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---
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: results1
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. -->
# results1
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0207
- Accuracy: 0.9960
- Precision: 0.9960
- Recall: 0.9960
- F1: 0.9960
- Roc Auc: 0.9998
## 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: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.0672 | 0.2202 | 500 | 0.0532 | 0.9832 | 0.9833 | 0.9832 | 0.9832 | 0.9985 |
| 0.0369 | 0.4403 | 1000 | 0.0380 | 0.9886 | 0.9886 | 0.9886 | 0.9886 | 0.9992 |
| 0.0347 | 0.6605 | 1500 | 0.0298 | 0.9910 | 0.9910 | 0.9910 | 0.9910 | 0.9995 |
| 0.0382 | 0.8807 | 2000 | 0.0265 | 0.9922 | 0.9922 | 0.9922 | 0.9922 | 0.9995 |
| 0.0209 | 1.1008 | 2500 | 0.0228 | 0.9942 | 0.9942 | 0.9942 | 0.9942 | 0.9997 |
| 0.0558 | 1.3210 | 3000 | 0.0245 | 0.9947 | 0.9947 | 0.9947 | 0.9947 | 0.9997 |
| 0.0184 | 1.5412 | 3500 | 0.0299 | 0.9931 | 0.9932 | 0.9931 | 0.9931 | 0.9997 |
| 0.0021 | 1.7613 | 4000 | 0.0215 | 0.9949 | 0.9949 | 0.9949 | 0.9949 | 0.9998 |
| 0.0296 | 1.9815 | 4500 | 0.0250 | 0.9936 | 0.9936 | 0.9936 | 0.9936 | 0.9998 |
| 0.0012 | 2.2017 | 5000 | 0.0211 | 0.9955 | 0.9955 | 0.9955 | 0.9955 | 0.9998 |
| 0.0078 | 2.4218 | 5500 | 0.0212 | 0.9961 | 0.9961 | 0.9961 | 0.9961 | 0.9998 |
| 0.0009 | 2.6420 | 6000 | 0.0239 | 0.9952 | 0.9952 | 0.9952 | 0.9952 | 0.9998 |
| 0.0105 | 2.8622 | 6500 | 0.0209 | 0.9956 | 0.9956 | 0.9956 | 0.9956 | 0.9998 |
### Framework versions
- Transformers 4.53.3
- Pytorch 2.6.0+cu124
- Datasets 4.4.1
- Tokenizers 0.21.2
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