metadata

license: mit
base_model: xlm-roberta-base
tags:
  - generated_from_trainer
metrics:
  - f1
  - accuracy
model-index:
  - name: xlmroberta-finetuned-sem_eval-rest14-english
    results: []

xlmroberta-finetuned-sem_eval-rest14-english

This model is a fine-tuned version of xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0880
F1: 0.4875
Roc Auc: 0.8755
Accuracy: 0.7087
Hamming Loss: 0.0254

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: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Roc Auc	Accuracy	Hamming Loss
No log	1.0	381	0.1858	0.0351	0.6366	0.2288	0.0609
0.2234	2.0	762	0.1354	0.1345	0.6905	0.3325	0.0418
0.1455	3.0	1143	0.1091	0.2639	0.7709	0.5138	0.0343
0.1064	4.0	1524	0.0971	0.3768	0.8313	0.6212	0.0282
0.1064	5.0	1905	0.0917	0.3767	0.8316	0.6312	0.0286
0.0807	6.0	2286	0.0880	0.3966	0.8459	0.65	0.0274
0.0657	7.0	2667	0.0922	0.3977	0.8513	0.6525	0.0283
0.0527	8.0	3048	0.0903	0.4129	0.8555	0.6575	0.028
0.0527	9.0	3429	0.0921	0.4298	0.8611	0.6713	0.0278
0.0453	10.0	3810	0.0882	0.4612	0.8697	0.6887	0.0261
0.0385	11.0	4191	0.0859	0.4567	0.8701	0.6925	0.0253
0.0339	12.0	4572	0.0881	0.4600	0.8726	0.7025	0.0258
0.0339	13.0	4953	0.0882	0.4818	0.8775	0.7063	0.0252
0.0304	14.0	5334	0.0879	0.4793	0.8742	0.7	0.0254
0.0271	15.0	5715	0.0880	0.4875	0.8755	0.7087	0.0254

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.1