---
license: mit
base_model: FacebookAI/roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: uniBERT.RoBERTa.2
  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. -->

# uniBERT.RoBERTa.2

This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5630
- Accuracy: (0.5576407506702413,)
- F1: (0.5567150966762268,)
- Precision: (0.5821362469913879,)
- Recall: 0.5576

## 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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy               | F1                     | Precision              | Recall |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:----------------------:|:----------------------:|:------:|
| 3.2109        | 1.0   | 187  | 2.8161          | (0.14075067024128687,) | (0.11441602156126289,) | (0.14931859559553662,) | 0.1408 |
| 2.379         | 2.0   | 374  | 2.1989          | (0.29624664879356566,) | (0.29511135501125035,) | (0.46510470134694354,) | 0.2962 |
| 1.8982        | 3.0   | 561  | 1.8944          | (0.39812332439678283,) | (0.3899880572417641,)  | (0.5315801863934072,)  | 0.3981 |
| 1.5421        | 4.0   | 748  | 1.7216          | (0.435656836461126,)   | (0.43699437984272427,) | (0.5225927530578255,)  | 0.4357 |
| 1.2096        | 5.0   | 935  | 1.6234          | (0.4906166219839142,)  | (0.4898871795571693,)  | (0.5614942106807528,)  | 0.4906 |
| 1.0077        | 6.0   | 1122 | 1.5807          | (0.5201072386058981,)  | (0.5180183790949172,)  | (0.5564502396694377,)  | 0.5201 |
| 0.9205        | 7.0   | 1309 | 1.5927          | (0.5308310991957105,)  | (0.5275104307804458,)  | (0.5702694562019299,)  | 0.5308 |
| 0.7537        | 8.0   | 1496 | 1.5717          | (0.532171581769437,)   | (0.5297487624770745,)  | (0.5623053308004577,)  | 0.5322 |
| 0.6635        | 9.0   | 1683 | 1.5720          | (0.5495978552278821,)  | (0.5497874364324945,)  | (0.579798743930685,)   | 0.5496 |
| 0.6479        | 10.0  | 1870 | 1.5630          | (0.5576407506702413,)  | (0.5567150966762268,)  | (0.5821362469913879,)  | 0.5576 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2