distilbert-base-multilingual-shared-task

This model is a fine-tuned version of distilbert/distilbert-base-multilingual-cased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.6391
• Precision: 0.7004
• Recall: 0.7079
• Accuracy: 0.7786
• F1: 0.7039

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: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 286
• num_epochs: 10
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	Accuracy	F1
0.6946	1.0	96	0.6838	0.5609	0.5554	0.6895	0.5573
0.6654	2.0	192	0.6342	0.6254	0.6664	0.5798	0.5663
0.5334	3.0	288	0.5752	0.6798	0.7006	0.7571	0.6878
0.4491	4.0	384	0.6391	0.7004	0.7079	0.7786	0.7039
0.371	5.0	480	0.7457	0.6774	0.6629	0.7669	0.6691
0.2751	6.0	576	0.7689	0.6586	0.6758	0.7405	0.6652
0.2005	7.0	672	1.1899	0.7265	0.6390	0.7933	0.6582
0.1462	8.0	768	1.1159	0.6876	0.6550	0.7757	0.6666
0.1084	9.0	864	1.1029	0.6809	0.6872	0.7640	0.6838
0.1046	10.0	960	1.2153	0.6780	0.6608	0.7679	0.6680

Framework versions

• Transformers 4.47.0
• Pytorch 2.5.1+cu121
• Datasets 3.3.1
• Tokenizers 0.21.0