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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
base_model: facebook/bart-base
model-index:
- name: bart-base-lora
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-lora
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6614
- Accuracy: 0.7909
- Precision: 0.7794
- Recall: 0.7909
- Precision Macro: 0.6664
- Recall Macro: 0.6485
- Macro Fpr: 0.0194
- Weighted Fpr: 0.0186
- Weighted Specificity: 0.9735
- Macro Specificity: 0.9842
- Weighted Sensitivity: 0.7901
- Macro Sensitivity: 0.6485
- F1 Micro: 0.7901
- F1 Macro: 0.6250
- F1 Weighted: 0.7804
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:---------------:|:------------:|:---------:|:------------:|:--------------------:|:-----------------:|:--------------------:|:-----------------:|:--------:|:--------:|:-----------:|
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1
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