---
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-TAPT
  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-TAPT

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: 0.4897
- F1: 0.8302
- Roc Auc: 0.8696
- Accuracy: 0.6338

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.3371        | 1.0   | 317  | 0.3025          | 0.7356 | 0.8000  | 0.5233   |
| 0.2571        | 2.0   | 634  | 0.3055          | 0.7376 | 0.7942  | 0.5572   |
| 0.1848        | 3.0   | 951  | 0.2850          | 0.7964 | 0.8431  | 0.5912   |
| 0.124         | 4.0   | 1268 | 0.3223          | 0.7738 | 0.8164  | 0.5635   |
| 0.0701        | 5.0   | 1585 | 0.3219          | 0.8091 | 0.8597  | 0.5951   |
| 0.0491        | 6.0   | 1902 | 0.3576          | 0.8148 | 0.8547  | 0.6014   |
| 0.0432        | 7.0   | 2219 | 0.3808          | 0.8216 | 0.8665  | 0.6196   |
| 0.0352        | 8.0   | 2536 | 0.3945          | 0.8278 | 0.8721  | 0.6259   |
| 0.0282        | 9.0   | 2853 | 0.4357          | 0.8173 | 0.8580  | 0.6054   |
| 0.012         | 10.0  | 3170 | 0.4670          | 0.8208 | 0.8679  | 0.5951   |
| 0.0054        | 11.0  | 3487 | 0.4864          | 0.8177 | 0.8599  | 0.6038   |
| 0.0029        | 12.0  | 3804 | 0.4882          | 0.8289 | 0.8687  | 0.6259   |
| 0.0011        | 13.0  | 4121 | 0.4897          | 0.8302 | 0.8696  | 0.6338   |
| 0.0012        | 14.0  | 4438 | 0.5079          | 0.8273 | 0.8680  | 0.6251   |
| 0.0008        | 15.0  | 4755 | 0.5146          | 0.8285 | 0.8688  | 0.6227   |
| 0.0007        | 16.0  | 5072 | 0.5100          | 0.8282 | 0.8693  | 0.6338   |
| 0.0008        | 17.0  | 5389 | 0.5158          | 0.8282 | 0.8673  | 0.6330   |


### Framework versions

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