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---
license: apache-2.0
tags:
- generated_from_trainer
base_model: facebook/bart-large
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-large](https://huggingface.co/facebook/bart-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6884
- Accuracy: 0.8172
- Precision: 0.8132
- Recall: 0.8172
- Precision Macro: 0.7584
- Recall Macro: 0.7412
- Macro Fpr: 0.0164
- Weighted Fpr: 0.0157
- Weighted Specificity: 0.9755
- Macro Specificity: 0.9862
- Weighted Sensitivity: 0.8172
- Macro Sensitivity: 0.7412
- F1 Micro: 0.8172
- F1 Macro: 0.7417
- F1 Weighted: 0.8124
## 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 | 0.9525 | 0.7157 | 0.6788 | 0.7157 | 0.3875 | 0.4416 | 0.0285 | 0.0276 | 0.9642 | 0.9787 | 0.7157 | 0.4416 | 0.7157 | 0.3958 | 0.6835 |
| No log | 2.0 | 321 | 0.7733 | 0.7413 | 0.7296 | 0.7413 | 0.4491 | 0.4687 | 0.0252 | 0.0243 | 0.9668 | 0.9805 | 0.7413 | 0.4687 | 0.7413 | 0.4337 | 0.7231 |
| No log | 3.0 | 482 | 0.7105 | 0.7738 | 0.7631 | 0.7738 | 0.5565 | 0.5408 | 0.0212 | 0.0205 | 0.9725 | 0.9831 | 0.7738 | 0.5408 | 0.7738 | 0.5271 | 0.7611 |
| 1.08 | 4.0 | 643 | 0.7539 | 0.7576 | 0.7584 | 0.7576 | 0.5791 | 0.5613 | 0.0234 | 0.0223 | 0.9681 | 0.9817 | 0.7576 | 0.5613 | 0.7576 | 0.5497 | 0.7438 |
| 1.08 | 5.0 | 803 | 0.6978 | 0.7831 | 0.7900 | 0.7831 | 0.7410 | 0.6492 | 0.0203 | 0.0194 | 0.9710 | 0.9836 | 0.7831 | 0.6492 | 0.7831 | 0.6354 | 0.7703 |
| 1.08 | 6.0 | 964 | 0.5920 | 0.8156 | 0.8053 | 0.8156 | 0.7051 | 0.6889 | 0.0166 | 0.0159 | 0.9746 | 0.9860 | 0.8156 | 0.6889 | 0.8156 | 0.6860 | 0.8088 |
| 0.5581 | 7.0 | 1125 | 0.6231 | 0.8187 | 0.8178 | 0.8187 | 0.7627 | 0.7425 | 0.0162 | 0.0156 | 0.9766 | 0.9864 | 0.8187 | 0.7425 | 0.8187 | 0.7393 | 0.8147 |
| 0.5581 | 8.0 | 1286 | 0.6291 | 0.8141 | 0.8134 | 0.8141 | 0.7636 | 0.7307 | 0.0167 | 0.0160 | 0.9758 | 0.9860 | 0.8141 | 0.7307 | 0.8141 | 0.7329 | 0.8089 |
| 0.5581 | 9.0 | 1446 | 0.6226 | 0.8242 | 0.8212 | 0.8242 | 0.7666 | 0.7340 | 0.0158 | 0.0150 | 0.9760 | 0.9867 | 0.8242 | 0.7340 | 0.8242 | 0.7365 | 0.8191 |
| 0.3924 | 10.0 | 1607 | 0.6728 | 0.8110 | 0.8123 | 0.8110 | 0.7418 | 0.7289 | 0.0170 | 0.0164 | 0.9762 | 0.9858 | 0.8110 | 0.7289 | 0.8110 | 0.7240 | 0.8048 |
| 0.3924 | 11.0 | 1768 | 0.6805 | 0.8095 | 0.8123 | 0.8095 | 0.7390 | 0.7303 | 0.0173 | 0.0165 | 0.9752 | 0.9856 | 0.8095 | 0.7303 | 0.8095 | 0.7263 | 0.8026 |
| 0.3924 | 12.0 | 1929 | 0.6710 | 0.8133 | 0.8137 | 0.8133 | 0.7396 | 0.7306 | 0.0168 | 0.0161 | 0.9759 | 0.9859 | 0.8133 | 0.7306 | 0.8133 | 0.7284 | 0.8090 |
| 0.2929 | 13.0 | 2089 | 0.6740 | 0.8187 | 0.8170 | 0.8187 | 0.7644 | 0.7360 | 0.0162 | 0.0156 | 0.9761 | 0.9863 | 0.8187 | 0.7360 | 0.8187 | 0.7368 | 0.8151 |
| 0.2929 | 14.0 | 2250 | 0.6823 | 0.8180 | 0.8159 | 0.8180 | 0.7657 | 0.7336 | 0.0164 | 0.0156 | 0.9753 | 0.9862 | 0.8180 | 0.7336 | 0.8180 | 0.7361 | 0.8137 |
| 0.2929 | 14.93 | 2400 | 0.6884 | 0.8172 | 0.8132 | 0.8172 | 0.7584 | 0.7412 | 0.0164 | 0.0157 | 0.9755 | 0.9862 | 0.8172 | 0.7412 | 0.8172 | 0.7417 | 0.8124 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.19.0
- Tokenizers 0.15.1
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