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--- |
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license: apache-2.0 |
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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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base_model: albert-base-v2 |
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model-index: |
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- name: albert-base-ours-run-4 |
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results: [] |
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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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# albert-base-ours-run-4 |
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This model is a fine-tuned version of [albert-base-v2](https://huggingface.co./albert-base-v2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.9565 |
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- Accuracy: 0.72 |
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- Precision: 0.6790 |
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- Recall: 0.6770 |
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- F1: 0.6766 |
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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: 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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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 1.0253 | 1.0 | 200 | 0.8974 | 0.605 | 0.7186 | 0.5341 | 0.4555 | |
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| 0.8121 | 2.0 | 400 | 0.8260 | 0.675 | 0.6792 | 0.6308 | 0.6112 | |
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| 0.6153 | 3.0 | 600 | 0.8504 | 0.66 | 0.6180 | 0.6026 | 0.6073 | |
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| 0.441 | 4.0 | 800 | 0.8917 | 0.685 | 0.6463 | 0.6385 | 0.6403 | |
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| 0.3273 | 5.0 | 1000 | 0.9384 | 0.69 | 0.6534 | 0.6602 | 0.6561 | |
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| 0.2138 | 6.0 | 1200 | 1.3501 | 0.705 | 0.6573 | 0.6374 | 0.6388 | |
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| 0.1435 | 7.0 | 1400 | 1.4614 | 0.71 | 0.6693 | 0.6553 | 0.6601 | |
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| 0.1202 | 8.0 | 1600 | 1.5825 | 0.7 | 0.6648 | 0.6592 | 0.6530 | |
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| 0.0587 | 9.0 | 1800 | 1.7755 | 0.72 | 0.6839 | 0.6849 | 0.6840 | |
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| 0.0237 | 10.0 | 2000 | 1.7240 | 0.735 | 0.6960 | 0.6924 | 0.6940 | |
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| 0.018 | 11.0 | 2200 | 1.7230 | 0.745 | 0.7105 | 0.7003 | 0.7026 | |
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| 0.0096 | 12.0 | 2400 | 1.7812 | 0.75 | 0.7225 | 0.7142 | 0.7158 | |
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| 0.006 | 13.0 | 2600 | 1.8223 | 0.75 | 0.7265 | 0.7082 | 0.7147 | |
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| 0.0033 | 14.0 | 2800 | 1.9872 | 0.76 | 0.7434 | 0.7107 | 0.7188 | |
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| 0.003 | 15.0 | 3000 | 1.8818 | 0.72 | 0.6778 | 0.6766 | 0.6765 | |
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| 0.0027 | 16.0 | 3200 | 1.9816 | 0.75 | 0.7125 | 0.6990 | 0.7043 | |
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| 0.002 | 17.0 | 3400 | 1.9268 | 0.725 | 0.6832 | 0.6834 | 0.6825 | |
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| 0.0023 | 18.0 | 3600 | 1.9456 | 0.73 | 0.6913 | 0.6898 | 0.6898 | |
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| 0.0025 | 19.0 | 3800 | 1.9543 | 0.72 | 0.6790 | 0.6770 | 0.6766 | |
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| 0.0016 | 20.0 | 4000 | 1.9565 | 0.72 | 0.6790 | 0.6770 | 0.6766 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.0+cu116 |
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- Tokenizers 0.13.2 |
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