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

# bryan-NM

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: 2.6055
- Accuracy: 0.5427
- F1: 0.5374
- Precision: 0.5365
- Recall: 0.5427

## 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: 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: 20

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 2.5019        | 1.0   | 3538  | 2.4996          | 0.3528   | 0.2684 | 0.2489    | 0.3528 |
| 2.1176        | 2.0   | 7076  | 2.1051          | 0.4478   | 0.3790 | 0.3630    | 0.4478 |
| 1.865         | 3.0   | 10614 | 1.9322          | 0.4827   | 0.4286 | 0.4186    | 0.4827 |
| 1.6961        | 4.0   | 14152 | 1.8417          | 0.5057   | 0.4681 | 0.4666    | 0.5057 |
| 1.5709        | 5.0   | 17690 | 1.8285          | 0.5149   | 0.4812 | 0.4826    | 0.5149 |
| 1.3717        | 6.0   | 21228 | 1.8153          | 0.5219   | 0.4938 | 0.5049    | 0.5219 |
| 1.2889        | 7.0   | 24766 | 1.8005          | 0.5369   | 0.5089 | 0.5040    | 0.5369 |
| 1.1258        | 8.0   | 28304 | 1.8269          | 0.5311   | 0.5103 | 0.5090    | 0.5311 |
| 1.0029        | 9.0   | 31842 | 1.8697          | 0.5421   | 0.5274 | 0.5289    | 0.5421 |
| 0.9032        | 10.0  | 35380 | 1.9533          | 0.5393   | 0.5255 | 0.5271    | 0.5393 |
| 0.7787        | 11.0  | 38918 | 2.0320          | 0.5371   | 0.5242 | 0.5245    | 0.5371 |
| 0.7137        | 12.0  | 42456 | 2.0956          | 0.5425   | 0.5331 | 0.5335    | 0.5425 |
| 0.6612        | 13.0  | 45994 | 2.1384          | 0.5419   | 0.5294 | 0.5291    | 0.5419 |
| 0.5733        | 14.0  | 49532 | 2.2058          | 0.5385   | 0.5289 | 0.5273    | 0.5385 |
| 0.5251        | 15.0  | 53070 | 2.2882          | 0.5397   | 0.5304 | 0.5276    | 0.5397 |
| 0.4666        | 16.0  | 56608 | 2.3806          | 0.5393   | 0.5327 | 0.5337    | 0.5393 |
| 0.4345        | 17.0  | 60146 | 2.4534          | 0.5485   | 0.5379 | 0.5366    | 0.5485 |
| 0.3668        | 18.0  | 63684 | 2.5234          | 0.5433   | 0.5368 | 0.5370    | 0.5433 |
| 0.3695        | 19.0  | 67222 | 2.5849          | 0.5417   | 0.5377 | 0.5381    | 0.5417 |
| 0.3226        | 20.0  | 70760 | 2.6055          | 0.5427   | 0.5374 | 0.5365    | 0.5427 |


### Framework versions

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