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---
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
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