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electra-finetuned

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electra-finetuned / README.md
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metadata
license: apache-2.0
base_model: google/electra-base-generator
tags:
  - generated_from_trainer
datasets:
  - datasets/all_binary_and_xe_ey_fae_counterfactual
metrics:
  - accuracy
model-index:
  - name: electra-base-finetuned-xe_ey_fae
    results:
      - task:
          name: Masked Language Modeling
          type: fill-mask
        dataset:
          name: datasets/all_binary_and_xe_ey_fae_counterfactual
          type: datasets/all_binary_and_xe_ey_fae_counterfactual
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.667333329363415

electra-base-finetuned-xe_ey_fae

This model is a fine-tuned version of google/electra-base-generator on the datasets/all_binary_and_xe_ey_fae_counterfactual dataset. It achieves the following results on the evaluation set:

  • Loss: 1.7211
  • Accuracy: 0.6673

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: 8
  • eval_batch_size: 8
  • seed: 100
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 3.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
2.5359 0.06 500 2.0696 0.6228
2.1807 0.13 1000 1.9677 0.6352
2.1028 0.19 1500 1.9192 0.6415
2.0658 0.26 2000 1.8923 0.6451
2.0426 0.32 2500 1.8699 0.6478
2.0133 0.39 3000 1.8580 0.6490
1.9978 0.45 3500 1.8411 0.6507
1.9862 0.52 4000 1.8297 0.6524
1.9745 0.58 4500 1.8154 0.6545
1.9606 0.64 5000 1.8056 0.6557
1.9486 0.71 5500 1.8033 0.6560
1.9416 0.77 6000 1.7894 0.6581
1.9279 0.84 6500 1.7848 0.6582
1.9196 0.9 7000 1.7786 0.6593
1.9168 0.97 7500 1.7762 0.6592
1.9123 1.03 8000 1.7744 0.6597
1.8942 1.1 8500 1.7625 0.6611
1.9053 1.16 9000 1.7576 0.6623
1.898 1.22 9500 1.7588 0.6620
1.8896 1.29 10000 1.7518 0.6625
1.8796 1.35 10500 1.7557 0.6619
1.8838 1.42 11000 1.7511 0.6628
1.8869 1.48 11500 1.7437 0.6640
1.8756 1.55 12000 1.7425 0.6641
1.8775 1.61 12500 1.7409 0.6641
1.8757 1.68 13000 1.7372 0.6649
1.8616 1.74 13500 1.7387 0.6646
1.8675 1.8 14000 1.7335 0.6648
1.8725 1.87 14500 1.7288 0.6660
1.8678 1.93 15000 1.7305 0.6659
1.8611 2.0 15500 1.7256 0.6666
1.853 2.06 16000 1.7286 0.6661
1.8487 2.13 16500 1.7285 0.6659
1.8543 2.19 17000 1.7229 0.6668
1.8519 2.26 17500 1.7240 0.6670
1.851 2.32 18000 1.7275 0.6662
1.8547 2.38 18500 1.7197 0.6673
1.8476 2.45 19000 1.7164 0.6675
1.8444 2.51 19500 1.7214 0.6676
1.8544 2.58 20000 1.7217 0.6668
1.8491 2.64 20500 1.7175 0.6678

Framework versions

  • Transformers 4.36.2
  • Pytorch 2.2.0+cu121
  • Datasets 2.17.0
  • Tokenizers 0.15.2