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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
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
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: 1.7146
- Accuracy: 0.8180
- Precision: 0.8189
- Recall: 0.8180
- Precision Macro: 0.7608
- Recall Macro: 0.7799
- Macro Fpr: 0.0157
- Weighted Fpr: 0.0151
- Weighted Specificity: 0.9781
- Macro Specificity: 0.9868
- Weighted Sensitivity: 0.8234
- Macro Sensitivity: 0.7799
- F1 Micro: 0.8234
- F1 Macro: 0.7642
- F1 Weighted: 0.8237
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:---------------:|:------------:|:---------:|:------------:|:--------------------:|:-----------------:|:--------------------:|:-----------------:|:--------:|:--------:|:-----------:|
| 1.2028 | 1.0 | 643 | 0.8430 | 0.7599 | 0.7601 | 0.7599 | 0.6004 | 0.6367 | 0.0232 | 0.0221 | 0.9655 | 0.9817 | 0.7599 | 0.6367 | 0.7599 | 0.6064 | 0.7489 |
| 0.715 | 2.0 | 1286 | 0.7332 | 0.7932 | 0.8020 | 0.7932 | 0.7386 | 0.7321 | 0.0190 | 0.0183 | 0.9745 | 0.9845 | 0.7932 | 0.7321 | 0.7932 | 0.7214 | 0.7853 |
| 0.578 | 3.0 | 1929 | 0.8045 | 0.7940 | 0.8075 | 0.7940 | 0.7231 | 0.7069 | 0.0185 | 0.0182 | 0.9775 | 0.9848 | 0.7940 | 0.7069 | 0.7940 | 0.6998 | 0.7901 |
| 0.3938 | 4.0 | 2572 | 0.8291 | 0.8156 | 0.8171 | 0.8156 | 0.7937 | 0.7218 | 0.0169 | 0.0159 | 0.9711 | 0.9858 | 0.8156 | 0.7218 | 0.8156 | 0.7369 | 0.8105 |
| 0.3238 | 5.0 | 3215 | 0.8889 | 0.7940 | 0.8146 | 0.7940 | 0.7464 | 0.7515 | 0.0188 | 0.0182 | 0.9762 | 0.9847 | 0.7940 | 0.7515 | 0.7940 | 0.7361 | 0.7995 |
| 0.246 | 6.0 | 3858 | 1.1629 | 0.7955 | 0.8067 | 0.7955 | 0.7483 | 0.7600 | 0.0186 | 0.0180 | 0.9749 | 0.9847 | 0.7955 | 0.7600 | 0.7955 | 0.7362 | 0.7946 |
| 0.1791 | 7.0 | 4501 | 1.1354 | 0.8180 | 0.8151 | 0.8180 | 0.7832 | 0.7697 | 0.0165 | 0.0156 | 0.9747 | 0.9862 | 0.8180 | 0.7697 | 0.8180 | 0.7736 | 0.8147 |
| 0.1305 | 8.0 | 5144 | 1.2825 | 0.8110 | 0.8148 | 0.8110 | 0.7422 | 0.7489 | 0.0169 | 0.0164 | 0.9765 | 0.9858 | 0.8110 | 0.7489 | 0.8110 | 0.7369 | 0.8088 |
| 0.0924 | 9.0 | 5787 | 1.4217 | 0.8040 | 0.8114 | 0.8040 | 0.7465 | 0.7809 | 0.0178 | 0.0171 | 0.9762 | 0.9853 | 0.8040 | 0.7809 | 0.8040 | 0.7560 | 0.8015 |
| 0.0953 | 10.0 | 6430 | 1.5552 | 0.8025 | 0.8056 | 0.8025 | 0.7702 | 0.7822 | 0.0183 | 0.0173 | 0.9712 | 0.9849 | 0.8025 | 0.7822 | 0.8025 | 0.7661 | 0.8001 |
| 0.0617 | 11.0 | 7073 | 1.5224 | 0.8040 | 0.8144 | 0.8040 | 0.7457 | 0.7512 | 0.0176 | 0.0171 | 0.9762 | 0.9853 | 0.8040 | 0.7512 | 0.8040 | 0.7422 | 0.8070 |
| 0.0582 | 12.0 | 7716 | 1.6428 | 0.7971 | 0.8148 | 0.7971 | 0.7470 | 0.7655 | 0.0183 | 0.0179 | 0.9771 | 0.9849 | 0.7971 | 0.7655 | 0.7971 | 0.7465 | 0.8022 |
| 0.0511 | 13.0 | 8359 | 1.4952 | 0.8195 | 0.8208 | 0.8195 | 0.7645 | 0.7580 | 0.0162 | 0.0155 | 0.9759 | 0.9864 | 0.8195 | 0.7580 | 0.8195 | 0.7586 | 0.8187 |
| 0.0476 | 14.0 | 9002 | 1.7132 | 0.7971 | 0.7958 | 0.7971 | 0.7637 | 0.7328 | 0.0189 | 0.0179 | 0.9708 | 0.9845 | 0.7971 | 0.7328 | 0.7971 | 0.7417 | 0.7913 |
| 0.0375 | 15.0 | 9645 | 1.7058 | 0.8002 | 0.8110 | 0.8002 | 0.7369 | 0.7696 | 0.0182 | 0.0175 | 0.9757 | 0.9851 | 0.8002 | 0.7696 | 0.8002 | 0.7437 | 0.8017 |
| 0.0241 | 16.0 | 10288 | 1.7146 | 0.8180 | 0.8189 | 0.8180 | 0.7852 | 0.7787 | 0.0162 | 0.0156 | 0.9761 | 0.9863 | 0.8180 | 0.7787 | 0.8180 | 0.7780 | 0.8174 |
| 0.0226 | 17.0 | 10931 | 1.7035 | 0.8203 | 0.8238 | 0.8203 | 0.7732 | 0.7781 | 0.0160 | 0.0154 | 0.9774 | 0.9865 | 0.8203 | 0.7781 | 0.8203 | 0.7714 | 0.8206 |
| 0.0189 | 18.0 | 11574 | 1.8079 | 0.8164 | 0.8160 | 0.8164 | 0.7583 | 0.7677 | 0.0166 | 0.0158 | 0.9749 | 0.9861 | 0.8164 | 0.7677 | 0.8164 | 0.7578 | 0.8149 |
| 0.026 | 19.0 | 12217 | 1.8187 | 0.8125 | 0.8170 | 0.8125 | 0.7675 | 0.7833 | 0.0169 | 0.0162 | 0.9748 | 0.9858 | 0.8125 | 0.7833 | 0.8125 | 0.7719 | 0.8138 |
| 0.0101 | 20.0 | 12860 | 1.8354 | 0.8187 | 0.8220 | 0.8187 | 0.7748 | 0.7818 | 0.0161 | 0.0156 | 0.9772 | 0.9864 | 0.8187 | 0.7818 | 0.8187 | 0.7710 | 0.8180 |
| 0.0216 | 21.0 | 13503 | 1.8372 | 0.8156 | 0.8219 | 0.8156 | 0.7502 | 0.7858 | 0.0163 | 0.0159 | 0.9789 | 0.9863 | 0.8156 | 0.7858 | 0.8156 | 0.7618 | 0.8164 |
| 0.0138 | 22.0 | 14146 | 1.8472 | 0.8203 | 0.8263 | 0.8203 | 0.7613 | 0.7796 | 0.0159 | 0.0154 | 0.9786 | 0.9866 | 0.8203 | 0.7796 | 0.8203 | 0.7662 | 0.8222 |
| 0.0169 | 23.0 | 14789 | 1.8104 | 0.8218 | 0.8252 | 0.8218 | 0.7719 | 0.7595 | 0.0160 | 0.0152 | 0.9749 | 0.9865 | 0.8218 | 0.7595 | 0.8218 | 0.7607 | 0.8209 |
| 0.0079 | 24.0 | 15432 | 1.9253 | 0.8110 | 0.8202 | 0.8110 | 0.7622 | 0.7576 | 0.0171 | 0.0164 | 0.9759 | 0.9858 | 0.8110 | 0.7576 | 0.8110 | 0.7524 | 0.8123 |
| 0.0017 | 25.0 | 16075 | 1.9111 | 0.8156 | 0.8193 | 0.8156 | 0.7554 | 0.7742 | 0.0164 | 0.0159 | 0.9775 | 0.9862 | 0.8156 | 0.7742 | 0.8156 | 0.7594 | 0.8155 |
| 0.0071 | 26.0 | 16718 | 1.8809 | 0.8187 | 0.8244 | 0.8187 | 0.7595 | 0.7749 | 0.0161 | 0.0156 | 0.9783 | 0.9865 | 0.8187 | 0.7749 | 0.8187 | 0.7601 | 0.8199 |
| 0.0032 | 27.0 | 17361 | 1.8246 | 0.8273 | 0.8333 | 0.8273 | 0.7727 | 0.7807 | 0.0152 | 0.0147 | 0.9786 | 0.9871 | 0.8273 | 0.7807 | 0.8273 | 0.7718 | 0.8289 |
| 0.0014 | 28.0 | 18004 | 1.8354 | 0.8265 | 0.8337 | 0.8265 | 0.7624 | 0.7806 | 0.0154 | 0.0148 | 0.9784 | 0.9870 | 0.8265 | 0.7806 | 0.8265 | 0.7648 | 0.8282 |
| 0.0004 | 29.0 | 18647 | 1.8558 | 0.8234 | 0.8277 | 0.8234 | 0.7616 | 0.7801 | 0.0157 | 0.0151 | 0.9778 | 0.9867 | 0.8234 | 0.7801 | 0.8234 | 0.7646 | 0.8234 |
| 0.0012 | 30.0 | 19290 | 1.8392 | 0.8234 | 0.8281 | 0.8234 | 0.7608 | 0.7799 | 0.0157 | 0.0151 | 0.9781 | 0.9868 | 0.8234 | 0.7799 | 0.8234 | 0.7642 | 0.8237 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1
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