---
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: results
  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. -->

# results

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: 0.0528
- Accuracy: 0.9885
- Precision: 0.9885
- Recall: 0.9885
- F1: 0.9885
- Roc Auc: 0.9992

## 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: 32
- eval_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.1227        | 0.2   | 50   | 0.2116          | 0.935    | 0.9392    | 0.935  | 0.9338 | 0.9937  |
| 0.0744        | 0.4   | 100  | 0.0989          | 0.97     | 0.9705    | 0.97   | 0.9698 | 0.9960  |
| 0.0715        | 0.6   | 150  | 0.0651          | 0.982    | 0.9820    | 0.982  | 0.9820 | 0.9977  |
| 0.1218        | 0.8   | 200  | 0.1539          | 0.9555   | 0.9590    | 0.9555 | 0.9559 | 0.9961  |
| 0.0709        | 1.0   | 250  | 0.0528          | 0.9855   | 0.9855    | 0.9855 | 0.9855 | 0.9989  |
| 0.0602        | 1.2   | 300  | 0.0986          | 0.978    | 0.9782    | 0.978  | 0.9779 | 0.9986  |
| 0.034         | 1.4   | 350  | 0.0687          | 0.9835   | 0.9835    | 0.9835 | 0.9835 | 0.9986  |
| 0.0137        | 1.6   | 400  | 0.0613          | 0.9845   | 0.9845    | 0.9845 | 0.9845 | 0.9989  |
| 0.047         | 1.8   | 450  | 0.0472          | 0.9895   | 0.9895    | 0.9895 | 0.9895 | 0.9991  |
| 0.0617        | 2.0   | 500  | 0.0497          | 0.9885   | 0.9885    | 0.9885 | 0.9885 | 0.9991  |
| 0.0513        | 2.2   | 550  | 0.0534          | 0.987    | 0.9870    | 0.987  | 0.9870 | 0.9992  |
| 0.0269        | 2.4   | 600  | 0.0467          | 0.9885   | 0.9885    | 0.9885 | 0.9885 | 0.9993  |
| 0.001         | 2.6   | 650  | 0.0509          | 0.987    | 0.9870    | 0.987  | 0.9870 | 0.9994  |
| 0.0195        | 2.8   | 700  | 0.0521          | 0.9895   | 0.9895    | 0.9895 | 0.9895 | 0.9992  |
| 0.0011        | 3.0   | 750  | 0.0528          | 0.9885   | 0.9885    | 0.9885 | 0.9885 | 0.9992  |


### Framework versions

- Transformers 4.53.3
- Pytorch 2.6.0+cu124
- Datasets 4.4.1
- Tokenizers 0.21.2