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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-3 |
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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-3 |
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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: 2.4381 |
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- Accuracy: 0.7 |
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- Precision: 0.6579 |
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- Recall: 0.6558 |
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- F1: 0.6568 |
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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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| 0.9876 | 1.0 | 200 | 0.9367 | 0.64 | 0.6707 | 0.5623 | 0.5425 | |
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| 0.7553 | 2.0 | 400 | 0.7936 | 0.66 | 0.6269 | 0.6298 | 0.6105 | |
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| 0.556 | 3.0 | 600 | 0.9257 | 0.71 | 0.6759 | 0.6504 | 0.6563 | |
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| 0.3871 | 4.0 | 800 | 0.9893 | 0.63 | 0.5882 | 0.5985 | 0.5876 | |
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| 0.2446 | 5.0 | 1000 | 1.1867 | 0.695 | 0.6582 | 0.6563 | 0.6566 | |
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| 0.1502 | 6.0 | 1200 | 1.6108 | 0.71 | 0.6708 | 0.6523 | 0.6585 | |
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| 0.1049 | 7.0 | 1400 | 2.4882 | 0.645 | 0.6030 | 0.5597 | 0.5649 | |
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| 0.0764 | 8.0 | 1600 | 2.0064 | 0.715 | 0.6798 | 0.6602 | 0.6651 | |
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| 0.032 | 9.0 | 1800 | 2.6447 | 0.655 | 0.5913 | 0.5774 | 0.5727 | |
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| 0.0177 | 10.0 | 2000 | 2.2460 | 0.675 | 0.6290 | 0.6287 | 0.6287 | |
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| 0.0153 | 11.0 | 2200 | 2.3537 | 0.69 | 0.6524 | 0.6407 | 0.6408 | |
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| 0.006 | 12.0 | 2400 | 2.4205 | 0.695 | 0.6582 | 0.6448 | 0.6486 | |
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| 0.0045 | 13.0 | 2600 | 2.3032 | 0.68 | 0.6394 | 0.6314 | 0.6287 | |
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| 0.0038 | 14.0 | 2800 | 2.3506 | 0.685 | 0.6388 | 0.6370 | 0.6367 | |
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| 0.0034 | 15.0 | 3000 | 2.3750 | 0.7 | 0.6590 | 0.6558 | 0.6573 | |
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| 0.0019 | 16.0 | 3200 | 2.4289 | 0.72 | 0.6819 | 0.6723 | 0.6763 | |
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| 0.0016 | 17.0 | 3400 | 2.4470 | 0.725 | 0.6892 | 0.6788 | 0.6830 | |
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| 0.0002 | 18.0 | 3600 | 2.4374 | 0.71 | 0.6700 | 0.6626 | 0.6657 | |
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| 0.0002 | 19.0 | 3800 | 2.4353 | 0.7 | 0.6579 | 0.6558 | 0.6568 | |
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| 0.0002 | 20.0 | 4000 | 2.4381 | 0.7 | 0.6579 | 0.6558 | 0.6568 | |
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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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