---
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_alpha-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_alpha-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: 16.2447
- Accuracy: 0.3866
- F1: 0.3858

## 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: 2222
- 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  | 12.0259         | 0.3449   | 0.3171 |
| 14.0331       | 2.17  | 500  | 11.3284         | 0.3819   | 0.3694 |
| 14.0331       | 3.26  | 750  | 11.1163         | 0.3951   | 0.3941 |
| 11.7619       | 4.35  | 1000 | 11.5284         | 0.3796   | 0.3733 |
| 11.7619       | 5.43  | 1250 | 11.3713         | 0.4174   | 0.4154 |
| 9.9697        | 6.52  | 1500 | 11.7460         | 0.3850   | 0.3770 |
| 9.9697        | 7.61  | 1750 | 12.6216         | 0.3927   | 0.3863 |
| 8.7178        | 8.7   | 2000 | 12.5277         | 0.4020   | 0.4005 |
| 8.7178        | 9.78  | 2250 | 11.8300         | 0.3912   | 0.3911 |
| 7.7259        | 10.87 | 2500 | 12.7404         | 0.4051   | 0.4035 |
| 7.7259        | 11.96 | 2750 | 13.6012         | 0.4051   | 0.4037 |
| 6.6383        | 13.04 | 3000 | 14.1112         | 0.3912   | 0.3884 |
| 6.6383        | 14.13 | 3250 | 14.0430         | 0.3920   | 0.3881 |
| 5.7088        | 15.22 | 3500 | 13.9183         | 0.3966   | 0.3951 |
| 5.7088        | 16.3  | 3750 | 14.5237         | 0.3904   | 0.3858 |
| 5.1104        | 17.39 | 4000 | 15.0371         | 0.4012   | 0.4011 |
| 5.1104        | 18.48 | 4250 | 15.4539         | 0.3866   | 0.3814 |
| 4.587         | 19.57 | 4500 | 14.4770         | 0.3989   | 0.3982 |
| 4.587         | 20.65 | 4750 | 15.9417         | 0.4136   | 0.4103 |
| 4.1118        | 21.74 | 5000 | 15.0406         | 0.3966   | 0.3966 |
| 4.1118        | 22.83 | 5250 | 16.1274         | 0.4020   | 0.4016 |
| 3.7338        | 23.91 | 5500 | 15.8530         | 0.3858   | 0.3835 |
| 3.7338        | 25.0  | 5750 | 16.3221         | 0.4090   | 0.4074 |
| 3.4628        | 26.09 | 6000 | 16.5572         | 0.4028   | 0.4017 |
| 3.4628        | 27.17 | 6250 | 16.4879         | 0.3881   | 0.3868 |
| 3.3012        | 28.26 | 6500 | 16.4834         | 0.3997   | 0.3995 |
| 3.3012        | 29.35 | 6750 | 16.2447         | 0.3866   | 0.3858 |


### Framework versions

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