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---
license: apache-2.0
base_model: facebook/bart-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
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: 0.6655
- Accuracy: 0.7963
- Precision: 0.7841
- Recall: 0.7963
- Precision Macro: 0.5968
- Recall Macro: 0.6325
- Macro Fpr: 0.0186
- Weighted Fpr: 0.0179
- Weighted Specificity: 0.9749
- Macro Specificity: 0.9847
- Weighted Sensitivity: 0.7963
- Macro Sensitivity: 0.6325
- F1 Micro: 0.7963
- F1 Macro: 0.6074
- F1 Weighted: 0.7859
## 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 | 1.2642 | 0.6313 | 0.5477 | 0.6313 | 0.3009 | 0.3127 | 0.0428 | 0.0400 | 0.9351 | 0.9711 | 0.6313 | 0.3127 | 0.6313 | 0.2941 | 0.5769 |
| No log | 2.0 | 321 | 0.8962 | 0.7119 | 0.6939 | 0.7119 | 0.3937 | 0.4525 | 0.0285 | 0.0281 | 0.9669 | 0.9786 | 0.7119 | 0.4525 | 0.7119 | 0.4107 | 0.6960 |
| No log | 3.0 | 482 | 0.8204 | 0.7196 | 0.6953 | 0.7196 | 0.3974 | 0.4468 | 0.0278 | 0.0271 | 0.9653 | 0.9790 | 0.7196 | 0.4468 | 0.7196 | 0.3998 | 0.6885 |
| 1.2731 | 4.0 | 643 | 0.7519 | 0.7436 | 0.7186 | 0.7436 | 0.4131 | 0.4673 | 0.0244 | 0.0240 | 0.9695 | 0.9809 | 0.7436 | 0.4673 | 0.7436 | 0.4272 | 0.7248 |
| 1.2731 | 5.0 | 803 | 0.7364 | 0.7475 | 0.7524 | 0.7475 | 0.6132 | 0.5050 | 0.0243 | 0.0236 | 0.9679 | 0.9810 | 0.7475 | 0.5050 | 0.7475 | 0.4905 | 0.7286 |
| 1.2731 | 6.0 | 964 | 0.7273 | 0.7514 | 0.7423 | 0.7514 | 0.5784 | 0.5258 | 0.0237 | 0.0231 | 0.9699 | 0.9814 | 0.7514 | 0.5258 | 0.7514 | 0.5150 | 0.7311 |
| 0.7243 | 7.0 | 1125 | 0.6993 | 0.7645 | 0.7478 | 0.7645 | 0.5498 | 0.5565 | 0.0222 | 0.0215 | 0.9721 | 0.9824 | 0.7645 | 0.5565 | 0.7645 | 0.5453 | 0.7538 |
| 0.7243 | 8.0 | 1286 | 0.6952 | 0.7769 | 0.7639 | 0.7769 | 0.5682 | 0.5888 | 0.0207 | 0.0201 | 0.9731 | 0.9833 | 0.7769 | 0.5888 | 0.7769 | 0.5700 | 0.7649 |
| 0.7243 | 9.0 | 1446 | 0.6759 | 0.7823 | 0.7708 | 0.7823 | 0.5764 | 0.5877 | 0.0201 | 0.0195 | 0.9739 | 0.9838 | 0.7823 | 0.5877 | 0.7823 | 0.5699 | 0.7697 |
| 0.6098 | 10.0 | 1607 | 0.6705 | 0.7847 | 0.7720 | 0.7847 | 0.5899 | 0.6176 | 0.0199 | 0.0192 | 0.9732 | 0.9839 | 0.7847 | 0.6176 | 0.7847 | 0.5935 | 0.7724 |
| 0.6098 | 11.0 | 1768 | 0.6794 | 0.7909 | 0.7737 | 0.7909 | 0.5882 | 0.6237 | 0.0193 | 0.0185 | 0.9736 | 0.9843 | 0.7909 | 0.6237 | 0.7909 | 0.5988 | 0.7773 |
| 0.6098 | 12.0 | 1929 | 0.6836 | 0.7909 | 0.7816 | 0.7909 | 0.5973 | 0.6285 | 0.0192 | 0.0185 | 0.9742 | 0.9843 | 0.7909 | 0.6285 | 0.7909 | 0.6034 | 0.7802 |
| 0.5239 | 13.0 | 2089 | 0.6508 | 0.7932 | 0.7783 | 0.7932 | 0.5965 | 0.6273 | 0.0189 | 0.0183 | 0.9738 | 0.9845 | 0.7932 | 0.6273 | 0.7932 | 0.6046 | 0.7821 |
| 0.5239 | 14.0 | 2250 | 0.6588 | 0.7963 | 0.7823 | 0.7963 | 0.5957 | 0.6290 | 0.0186 | 0.0179 | 0.9746 | 0.9847 | 0.7963 | 0.6290 | 0.7963 | 0.6055 | 0.7852 |
| 0.5239 | 14.93 | 2400 | 0.6655 | 0.7963 | 0.7841 | 0.7963 | 0.5968 | 0.6325 | 0.0186 | 0.0179 | 0.9749 | 0.9847 | 0.7963 | 0.6325 | 0.7963 | 0.6074 | 0.7859 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1
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