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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-adapter-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: 0.6258363412553052 |
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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-adapter-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: 2.0392 |
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- Accuracy: 0.6258 |
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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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| 3.9488 | 0.06 | 500 | 3.1500 | 0.5509 | |
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| 2.942 | 0.13 | 1000 | 2.5844 | 0.5680 | |
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| 2.6751 | 0.19 | 1500 | 2.4443 | 0.5790 | |
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| 2.582 | 0.26 | 2000 | 2.3701 | 0.5869 | |
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| 2.5267 | 0.32 | 2500 | 2.3097 | 0.5937 | |
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| 2.4722 | 0.39 | 3000 | 2.2695 | 0.5986 | |
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| 2.4289 | 0.45 | 3500 | 2.2329 | 0.6024 | |
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| 2.404 | 0.52 | 4000 | 2.2063 | 0.6055 | |
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| 2.3826 | 0.58 | 4500 | 2.1840 | 0.6087 | |
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| 2.3633 | 0.64 | 5000 | 2.1646 | 0.6109 | |
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| 2.3425 | 0.71 | 5500 | 2.1557 | 0.6121 | |
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| 2.333 | 0.77 | 6000 | 2.1350 | 0.6141 | |
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| 2.311 | 0.84 | 6500 | 2.1292 | 0.6152 | |
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| 2.3014 | 0.9 | 7000 | 2.1182 | 0.6166 | |
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| 2.2974 | 0.97 | 7500 | 2.1121 | 0.6170 | |
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| 2.2866 | 1.03 | 8000 | 2.1079 | 0.6173 | |
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| 2.2675 | 1.1 | 8500 | 2.0940 | 0.6192 | |
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| 2.2789 | 1.16 | 9000 | 2.0882 | 0.6201 | |
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| 2.2684 | 1.22 | 9500 | 2.0873 | 0.6200 | |
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| 2.2608 | 1.29 | 10000 | 2.0796 | 0.6209 | |
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| 2.2478 | 1.35 | 10500 | 2.0827 | 0.6204 | |
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| 2.2524 | 1.42 | 11000 | 2.0741 | 0.6215 | |
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| 2.2502 | 1.48 | 11500 | 2.0685 | 0.6220 | |
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| 2.243 | 1.55 | 12000 | 2.0665 | 0.6228 | |
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| 2.2417 | 1.61 | 12500 | 2.0632 | 0.6229 | |
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| 2.2398 | 1.68 | 13000 | 2.0593 | 0.6232 | |
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| 2.2233 | 1.74 | 13500 | 2.0600 | 0.6232 | |
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| 2.2277 | 1.8 | 14000 | 2.0535 | 0.6236 | |
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| 2.2344 | 1.87 | 14500 | 2.0485 | 0.6248 | |
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| 2.2274 | 1.93 | 15000 | 2.0507 | 0.6245 | |
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| 2.2212 | 2.0 | 15500 | 2.0428 | 0.6256 | |
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| 2.214 | 2.06 | 16000 | 2.0464 | 0.6244 | |
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| 2.2104 | 2.13 | 16500 | 2.0477 | 0.6250 | |
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| 2.2185 | 2.19 | 17000 | 2.0397 | 0.6257 | |
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| 2.2157 | 2.26 | 17500 | 2.0419 | 0.6257 | |
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| 2.2128 | 2.32 | 18000 | 2.0439 | 0.6255 | |
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| 2.2154 | 2.38 | 18500 | 2.0372 | 0.6259 | |
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| 2.2099 | 2.45 | 19000 | 2.0337 | 0.6263 | |
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| 2.2045 | 2.51 | 19500 | 2.0396 | 0.6259 | |
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| 2.2138 | 2.58 | 20000 | 2.0390 | 0.6262 | |
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| 2.2103 | 2.64 | 20500 | 2.0339 | 0.6263 | |
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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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