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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-5 |
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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-5 |
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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: 2.6151 |
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- Accuracy: 0.675 |
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- Precision: 0.6356 |
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- Recall: 0.6360 |
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- F1: 0.6356 |
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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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| 0.9766 | 1.0 | 200 | 0.8865 | 0.645 | 0.5935 | 0.5872 | 0.5881 | |
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| 0.7725 | 2.0 | 400 | 1.0650 | 0.665 | 0.7143 | 0.5936 | 0.5556 | |
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| 0.6018 | 3.0 | 600 | 0.8558 | 0.7 | 0.6637 | 0.6444 | 0.6456 | |
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| 0.3838 | 4.0 | 800 | 0.9796 | 0.67 | 0.6220 | 0.6219 | 0.6218 | |
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| 0.2135 | 5.0 | 1000 | 1.4533 | 0.675 | 0.6611 | 0.5955 | 0.6055 | |
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| 0.1209 | 6.0 | 1200 | 1.4688 | 0.67 | 0.6392 | 0.6474 | 0.6398 | |
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| 0.072 | 7.0 | 1400 | 1.8395 | 0.695 | 0.6574 | 0.6540 | 0.6514 | |
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| 0.0211 | 8.0 | 1600 | 2.0849 | 0.7 | 0.6691 | 0.6607 | 0.6603 | |
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| 0.0102 | 9.0 | 1800 | 2.3042 | 0.695 | 0.6675 | 0.6482 | 0.6533 | |
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| 0.0132 | 10.0 | 2000 | 2.2390 | 0.685 | 0.6472 | 0.6423 | 0.6439 | |
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| 0.004 | 11.0 | 2200 | 2.3779 | 0.68 | 0.6435 | 0.6481 | 0.6443 | |
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| 0.0004 | 12.0 | 2400 | 2.4575 | 0.675 | 0.6397 | 0.6352 | 0.6357 | |
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| 0.0003 | 13.0 | 2600 | 2.4676 | 0.675 | 0.6356 | 0.6360 | 0.6356 | |
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| 0.0003 | 14.0 | 2800 | 2.5109 | 0.68 | 0.6427 | 0.6424 | 0.6422 | |
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| 0.0002 | 15.0 | 3000 | 2.5470 | 0.675 | 0.6356 | 0.6360 | 0.6356 | |
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| 0.0002 | 16.0 | 3200 | 2.5674 | 0.675 | 0.6356 | 0.6360 | 0.6356 | |
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| 0.0001 | 17.0 | 3400 | 2.5889 | 0.685 | 0.6471 | 0.6488 | 0.6474 | |
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| 0.0001 | 18.0 | 3600 | 2.6016 | 0.675 | 0.6356 | 0.6360 | 0.6356 | |
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| 0.0001 | 19.0 | 3800 | 2.6108 | 0.675 | 0.6356 | 0.6360 | 0.6356 | |
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| 0.0001 | 20.0 | 4000 | 2.6151 | 0.675 | 0.6356 | 0.6360 | 0.6356 | |
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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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