---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: training-1
  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. -->

# training-1

This model is a fine-tuned version of [roberta-base](https://huggingface.co./roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0448
- Accuracy: 0.9937
- Precision: 0.9912
- Recall: 0.9859
- F1: 0.9885

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 0.5   | 302  | 0.0546          | 0.9870   | 0.9737    | 0.9789 | 0.9763 |
| No log        | 1.0   | 604  | 0.0511          | 0.9913   | 0.9911    | 0.9771 | 0.9840 |
| 0.1032        | 1.5   | 906  | 0.0558          | 0.9899   | 0.9807    | 0.9824 | 0.9815 |
| 0.1032        | 2.0   | 1208 | 0.0467          | 0.9928   | 0.9982    | 0.9754 | 0.9866 |
| 0.0353        | 2.5   | 1510 | 0.0411          | 0.9937   | 0.9929    | 0.9842 | 0.9885 |
| 0.0353        | 3.0   | 1812 | 0.0460          | 0.9932   | 0.9911    | 0.9842 | 0.9876 |
| 0.0183        | 3.49  | 2114 | 0.0423          | 0.9937   | 0.9947    | 0.9824 | 0.9885 |
| 0.0183        | 3.99  | 2416 | 0.0476          | 0.9932   | 0.9911    | 0.9842 | 0.9876 |
| 0.013         | 4.49  | 2718 | 0.0463          | 0.9932   | 0.9911    | 0.9842 | 0.9876 |
| 0.013         | 4.99  | 3020 | 0.0448          | 0.9937   | 0.9912    | 0.9859 | 0.9885 |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.2.0.dev20230913+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3