---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
base_model: roberta-base
model-index:
- name: source-role-model
  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. -->

# source-role-model

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: 2.5543
- F1: 0.5814

## 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: 5e-05
- train_batch_size: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 0.12  | 100  | 1.0000          | 0.3391 |
| No log        | 0.25  | 200  | 0.8371          | 0.5055 |
| No log        | 0.37  | 300  | 0.8684          | 0.5019 |
| No log        | 0.49  | 400  | 0.8668          | 0.5208 |
| 0.9644        | 0.62  | 500  | 0.8473          | 0.5422 |
| 0.9644        | 0.74  | 600  | 0.8852          | 0.4956 |
| 0.9644        | 0.86  | 700  | 0.8368          | 0.5124 |
| 0.9644        | 0.99  | 800  | 0.7913          | 0.5848 |
| 0.9644        | 1.11  | 900  | 1.0570          | 0.4950 |
| 0.8375        | 1.23  | 1000 | 0.9402          | 0.5280 |
| 0.8375        | 1.35  | 1100 | 0.8023          | 0.5084 |
| 0.8375        | 1.48  | 1200 | 0.9299          | 0.4807 |
| 0.8375        | 1.6   | 1300 | 0.9661          | 0.5194 |
| 0.8375        | 1.72  | 1400 | 0.8014          | 0.6016 |
| 0.8149        | 1.85  | 1500 | 0.8608          | 0.6105 |
| 0.8149        | 1.97  | 1600 | 0.9195          | 0.5741 |
| 0.8149        | 2.09  | 1700 | 1.2378          | 0.5964 |
| 0.8149        | 2.22  | 1800 | 1.0415          | 0.5902 |
| 0.8149        | 2.34  | 1900 | 1.0499          | 0.5526 |
| 0.6932        | 2.46  | 2000 | 1.0600          | 0.5832 |
| 0.6932        | 2.59  | 2100 | 0.9368          | 0.6074 |
| 0.6932        | 2.71  | 2200 | 1.0872          | 0.6270 |
| 0.6932        | 2.83  | 2300 | 1.0912          | 0.5707 |
| 0.6932        | 2.96  | 2400 | 0.8815          | 0.5602 |
| 0.6214        | 3.08  | 2500 | 1.1650          | 0.5993 |
| 0.6214        | 3.2   | 2600 | 1.4485          | 0.5821 |
| 0.6214        | 3.33  | 2700 | 1.5382          | 0.5775 |
| 0.6214        | 3.45  | 2800 | 1.3999          | 0.5696 |
| 0.6214        | 3.57  | 2900 | 1.3702          | 0.6114 |
| 0.5686        | 3.69  | 3000 | 1.3840          | 0.5635 |
| 0.5686        | 3.82  | 3100 | 1.3547          | 0.5403 |
| 0.5686        | 3.94  | 3200 | 1.0283          | 0.5723 |
| 0.5686        | 4.06  | 3300 | 1.3593          | 0.6242 |
| 0.5686        | 4.19  | 3400 | 1.5985          | 0.6004 |
| 0.4807        | 4.31  | 3500 | 1.5351          | 0.6177 |
| 0.4807        | 4.43  | 3600 | 1.4109          | 0.5779 |
| 0.4807        | 4.56  | 3700 | 1.6972          | 0.5637 |
| 0.4807        | 4.68  | 3800 | 1.5336          | 0.6047 |
| 0.4807        | 4.8   | 3900 | 1.7811          | 0.5909 |
| 0.4387        | 4.93  | 4000 | 1.5862          | 0.5869 |
| 0.4387        | 5.05  | 4100 | 1.7106          | 0.5637 |
| 0.4387        | 5.17  | 4200 | 1.5251          | 0.5624 |
| 0.4387        | 5.3   | 4300 | 1.5519          | 0.5944 |
| 0.4387        | 5.42  | 4400 | 1.7315          | 0.5908 |
| 0.3219        | 5.54  | 4500 | 1.7588          | 0.6015 |
| 0.3219        | 5.67  | 4600 | 1.9277          | 0.5635 |
| 0.3219        | 5.79  | 4700 | 1.7663          | 0.5891 |
| 0.3219        | 5.91  | 4800 | 1.8401          | 0.5917 |
| 0.3219        | 6.03  | 4900 | 2.0516          | 0.5845 |
| 0.2311        | 6.16  | 5000 | 2.0510          | 0.6166 |
| 0.2311        | 6.28  | 5100 | 2.1673          | 0.5732 |
| 0.2311        | 6.4   | 5200 | 2.0931          | 0.5819 |
| 0.2311        | 6.53  | 5300 | 2.2803          | 0.5961 |
| 0.2311        | 6.65  | 5400 | 1.9985          | 0.6010 |
| 0.1669        | 6.77  | 5500 | 2.1742          | 0.5664 |
| 0.1669        | 6.9   | 5600 | 2.1021          | 0.5732 |
| 0.1669        | 7.02  | 5700 | 2.2043          | 0.5641 |
| 0.1669        | 7.14  | 5800 | 2.2018          | 0.5837 |
| 0.1669        | 7.27  | 5900 | 2.3575          | 0.5721 |
| 0.1698        | 7.39  | 6000 | 2.4663          | 0.5662 |
| 0.1698        | 7.51  | 6100 | 2.2658          | 0.5851 |
| 0.1698        | 7.64  | 6200 | 2.1585          | 0.5676 |
| 0.1698        | 7.76  | 6300 | 2.1755          | 0.5774 |
| 0.1698        | 7.88  | 6400 | 2.2680          | 0.5696 |
| 0.1378        | 8.0   | 6500 | 2.3505          | 0.5615 |
| 0.1378        | 8.13  | 6600 | 2.2773          | 0.5705 |
| 0.1378        | 8.25  | 6700 | 2.3112          | 0.5662 |
| 0.1378        | 8.37  | 6800 | 2.4572          | 0.5679 |
| 0.1378        | 8.5   | 6900 | 2.4642          | 0.5766 |
| 0.0756        | 8.62  | 7000 | 2.4643          | 0.5885 |
| 0.0756        | 8.74  | 7100 | 2.5096          | 0.5779 |
| 0.0756        | 8.87  | 7200 | 2.4261          | 0.5789 |
| 0.0756        | 8.99  | 7300 | 2.3973          | 0.5757 |
| 0.0756        | 9.11  | 7400 | 2.4137          | 0.5906 |
| 0.0842        | 9.24  | 7500 | 2.4577          | 0.5844 |
| 0.0842        | 9.36  | 7600 | 2.5034          | 0.5840 |
| 0.0842        | 9.48  | 7700 | 2.5176          | 0.5810 |
| 0.0842        | 9.61  | 7800 | 2.5240          | 0.5852 |
| 0.0842        | 9.73  | 7900 | 2.5141          | 0.5824 |
| 0.0634        | 9.85  | 8000 | 2.5482          | 0.5814 |
| 0.0634        | 9.98  | 8100 | 2.5543          | 0.5814 |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3