---
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_delta
  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_delta

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: 5.4388
- Accuracy: 0.4390
- F1: 0.4380

## 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: 11213
- 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  | 1.1982          | 0.4576   | 0.4521 |
| 0.9123        | 2.17  | 500  | 1.4363          | 0.4722   | 0.4650 |
| 0.9123        | 3.26  | 750  | 1.5173          | 0.4715   | 0.4672 |
| 0.5863        | 4.35  | 1000 | 1.8168          | 0.4545   | 0.4539 |
| 0.5863        | 5.43  | 1250 | 2.0339          | 0.4645   | 0.4643 |
| 0.3146        | 6.52  | 1500 | 2.0526          | 0.4753   | 0.4739 |
| 0.3146        | 7.61  | 1750 | 2.5574          | 0.4560   | 0.4543 |
| 0.1799        | 8.7   | 2000 | 2.7053          | 0.4537   | 0.4542 |
| 0.1799        | 9.78  | 2250 | 3.2816          | 0.4468   | 0.4462 |
| 0.1166        | 10.87 | 2500 | 3.5971          | 0.4414   | 0.4418 |
| 0.1166        | 11.96 | 2750 | 3.5830          | 0.4491   | 0.4477 |
| 0.0894        | 13.04 | 3000 | 3.7770          | 0.4537   | 0.4537 |
| 0.0894        | 14.13 | 3250 | 4.0171          | 0.4475   | 0.4464 |
| 0.0625        | 15.22 | 3500 | 4.3230          | 0.4383   | 0.4350 |
| 0.0625        | 16.3  | 3750 | 4.4061          | 0.4421   | 0.4402 |
| 0.0419        | 17.39 | 4000 | 4.5390          | 0.4468   | 0.4460 |
| 0.0419        | 18.48 | 4250 | 4.7343          | 0.4452   | 0.4445 |
| 0.0328        | 19.57 | 4500 | 4.5586          | 0.4514   | 0.4527 |
| 0.0328        | 20.65 | 4750 | 4.9107          | 0.4437   | 0.4424 |
| 0.0225        | 21.74 | 5000 | 5.1509          | 0.4313   | 0.4276 |
| 0.0225        | 22.83 | 5250 | 4.8634          | 0.4444   | 0.4436 |
| 0.0209        | 23.91 | 5500 | 5.1513          | 0.4352   | 0.4318 |
| 0.0209        | 25.0  | 5750 | 5.0801          | 0.4552   | 0.4555 |
| 0.0117        | 26.09 | 6000 | 5.2642          | 0.4468   | 0.4444 |
| 0.0117        | 27.17 | 6250 | 5.3801          | 0.4367   | 0.4342 |
| 0.0092        | 28.26 | 6500 | 5.4445          | 0.4367   | 0.4343 |
| 0.0092        | 29.35 | 6750 | 5.4388          | 0.4390   | 0.4380 |


### Framework versions

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