---
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