---
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: 6.7853
- Accuracy: 0.3580
- F1: 0.3378

## 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  | 1.1518          | 0.3495   | 0.3449 |
| 1.0509        | 2.17  | 500  | 1.5848          | 0.3665   | 0.3279 |
| 1.0509        | 3.26  | 750  | 2.0537          | 0.3673   | 0.3351 |
| 0.4971        | 4.35  | 1000 | 2.6254          | 0.3642   | 0.3389 |
| 0.4971        | 5.43  | 1250 | 3.3984          | 0.3495   | 0.3056 |
| 0.189         | 6.52  | 1500 | 3.9545          | 0.3588   | 0.3213 |
| 0.189         | 7.61  | 1750 | 4.3147          | 0.3634   | 0.3250 |
| 0.0832        | 8.7   | 2000 | 4.5326          | 0.3495   | 0.3223 |
| 0.0832        | 9.78  | 2250 | 4.8999          | 0.3627   | 0.3396 |
| 0.0407        | 10.87 | 2500 | 5.2749          | 0.3503   | 0.3354 |
| 0.0407        | 11.96 | 2750 | 5.2814          | 0.3634   | 0.3500 |
| 0.0279        | 13.04 | 3000 | 5.3923          | 0.3657   | 0.3502 |
| 0.0279        | 14.13 | 3250 | 5.7450          | 0.3565   | 0.3397 |
| 0.0153        | 15.22 | 3500 | 5.6113          | 0.3681   | 0.3582 |
| 0.0153        | 16.3  | 3750 | 5.1689          | 0.3704   | 0.3615 |
| 0.0145        | 17.39 | 4000 | 5.8264          | 0.3650   | 0.3579 |
| 0.0145        | 18.48 | 4250 | 5.6710          | 0.3650   | 0.3603 |
| 0.0092        | 19.57 | 4500 | 6.0070          | 0.3650   | 0.3547 |
| 0.0092        | 20.65 | 4750 | 6.2579          | 0.3519   | 0.3287 |
| 0.0034        | 21.74 | 5000 | 6.3540          | 0.3650   | 0.3550 |
| 0.0034        | 22.83 | 5250 | 6.4666          | 0.3619   | 0.3438 |
| 0.0031        | 23.91 | 5500 | 6.6982          | 0.3580   | 0.3286 |
| 0.0031        | 25.0  | 5750 | 6.6139          | 0.3657   | 0.3611 |
| 0.0016        | 26.09 | 6000 | 6.6320          | 0.3688   | 0.3632 |
| 0.0016        | 27.17 | 6250 | 6.7619          | 0.3673   | 0.3544 |
| 0.0015        | 28.26 | 6500 | 6.7368          | 0.3627   | 0.3426 |
| 0.0015        | 29.35 | 6750 | 6.7853          | 0.3580   | 0.3378 |


### Framework versions

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