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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-large-lora-no-grad
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-large-lora-no-grad
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: 0.8724
- Accuracy: 0.8428
- Precision: 0.8414
- Recall: 0.8428
- Precision Macro: 0.8149
- Recall Macro: 0.7856
- Macro Fpr: 0.0144
- Weighted Fpr: 0.0138
- Weighted Specificity: 0.9778
- Macro Specificity: 0.9876
- Weighted Sensitivity: 0.8366
- Macro Sensitivity: 0.7856
- F1 Micro: 0.8366
- F1 Macro: 0.7922
- F1 Weighted: 0.8329
## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:---------------:|:------------:|:---------:|:------------:|:--------------------:|:-----------------:|:--------------------:|:-----------------:|:--------:|:--------:|:-----------:|
| 1.3548 | 1.0 | 643 | 0.7811 | 0.7568 | 0.7272 | 0.7568 | 0.4206 | 0.4734 | 0.0234 | 0.0224 | 0.9682 | 0.9817 | 0.7568 | 0.4734 | 0.7568 | 0.4364 | 0.7359 |
| 0.7738 | 2.0 | 1286 | 0.6572 | 0.7893 | 0.7848 | 0.7893 | 0.6529 | 0.5639 | 0.0196 | 0.0187 | 0.9732 | 0.9842 | 0.7893 | 0.5639 | 0.7893 | 0.5618 | 0.7783 |
| 0.6874 | 3.0 | 1929 | 0.6485 | 0.8009 | 0.7994 | 0.8009 | 0.6224 | 0.6498 | 0.0179 | 0.0174 | 0.9767 | 0.9852 | 0.8009 | 0.6498 | 0.8009 | 0.6248 | 0.7948 |
| 0.502 | 4.0 | 2572 | 0.6912 | 0.8257 | 0.8216 | 0.8257 | 0.7661 | 0.7399 | 0.0158 | 0.0149 | 0.9738 | 0.9866 | 0.8257 | 0.7399 | 0.8257 | 0.7393 | 0.8182 |
| 0.4443 | 5.0 | 3215 | 0.6655 | 0.8350 | 0.8324 | 0.8350 | 0.7584 | 0.7344 | 0.0146 | 0.0139 | 0.9781 | 0.9875 | 0.8350 | 0.7344 | 0.8350 | 0.7352 | 0.8308 |
| 0.3903 | 6.0 | 3858 | 0.7269 | 0.8304 | 0.8288 | 0.8304 | 0.7500 | 0.7407 | 0.0149 | 0.0144 | 0.9789 | 0.9873 | 0.8304 | 0.7407 | 0.8304 | 0.7363 | 0.8261 |
| 0.3398 | 7.0 | 4501 | 0.8292 | 0.8218 | 0.8264 | 0.8218 | 0.8274 | 0.7793 | 0.0161 | 0.0152 | 0.9752 | 0.9865 | 0.8218 | 0.7793 | 0.8218 | 0.7883 | 0.8163 |
| 0.2818 | 8.0 | 5144 | 0.8360 | 0.8218 | 0.8240 | 0.8218 | 0.8251 | 0.7683 | 0.0159 | 0.0152 | 0.9767 | 0.9866 | 0.8218 | 0.7683 | 0.8218 | 0.7744 | 0.8178 |
| 0.2572 | 9.0 | 5787 | 0.8456 | 0.8342 | 0.8328 | 0.8342 | 0.7999 | 0.7735 | 0.0146 | 0.0140 | 0.9787 | 0.9875 | 0.8342 | 0.7735 | 0.8342 | 0.7768 | 0.8310 |
| 0.2594 | 10.0 | 6430 | 0.8724 | 0.8428 | 0.8414 | 0.8428 | 0.8149 | 0.7891 | 0.0138 | 0.0132 | 0.9790 | 0.9881 | 0.8428 | 0.7891 | 0.8428 | 0.7955 | 0.8396 |
| 0.208 | 11.0 | 7073 | 0.9797 | 0.8335 | 0.8339 | 0.8335 | 0.8092 | 0.7870 | 0.0148 | 0.0141 | 0.9774 | 0.9874 | 0.8335 | 0.7870 | 0.8335 | 0.7896 | 0.8303 |
| 0.1786 | 12.0 | 7716 | 1.0180 | 0.8311 | 0.8323 | 0.8311 | 0.8100 | 0.7846 | 0.0149 | 0.0143 | 0.9777 | 0.9873 | 0.8311 | 0.7846 | 0.8311 | 0.7906 | 0.8285 |
| 0.1556 | 13.0 | 8359 | 1.0392 | 0.8358 | 0.8335 | 0.8358 | 0.8040 | 0.7830 | 0.0146 | 0.0138 | 0.9773 | 0.9875 | 0.8358 | 0.7830 | 0.8358 | 0.7876 | 0.8321 |
| 0.1419 | 14.0 | 9002 | 1.0568 | 0.8381 | 0.8362 | 0.8381 | 0.8110 | 0.7855 | 0.0143 | 0.0136 | 0.9779 | 0.9877 | 0.8381 | 0.7855 | 0.8381 | 0.7917 | 0.8349 |
| 0.1251 | 15.0 | 9645 | 1.0593 | 0.8366 | 0.8350 | 0.8366 | 0.8149 | 0.7856 | 0.0144 | 0.0138 | 0.9778 | 0.9876 | 0.8366 | 0.7856 | 0.8366 | 0.7922 | 0.8329 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.19.0
- Tokenizers 0.15.1
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