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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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base_model: roberta-base |
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model-index: |
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- name: source-role-model |
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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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# source-role-model |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co./roberta-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.5543 |
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- F1: 0.5814 |
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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: 5e-05 |
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- train_batch_size: 5 |
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- eval_batch_size: 5 |
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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: 10.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| No log | 0.12 | 100 | 1.0000 | 0.3391 | |
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| No log | 0.25 | 200 | 0.8371 | 0.5055 | |
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| No log | 0.37 | 300 | 0.8684 | 0.5019 | |
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| No log | 0.49 | 400 | 0.8668 | 0.5208 | |
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| 0.9644 | 0.62 | 500 | 0.8473 | 0.5422 | |
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| 0.9644 | 0.74 | 600 | 0.8852 | 0.4956 | |
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| 0.9644 | 0.86 | 700 | 0.8368 | 0.5124 | |
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| 0.9644 | 0.99 | 800 | 0.7913 | 0.5848 | |
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| 0.9644 | 1.11 | 900 | 1.0570 | 0.4950 | |
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| 0.8375 | 1.23 | 1000 | 0.9402 | 0.5280 | |
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| 0.8375 | 1.35 | 1100 | 0.8023 | 0.5084 | |
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| 0.8375 | 1.48 | 1200 | 0.9299 | 0.4807 | |
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| 0.8375 | 1.6 | 1300 | 0.9661 | 0.5194 | |
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| 0.8375 | 1.72 | 1400 | 0.8014 | 0.6016 | |
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| 0.8149 | 1.85 | 1500 | 0.8608 | 0.6105 | |
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| 0.8149 | 1.97 | 1600 | 0.9195 | 0.5741 | |
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| 0.8149 | 2.09 | 1700 | 1.2378 | 0.5964 | |
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| 0.8149 | 2.22 | 1800 | 1.0415 | 0.5902 | |
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| 0.8149 | 2.34 | 1900 | 1.0499 | 0.5526 | |
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| 0.6932 | 2.46 | 2000 | 1.0600 | 0.5832 | |
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| 0.6932 | 2.59 | 2100 | 0.9368 | 0.6074 | |
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| 0.6932 | 2.71 | 2200 | 1.0872 | 0.6270 | |
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| 0.6932 | 2.83 | 2300 | 1.0912 | 0.5707 | |
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| 0.6932 | 2.96 | 2400 | 0.8815 | 0.5602 | |
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| 0.6214 | 3.08 | 2500 | 1.1650 | 0.5993 | |
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| 0.6214 | 3.2 | 2600 | 1.4485 | 0.5821 | |
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| 0.6214 | 3.33 | 2700 | 1.5382 | 0.5775 | |
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| 0.6214 | 3.45 | 2800 | 1.3999 | 0.5696 | |
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| 0.6214 | 3.57 | 2900 | 1.3702 | 0.6114 | |
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| 0.5686 | 3.69 | 3000 | 1.3840 | 0.5635 | |
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| 0.5686 | 3.82 | 3100 | 1.3547 | 0.5403 | |
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| 0.5686 | 3.94 | 3200 | 1.0283 | 0.5723 | |
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| 0.5686 | 4.06 | 3300 | 1.3593 | 0.6242 | |
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| 0.5686 | 4.19 | 3400 | 1.5985 | 0.6004 | |
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| 0.4807 | 4.31 | 3500 | 1.5351 | 0.6177 | |
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| 0.4807 | 4.43 | 3600 | 1.4109 | 0.5779 | |
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| 0.4807 | 4.56 | 3700 | 1.6972 | 0.5637 | |
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| 0.4807 | 4.68 | 3800 | 1.5336 | 0.6047 | |
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| 0.4807 | 4.8 | 3900 | 1.7811 | 0.5909 | |
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| 0.4387 | 4.93 | 4000 | 1.5862 | 0.5869 | |
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| 0.4387 | 5.05 | 4100 | 1.7106 | 0.5637 | |
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| 0.4387 | 5.17 | 4200 | 1.5251 | 0.5624 | |
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| 0.4387 | 5.3 | 4300 | 1.5519 | 0.5944 | |
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| 0.4387 | 5.42 | 4400 | 1.7315 | 0.5908 | |
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| 0.3219 | 5.54 | 4500 | 1.7588 | 0.6015 | |
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| 0.3219 | 5.67 | 4600 | 1.9277 | 0.5635 | |
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| 0.3219 | 5.79 | 4700 | 1.7663 | 0.5891 | |
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| 0.3219 | 5.91 | 4800 | 1.8401 | 0.5917 | |
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| 0.3219 | 6.03 | 4900 | 2.0516 | 0.5845 | |
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| 0.2311 | 6.16 | 5000 | 2.0510 | 0.6166 | |
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| 0.2311 | 6.28 | 5100 | 2.1673 | 0.5732 | |
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| 0.2311 | 6.4 | 5200 | 2.0931 | 0.5819 | |
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| 0.2311 | 6.53 | 5300 | 2.2803 | 0.5961 | |
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| 0.2311 | 6.65 | 5400 | 1.9985 | 0.6010 | |
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| 0.1669 | 6.77 | 5500 | 2.1742 | 0.5664 | |
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| 0.1669 | 6.9 | 5600 | 2.1021 | 0.5732 | |
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| 0.1669 | 7.02 | 5700 | 2.2043 | 0.5641 | |
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| 0.1669 | 7.14 | 5800 | 2.2018 | 0.5837 | |
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| 0.1669 | 7.27 | 5900 | 2.3575 | 0.5721 | |
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| 0.1698 | 7.39 | 6000 | 2.4663 | 0.5662 | |
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| 0.1698 | 7.51 | 6100 | 2.2658 | 0.5851 | |
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| 0.1698 | 7.64 | 6200 | 2.1585 | 0.5676 | |
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| 0.1698 | 7.76 | 6300 | 2.1755 | 0.5774 | |
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| 0.1698 | 7.88 | 6400 | 2.2680 | 0.5696 | |
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| 0.1378 | 8.0 | 6500 | 2.3505 | 0.5615 | |
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| 0.1378 | 8.13 | 6600 | 2.2773 | 0.5705 | |
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| 0.1378 | 8.25 | 6700 | 2.3112 | 0.5662 | |
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| 0.1378 | 8.37 | 6800 | 2.4572 | 0.5679 | |
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| 0.1378 | 8.5 | 6900 | 2.4642 | 0.5766 | |
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| 0.0756 | 8.62 | 7000 | 2.4643 | 0.5885 | |
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| 0.0756 | 8.74 | 7100 | 2.5096 | 0.5779 | |
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| 0.0756 | 8.87 | 7200 | 2.4261 | 0.5789 | |
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| 0.0756 | 8.99 | 7300 | 2.3973 | 0.5757 | |
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| 0.0756 | 9.11 | 7400 | 2.4137 | 0.5906 | |
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| 0.0842 | 9.24 | 7500 | 2.4577 | 0.5844 | |
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| 0.0842 | 9.36 | 7600 | 2.5034 | 0.5840 | |
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| 0.0842 | 9.48 | 7700 | 2.5176 | 0.5810 | |
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| 0.0842 | 9.61 | 7800 | 2.5240 | 0.5852 | |
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| 0.0842 | 9.73 | 7900 | 2.5141 | 0.5824 | |
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| 0.0634 | 9.85 | 8000 | 2.5482 | 0.5814 | |
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| 0.0634 | 9.98 | 8100 | 2.5543 | 0.5814 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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