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--- |
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license: apache-2.0 |
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base_model: google/electra-base-generator |
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tags: |
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- generated_from_trainer |
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datasets: |
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- datasets/all_binary_and_xe_ey_fae_counterfactual |
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metrics: |
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- accuracy |
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model-index: |
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- name: electra-base-finetuned-xe_ey_fae |
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results: |
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- task: |
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name: Masked Language Modeling |
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type: fill-mask |
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dataset: |
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name: datasets/all_binary_and_xe_ey_fae_counterfactual |
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type: datasets/all_binary_and_xe_ey_fae_counterfactual |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.667333329363415 |
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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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# electra-base-finetuned-xe_ey_fae |
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This model is a fine-tuned version of [google/electra-base-generator](https://huggingface.co/google/electra-base-generator) on the datasets/all_binary_and_xe_ey_fae_counterfactual dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.7211 |
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- Accuracy: 0.6673 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 100 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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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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- num_epochs: 3.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
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| 2.5359 | 0.06 | 500 | 2.0696 | 0.6228 | |
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| 2.1807 | 0.13 | 1000 | 1.9677 | 0.6352 | |
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| 2.1028 | 0.19 | 1500 | 1.9192 | 0.6415 | |
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| 2.0658 | 0.26 | 2000 | 1.8923 | 0.6451 | |
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| 2.0426 | 0.32 | 2500 | 1.8699 | 0.6478 | |
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| 2.0133 | 0.39 | 3000 | 1.8580 | 0.6490 | |
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| 1.9978 | 0.45 | 3500 | 1.8411 | 0.6507 | |
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| 1.9862 | 0.52 | 4000 | 1.8297 | 0.6524 | |
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| 1.9745 | 0.58 | 4500 | 1.8154 | 0.6545 | |
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| 1.9606 | 0.64 | 5000 | 1.8056 | 0.6557 | |
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| 1.9486 | 0.71 | 5500 | 1.8033 | 0.6560 | |
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| 1.9416 | 0.77 | 6000 | 1.7894 | 0.6581 | |
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| 1.9279 | 0.84 | 6500 | 1.7848 | 0.6582 | |
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| 1.9196 | 0.9 | 7000 | 1.7786 | 0.6593 | |
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| 1.9168 | 0.97 | 7500 | 1.7762 | 0.6592 | |
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| 1.9123 | 1.03 | 8000 | 1.7744 | 0.6597 | |
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| 1.8942 | 1.1 | 8500 | 1.7625 | 0.6611 | |
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| 1.9053 | 1.16 | 9000 | 1.7576 | 0.6623 | |
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| 1.898 | 1.22 | 9500 | 1.7588 | 0.6620 | |
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| 1.8896 | 1.29 | 10000 | 1.7518 | 0.6625 | |
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| 1.8796 | 1.35 | 10500 | 1.7557 | 0.6619 | |
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| 1.8838 | 1.42 | 11000 | 1.7511 | 0.6628 | |
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| 1.8869 | 1.48 | 11500 | 1.7437 | 0.6640 | |
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| 1.8756 | 1.55 | 12000 | 1.7425 | 0.6641 | |
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| 1.8775 | 1.61 | 12500 | 1.7409 | 0.6641 | |
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| 1.8757 | 1.68 | 13000 | 1.7372 | 0.6649 | |
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| 1.8616 | 1.74 | 13500 | 1.7387 | 0.6646 | |
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| 1.8675 | 1.8 | 14000 | 1.7335 | 0.6648 | |
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| 1.8725 | 1.87 | 14500 | 1.7288 | 0.6660 | |
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| 1.8678 | 1.93 | 15000 | 1.7305 | 0.6659 | |
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| 1.8611 | 2.0 | 15500 | 1.7256 | 0.6666 | |
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| 1.853 | 2.06 | 16000 | 1.7286 | 0.6661 | |
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| 1.8487 | 2.13 | 16500 | 1.7285 | 0.6659 | |
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| 1.8543 | 2.19 | 17000 | 1.7229 | 0.6668 | |
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| 1.8519 | 2.26 | 17500 | 1.7240 | 0.6670 | |
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| 1.851 | 2.32 | 18000 | 1.7275 | 0.6662 | |
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| 1.8547 | 2.38 | 18500 | 1.7197 | 0.6673 | |
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| 1.8476 | 2.45 | 19000 | 1.7164 | 0.6675 | |
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| 1.8444 | 2.51 | 19500 | 1.7214 | 0.6676 | |
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| 1.8544 | 2.58 | 20000 | 1.7217 | 0.6668 | |
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| 1.8491 | 2.64 | 20500 | 1.7175 | 0.6678 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.2.0+cu121 |
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- Datasets 2.17.0 |
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- Tokenizers 0.15.2 |
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