---
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_delta-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_delta-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: 17.1445
- Accuracy: 0.4005
- F1: 0.3981

## 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: 7777
- 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  | 11.9607         | 0.3603   | 0.3165 |
| 13.9882       | 2.17  | 500  | 11.5568         | 0.375    | 0.3692 |
| 13.9882       | 3.26  | 750  | 11.3711         | 0.4090   | 0.4091 |
| 11.3923       | 4.35  | 1000 | 11.5830         | 0.3974   | 0.3972 |
| 11.3923       | 5.43  | 1250 | 11.6052         | 0.4074   | 0.4019 |
| 9.8435        | 6.52  | 1500 | 11.6147         | 0.4128   | 0.4100 |
| 9.8435        | 7.61  | 1750 | 12.7909         | 0.3966   | 0.3837 |
| 8.6495        | 8.7   | 2000 | 11.8204         | 0.4128   | 0.4046 |
| 8.6495        | 9.78  | 2250 | 12.4905         | 0.3889   | 0.3824 |
| 7.3906        | 10.87 | 2500 | 13.3672         | 0.4113   | 0.4097 |
| 7.3906        | 11.96 | 2750 | 15.0103         | 0.4020   | 0.3925 |
| 6.4797        | 13.04 | 3000 | 14.2666         | 0.3789   | 0.3791 |
| 6.4797        | 14.13 | 3250 | 14.9095         | 0.3904   | 0.3825 |
| 5.5862        | 15.22 | 3500 | 14.1357         | 0.4028   | 0.3995 |
| 5.5862        | 16.3  | 3750 | 15.0102         | 0.4005   | 0.3997 |
| 5.0029        | 17.39 | 4000 | 15.1376         | 0.4051   | 0.4035 |
| 5.0029        | 18.48 | 4250 | 15.2697         | 0.3958   | 0.3935 |
| 4.4091        | 19.57 | 4500 | 15.8735         | 0.3935   | 0.3905 |
| 4.4091        | 20.65 | 4750 | 15.5799         | 0.4028   | 0.4009 |
| 3.9734        | 21.74 | 5000 | 16.0068         | 0.4151   | 0.4137 |
| 3.9734        | 22.83 | 5250 | 15.9701         | 0.3796   | 0.3797 |
| 3.569         | 23.91 | 5500 | 16.0636         | 0.3850   | 0.3783 |
| 3.569         | 25.0  | 5750 | 16.2960         | 0.3904   | 0.3878 |
| 3.2517        | 26.09 | 6000 | 16.5689         | 0.3897   | 0.3850 |
| 3.2517        | 27.17 | 6250 | 17.1440         | 0.3858   | 0.3843 |
| 3.1047        | 28.26 | 6500 | 17.0026         | 0.4074   | 0.4028 |
| 3.1047        | 29.35 | 6750 | 17.1445         | 0.4005   | 0.3981 |


### Framework versions

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