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gpt2_sm_cv_summarized_4

Text Classification
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PyTorch
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gpt2
Generated from Trainer
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+ ---
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+ license: mit
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ - precision
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+ - recall
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+ - f1
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+ model-index:
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+ - name: gpt2_sm_cv_summarized_4
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # gpt2_sm_cv_summarized_4
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+
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+ This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 2.4906
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+ - Accuracy: 0.743
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+ - Precision: 0.3038
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+ - Recall: 0.2462
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+ - F1: 0.2720
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+ - D-index: 1.4383
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 8000
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+ - num_epochs: 20
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | D-index |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
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+ | No log | 1.0 | 250 | 1.0827 | 0.673 | 0.2155 | 0.2564 | 0.2342 | 1.3419 |
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+ | 2.5863 | 2.0 | 500 | 0.6038 | 0.761 | 0.2381 | 0.1026 | 0.1434 | 1.4124 |
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+ | 2.5863 | 3.0 | 750 | 0.5238 | 0.794 | 0.3333 | 0.0564 | 0.0965 | 1.4421 |
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+ | 0.5205 | 4.0 | 1000 | 0.5206 | 0.798 | 0.3158 | 0.0308 | 0.0561 | 1.4384 |
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+ | 0.5205 | 5.0 | 1250 | 0.5068 | 0.802 | 0.4286 | 0.0462 | 0.0833 | 1.4495 |
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+ | 0.4619 | 6.0 | 1500 | 0.5153 | 0.795 | 0.4074 | 0.1128 | 0.1767 | 1.4637 |
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+ | 0.4619 | 7.0 | 1750 | 0.5246 | 0.795 | 0.3611 | 0.0667 | 0.1126 | 1.4472 |
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+ | 0.3988 | 8.0 | 2000 | 0.6671 | 0.797 | 0.3333 | 0.0410 | 0.0731 | 1.4407 |
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+ | 0.3988 | 9.0 | 2250 | 0.6091 | 0.763 | 0.35 | 0.2513 | 0.2925 | 1.4680 |
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+ | 0.3128 | 10.0 | 2500 | 0.7342 | 0.759 | 0.3284 | 0.2256 | 0.2675 | 1.4535 |
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+ | 0.3128 | 11.0 | 2750 | 0.8236 | 0.741 | 0.3049 | 0.2564 | 0.2786 | 1.4391 |
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+ | 0.2198 | 12.0 | 3000 | 0.9349 | 0.742 | 0.3297 | 0.3128 | 0.3211 | 1.4601 |
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+ | 0.2198 | 13.0 | 3250 | 1.1979 | 0.764 | 0.3481 | 0.2410 | 0.2848 | 1.4658 |
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+ | 0.1522 | 14.0 | 3500 | 1.3995 | 0.758 | 0.3464 | 0.2718 | 0.3046 | 1.4682 |
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+ | 0.1522 | 15.0 | 3750 | 2.3304 | 0.779 | 0.3143 | 0.1128 | 0.1660 | 1.4414 |
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+ | 0.1137 | 16.0 | 4000 | 2.0930 | 0.762 | 0.2991 | 0.1641 | 0.2119 | 1.4359 |
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+ | 0.1137 | 17.0 | 4250 | 2.6869 | 0.787 | 0.3714 | 0.1333 | 0.1962 | 1.4598 |
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+ | 0.0904 | 18.0 | 4500 | 2.2347 | 0.678 | 0.2818 | 0.4205 | 0.3374 | 1.4071 |
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+ | 0.0904 | 19.0 | 4750 | 2.4580 | 0.752 | 0.3221 | 0.2462 | 0.2791 | 1.4509 |
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+ | 0.0737 | 20.0 | 5000 | 2.4906 | 0.743 | 0.3038 | 0.2462 | 0.2720 | 1.4383 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.28.0
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+ - Pytorch 2.0.1+cu118
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+ - Datasets 2.12.0
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+ - Tokenizers 0.13.3