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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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base_model: facebook/bart-base |
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model-index: |
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- name: bart-base-lora |
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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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# bart-base-lora |
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This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6614 |
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- Accuracy: 0.7909 |
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- Precision: 0.7794 |
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- Recall: 0.7909 |
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- Precision Macro: 0.6664 |
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- Recall Macro: 0.6485 |
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- Macro Fpr: 0.0194 |
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- Weighted Fpr: 0.0186 |
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- Weighted Specificity: 0.9735 |
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- Macro Specificity: 0.9842 |
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- Weighted Sensitivity: 0.7901 |
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- Macro Sensitivity: 0.6485 |
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- F1 Micro: 0.7901 |
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- F1 Macro: 0.6250 |
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- F1 Weighted: 0.7804 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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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: 15 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | Precision Macro | Recall Macro | Macro Fpr | Weighted Fpr | Weighted Specificity | Macro Specificity | Weighted Sensitivity | Macro Sensitivity | F1 Micro | F1 Macro | F1 Weighted | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:---------------:|:------------:|:---------:|:------------:|:--------------------:|:-----------------:|:--------------------:|:-----------------:|:--------:|:--------:|:-----------:| |
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| No log | 1.0 | 160 | 1.3205 | 0.6112 | 0.5322 | 0.6112 | 0.2887 | 0.3024 | 0.0464 | 0.0435 | 0.9266 | 0.9692 | 0.6112 | 0.3024 | 0.6112 | 0.2871 | 0.5575 | |
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| No log | 2.0 | 321 | 0.8875 | 0.6995 | 0.6728 | 0.6995 | 0.3822 | 0.4254 | 0.0306 | 0.0298 | 0.9609 | 0.9774 | 0.6995 | 0.4254 | 0.6995 | 0.3948 | 0.6808 | |
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| No log | 3.0 | 482 | 0.8427 | 0.7064 | 0.6952 | 0.7064 | 0.4131 | 0.4442 | 0.0295 | 0.0288 | 0.9641 | 0.9780 | 0.7064 | 0.4442 | 0.7064 | 0.3969 | 0.6752 | |
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| 1.2895 | 4.0 | 643 | 0.7719 | 0.7273 | 0.7132 | 0.7273 | 0.4198 | 0.4598 | 0.0264 | 0.0261 | 0.9690 | 0.9798 | 0.7273 | 0.4598 | 0.7273 | 0.4284 | 0.7167 | |
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| 1.2895 | 5.0 | 803 | 0.7388 | 0.7506 | 0.7400 | 0.7506 | 0.5733 | 0.5165 | 0.0239 | 0.0232 | 0.9697 | 0.9814 | 0.7506 | 0.5165 | 0.7506 | 0.5072 | 0.7368 | |
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| 1.2895 | 6.0 | 964 | 0.7526 | 0.7444 | 0.7337 | 0.7444 | 0.5703 | 0.5230 | 0.0247 | 0.0239 | 0.9691 | 0.9809 | 0.7444 | 0.5230 | 0.7444 | 0.5088 | 0.7268 | |
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| 0.7332 | 7.0 | 1125 | 0.7082 | 0.7552 | 0.7436 | 0.7552 | 0.5665 | 0.5728 | 0.0233 | 0.0226 | 0.9712 | 0.9818 | 0.7552 | 0.5728 | 0.7552 | 0.5609 | 0.7461 | |
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| 0.7332 | 8.0 | 1286 | 0.7161 | 0.7583 | 0.7489 | 0.7583 | 0.5641 | 0.5975 | 0.0228 | 0.0223 | 0.9721 | 0.9820 | 0.7583 | 0.5975 | 0.7583 | 0.5756 | 0.7503 | |
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| 0.7332 | 9.0 | 1446 | 0.6831 | 0.7777 | 0.7587 | 0.7777 | 0.5781 | 0.6069 | 0.0208 | 0.0200 | 0.9715 | 0.9833 | 0.7777 | 0.6069 | 0.7777 | 0.5875 | 0.7653 | |
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| 0.6167 | 10.0 | 1607 | 0.6683 | 0.7862 | 0.7714 | 0.7862 | 0.5917 | 0.6174 | 0.0198 | 0.0191 | 0.9728 | 0.9839 | 0.7862 | 0.6174 | 0.7862 | 0.5987 | 0.7754 | |
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| 0.6167 | 11.0 | 1768 | 0.6885 | 0.7761 | 0.7628 | 0.7761 | 0.5817 | 0.6220 | 0.0210 | 0.0202 | 0.9723 | 0.9832 | 0.7761 | 0.6220 | 0.7761 | 0.5946 | 0.7642 | |
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| 0.6167 | 12.0 | 1929 | 0.6830 | 0.7870 | 0.7826 | 0.7870 | 0.6627 | 0.6464 | 0.0197 | 0.0190 | 0.9734 | 0.9840 | 0.7870 | 0.6464 | 0.7870 | 0.6214 | 0.7764 | |
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| 0.5314 | 13.0 | 2089 | 0.6605 | 0.7916 | 0.7770 | 0.7916 | 0.5965 | 0.6358 | 0.0192 | 0.0185 | 0.9741 | 0.9844 | 0.7916 | 0.6358 | 0.7916 | 0.6111 | 0.7818 | |
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| 0.5314 | 14.0 | 2250 | 0.6614 | 0.7909 | 0.7794 | 0.7909 | 0.6368 | 0.6478 | 0.0193 | 0.0185 | 0.9729 | 0.9842 | 0.7909 | 0.6478 | 0.7909 | 0.6261 | 0.7803 | |
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| 0.5314 | 14.93 | 2400 | 0.6647 | 0.7901 | 0.7852 | 0.7901 | 0.6664 | 0.6485 | 0.0194 | 0.0186 | 0.9735 | 0.9842 | 0.7901 | 0.6485 | 0.7901 | 0.6250 | 0.7804 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.1 |
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