---
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