 8.0 2000 0.6671 0.797 0.3333 0.0410 0.0731 1.4407
0.3988 9.0 2250 0.6091 0.763 0.35 0.2513 0.2925 1.4680
0.3128 10.0 2500 0.7342 0.759 0.3284 0.2256 0.2675 1.4535
0.3128 11.0 2750 0.8236 0.741 0.3049 0.2564 0.2786 1.4391
0.2198 12.0 3000 0.9349 0.742 0.3297 0.3128 0.3211 1.4601
0.2198 13.0 3250 1.1979 0.764 0.3481 0.2410 0.2848 1.4658
0.1522 14.0 3500 1.3995 0.758 0.3464 0.2718 0.3046 1.4682
0.1522 15.0 3750 2.3304 0.779 0.3143 0.1128 0.1660 1.4414
0.1137 16.0 4000 2.0930 0.762 0.2991 0.1641 0.2119 1.4359
0.1137 17.0 4250 2.6869 0.787 0.3714 0.1333 0.1962 1.4598
0.0904 18.0 4500 2.2347 0.678 0.2818 0.4205 0.3374 1.4071
0.0904 19.0 4750 2.4580 0.752 0.3221 0.2462 0.2791 1.4509
0.0737 20.0 5000 2.4906 0.743 0.3038 0.2462 0.2720 1.4383

Framework versions

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