---
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: scenario-KD-PR-MSV-D2_data-cl-cardiff_cl_only_gamma-jason
  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. -->

# scenario-KD-PR-MSV-D2_data-cl-cardiff_cl_only_gamma-jason

This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 17.2063
- Accuracy: 0.3889
- F1: 0.3875

## 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: 32
- eval_batch_size: 32
- seed: 88458
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.09  | 250  | 12.0692         | 0.3727   | 0.3489 |
| 14.0169       | 2.17  | 500  | 10.9338         | 0.3819   | 0.3797 |
| 14.0169       | 3.26  | 750  | 10.7424         | 0.4059   | 0.4032 |
| 11.6579       | 4.35  | 1000 | 11.5422         | 0.3927   | 0.3806 |
| 11.6579       | 5.43  | 1250 | 11.1316         | 0.4012   | 0.4005 |
| 9.9619        | 6.52  | 1500 | 11.8448         | 0.4105   | 0.4082 |
| 9.9619        | 7.61  | 1750 | 12.5910         | 0.4174   | 0.4151 |
| 8.6208        | 8.7   | 2000 | 11.4658         | 0.3989   | 0.3971 |
| 8.6208        | 9.78  | 2250 | 12.6395         | 0.4066   | 0.4067 |
| 7.4098        | 10.87 | 2500 | 12.9836         | 0.4035   | 0.3977 |
| 7.4098        | 11.96 | 2750 | 13.6894         | 0.3966   | 0.3929 |
| 6.5302        | 13.04 | 3000 | 15.4482         | 0.3997   | 0.3936 |
| 6.5302        | 14.13 | 3250 | 14.9618         | 0.3688   | 0.3596 |
| 5.6406        | 15.22 | 3500 | 14.4216         | 0.3943   | 0.3905 |
| 5.6406        | 16.3  | 3750 | 15.1277         | 0.4035   | 0.4023 |
| 5.0685        | 17.39 | 4000 | 15.6837         | 0.3920   | 0.3883 |
| 5.0685        | 18.48 | 4250 | 16.1391         | 0.4020   | 0.4015 |
| 4.3243        | 19.57 | 4500 | 16.0750         | 0.3773   | 0.3744 |
| 4.3243        | 20.65 | 4750 | 16.0997         | 0.3989   | 0.3951 |
| 4.0088        | 21.74 | 5000 | 16.2234         | 0.3966   | 0.3907 |
| 4.0088        | 22.83 | 5250 | 16.6659         | 0.3850   | 0.3833 |
| 3.5813        | 23.91 | 5500 | 16.1725         | 0.4136   | 0.4133 |
| 3.5813        | 25.0  | 5750 | 16.7571         | 0.3873   | 0.3850 |
| 3.2165        | 26.09 | 6000 | 17.0675         | 0.3951   | 0.3943 |
| 3.2165        | 27.17 | 6250 | 16.7777         | 0.3951   | 0.3934 |
| 3.0739        | 28.26 | 6500 | 17.1236         | 0.4043   | 0.4036 |
| 3.0739        | 29.35 | 6750 | 17.2063         | 0.3889   | 0.3875 |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3