The model is trained considering the annotation of annotators from the United Kingdom only. The annotations from these annotators are aggregated using majority voting and then used to train the model.

Training and evaluation data

The model has been trained on the annotation from annotators from the United Kingdom from the EPIC dataset. The data has been randomly split in a train and a validation set.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-06
train_batch_size: 16
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20
early stopping (patience: 2)

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.0044	1.0	79	0.0042	0.5310	0.3612	0.7661	0.4910
0.0043	2.0	158	0.0040	0.6595	0.4417	0.5806	0.5017
0.0039	3.0	237	0.0034	0.6310	0.4188	0.6452	0.5079
0.0033	4.0	316	0.0027	0.7286	0.5352	0.6129	0.5714
0.0022	5.0	395	0.0024	0.5833	0.4066	0.8952	0.5592
0.0015	6.0	474	0.0022	0.7357	0.5474	0.6048	0.5747
0.001	7.0	553	0.0022	0.7262	0.5302	0.6371	0.5788
0.0005	8.0	632	0.0023	0.6833	0.4764	0.7339	0.5778

Framework versions

Transformers 4.34.1
Pytorch 2.0.1+cu117
Datasets 2.14.5
Tokenizers 0.14.1

Multilingual-Perspectivist-NLU/irony_en_United_Kingdom

irony_en_United_Kingdom

Model description

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Multilingual-Perspectivist-NLU/
irony_en_United_Kingdom