---
library_name: transformers
license: mit
base_model: FacebookAI/roberta-large
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: roberta-large-finetuned-augmentation-LUNAR
  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. -->

# roberta-large-finetuned-augmentation-LUNAR

This model is a fine-tuned version of [FacebookAI/roberta-large](https://huggingface.co/FacebookAI/roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6061
- F1: 0.7909
- Roc Auc: 0.8390
- Accuracy: 0.5680

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.4606        | 1.0   | 179  | 0.3928          | 0.5956 | 0.7155  | 0.4320   |
| 0.3171        | 2.0   | 358  | 0.3380          | 0.7156 | 0.7768  | 0.4727   |
| 0.2294        | 3.0   | 537  | 0.3398          | 0.7321 | 0.7927  | 0.5077   |
| 0.1528        | 4.0   | 716  | 0.3813          | 0.7577 | 0.8113  | 0.5175   |
| 0.0887        | 5.0   | 895  | 0.4250          | 0.7669 | 0.8306  | 0.5175   |
| 0.0583        | 6.0   | 1074 | 0.4355          | 0.7686 | 0.8278  | 0.5273   |
| 0.0448        | 7.0   | 1253 | 0.5045          | 0.7498 | 0.8029  | 0.5316   |
| 0.0298        | 8.0   | 1432 | 0.4862          | 0.7809 | 0.8321  | 0.5554   |
| 0.0227        | 9.0   | 1611 | 0.5282          | 0.7793 | 0.8248  | 0.5484   |
| 0.0111        | 10.0  | 1790 | 0.5567          | 0.7787 | 0.8340  | 0.5428   |
| 0.0082        | 11.0  | 1969 | 0.5762          | 0.7845 | 0.8408  | 0.5498   |
| 0.0055        | 12.0  | 2148 | 0.5771          | 0.7796 | 0.8325  | 0.5582   |
| 0.0032        | 13.0  | 2327 | 0.5884          | 0.7865 | 0.8336  | 0.5610   |
| 0.003         | 14.0  | 2506 | 0.6064          | 0.7901 | 0.8380  | 0.5568   |
| 0.0024        | 15.0  | 2685 | 0.6061          | 0.7909 | 0.8390  | 0.5680   |
| 0.002         | 16.0  | 2864 | 0.6041          | 0.7878 | 0.8399  | 0.5736   |
| 0.0016        | 17.0  | 3043 | 0.6129          | 0.7848 | 0.8346  | 0.5596   |
| 0.0014        | 18.0  | 3222 | 0.6129          | 0.7860 | 0.8366  | 0.5694   |
| 0.0038        | 19.0  | 3401 | 0.6143          | 0.7893 | 0.8400  | 0.5722   |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0