irony_fr_France

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

• Loss: 0.0028
• Accuracy: 0.7192
• Precision: 0.3910
• Recall: 0.6667
• F1: 0.4929

Model description

The model is trained considering the annotation of annotators from France 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 France from the MultiPICo dataset. The data has been randomly split into 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

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.0044	1.0	72	0.0042	0.7953	0.0	0.0	0.0
0.0042	2.0	144	0.0041	0.8058	0.5417	0.3333	0.4127
0.0041	3.0	216	0.0039	0.5958	0.3	0.7308	0.4254
0.0037	4.0	288	0.0036	0.5486	0.2824	0.7821	0.4150
0.0034	5.0	360	0.0031	0.7192	0.3782	0.5769	0.4569
0.0033	6.0	432	0.0031	0.6352	0.3129	0.6538	0.4232
0.0027	7.0	504	0.0028	0.6089	0.3216	0.8205	0.4621
0.002	8.0	576	0.0028	0.6614	0.3265	0.6154	0.4267
0.0017	9.0	648	0.0023	0.6562	0.3536	0.8205	0.4942
0.0017	10.0	720	0.0024	0.5906	0.3178	0.8718	0.4658
0.0012	11.0	792	0.0028	0.7192	0.3910	0.6667	0.4929

Framework versions

• Transformers 4.34.1
• Pytorch 2.0.1+cu117
• Datasets 2.14.5
• Tokenizers 0.14.1