---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: scenario-NON-KD-SCR-COPY-CDF-CL-D2_data-cl-cardiff_cl_only_alpha
  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-NON-KD-SCR-COPY-CDF-CL-D2_data-cl-cardiff_cl_only_alpha

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.4834
- Accuracy: 0.3758
- F1: 0.3692

## 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: 1123
- 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  | 1.1269          | 0.3302   | 0.2264 |
| 1.0552        | 2.17  | 500  | 1.4862          | 0.3673   | 0.3240 |
| 1.0552        | 3.26  | 750  | 2.0425          | 0.3580   | 0.3105 |
| 0.4849        | 4.35  | 1000 | 2.7827          | 0.3673   | 0.3562 |
| 0.4849        | 5.43  | 1250 | 2.3787          | 0.3804   | 0.3790 |
| 0.1731        | 6.52  | 1500 | 3.5404          | 0.3873   | 0.3523 |
| 0.1731        | 7.61  | 1750 | 4.1335          | 0.3765   | 0.3561 |
| 0.0878        | 8.7   | 2000 | 4.4114          | 0.375    | 0.3748 |
| 0.0878        | 9.78  | 2250 | 4.9951          | 0.3650   | 0.3587 |
| 0.036         | 10.87 | 2500 | 5.1386          | 0.3642   | 0.3569 |
| 0.036         | 11.96 | 2750 | 5.4524          | 0.3711   | 0.3499 |
| 0.0297        | 13.04 | 3000 | 5.4964          | 0.3681   | 0.3565 |
| 0.0297        | 14.13 | 3250 | 5.3291          | 0.3765   | 0.3709 |
| 0.0225        | 15.22 | 3500 | 5.6184          | 0.3719   | 0.3346 |
| 0.0225        | 16.3  | 3750 | 5.4079          | 0.3719   | 0.3466 |
| 0.0144        | 17.39 | 4000 | 5.6999          | 0.3634   | 0.3538 |
| 0.0144        | 18.48 | 4250 | 5.7290          | 0.3681   | 0.3575 |
| 0.007         | 19.57 | 4500 | 5.9349          | 0.3673   | 0.3272 |
| 0.007         | 20.65 | 4750 | 5.7265          | 0.3789   | 0.3760 |
| 0.0064        | 21.74 | 5000 | 6.1464          | 0.3611   | 0.3370 |
| 0.0064        | 22.83 | 5250 | 6.1483          | 0.3765   | 0.3678 |
| 0.0026        | 23.91 | 5500 | 6.3733          | 0.3742   | 0.3688 |
| 0.0026        | 25.0  | 5750 | 6.3795          | 0.3704   | 0.3498 |
| 0.002         | 26.09 | 6000 | 6.4692          | 0.3735   | 0.3582 |
| 0.002         | 27.17 | 6250 | 6.5994          | 0.3727   | 0.3448 |
| 0.0006        | 28.26 | 6500 | 6.4658          | 0.3758   | 0.3693 |
| 0.0006        | 29.35 | 6750 | 6.4834          | 0.3758   | 0.3692 |


### Framework versions

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