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

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: 5.4088
- Accuracy: 0.4452
- F1: 0.4432

## 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: 11423
- 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  | 1.1861          | 0.4599   | 0.4461 |
| 0.8893        | 2.17  | 500  | 1.2483          | 0.4753   | 0.4682 |
| 0.8893        | 3.26  | 750  | 1.4640          | 0.4877   | 0.4872 |
| 0.5435        | 4.35  | 1000 | 1.9901          | 0.4529   | 0.4440 |
| 0.5435        | 5.43  | 1250 | 2.1858          | 0.4398   | 0.4357 |
| 0.2767        | 6.52  | 1500 | 2.2484          | 0.4653   | 0.4643 |
| 0.2767        | 7.61  | 1750 | 2.7287          | 0.4653   | 0.4642 |
| 0.1584        | 8.7   | 2000 | 2.7996          | 0.4637   | 0.4616 |
| 0.1584        | 9.78  | 2250 | 3.2599          | 0.4684   | 0.4684 |
| 0.1119        | 10.87 | 2500 | 3.7690          | 0.4344   | 0.4244 |
| 0.1119        | 11.96 | 2750 | 3.5578          | 0.4591   | 0.4584 |
| 0.0771        | 13.04 | 3000 | 3.9089          | 0.4483   | 0.4490 |
| 0.0771        | 14.13 | 3250 | 4.1349          | 0.4637   | 0.4587 |
| 0.054         | 15.22 | 3500 | 4.4418          | 0.4506   | 0.4435 |
| 0.054         | 16.3  | 3750 | 4.4987          | 0.4522   | 0.4511 |
| 0.04          | 17.39 | 4000 | 4.5234          | 0.4514   | 0.4511 |
| 0.04          | 18.48 | 4250 | 4.7455          | 0.4529   | 0.4517 |
| 0.0241        | 19.57 | 4500 | 5.0606          | 0.4329   | 0.4238 |
| 0.0241        | 20.65 | 4750 | 5.0820          | 0.4414   | 0.4394 |
| 0.0243        | 21.74 | 5000 | 5.2753          | 0.4360   | 0.4304 |
| 0.0243        | 22.83 | 5250 | 5.1224          | 0.4660   | 0.4666 |
| 0.0155        | 23.91 | 5500 | 5.2712          | 0.4437   | 0.4407 |
| 0.0155        | 25.0  | 5750 | 5.3846          | 0.4421   | 0.4393 |
| 0.0156        | 26.09 | 6000 | 5.4060          | 0.4398   | 0.4352 |
| 0.0156        | 27.17 | 6250 | 5.3914          | 0.4383   | 0.4344 |
| 0.0105        | 28.26 | 6500 | 5.3427          | 0.4421   | 0.4413 |
| 0.0105        | 29.35 | 6750 | 5.4088          | 0.4452   | 0.4432 |


### Framework versions

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