bert-goemotions-10epochs-run5

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

• Loss: 0.1699
• Accuracy Thresh: 0.9478
• F1 weighted: 0.3214
• F1 macro: 0.2568
• Accuracy: 0.3159
• Recall weighted: 0.3159
• Recall macro: 0.2830

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: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy Thresh	F1 weighted	F1 macro	Accuracy	Recall weighted	Recall macro
0.0641	1.0	10558	0.1495	0.9525	0.3589	0.2877	0.3579	0.3579	0.3107
0.059	2.0	21116	0.1485	0.9517	0.3512	0.2834	0.3472	0.3472	0.3064
0.0624	3.0	31674	0.1596	0.9505	0.3426	0.2708	0.3411	0.3411	0.2892
0.0602	4.0	42232	0.1671	0.9474	0.3294	0.2678	0.3238	0.3238	0.2946
0.0593	5.0	52790	0.1697	0.9481	0.3308	0.2599	0.3278	0.3278	0.2834
0.0645	6.0	63348	0.1440	0.9532	0.3419	0.2772	0.3405	0.3405	0.3053
0.075	7.0	73906	0.1501	0.9516	0.3362	0.2682	0.3324	0.3324	0.2909
0.0721	8.0	84464	0.1570	0.9501	0.3292	0.2613	0.3249	0.3249	0.2843
0.0695	9.0	95022	0.1627	0.9492	0.3252	0.2600	0.3214	0.3214	0.2861
0.0672	10.0	105580	0.1699	0.9478	0.3214	0.2568	0.3159	0.3159	0.2830

Framework versions

• Transformers 4.35.1
• Pytorch 2.1.0+cu118
• Datasets 2.14.6
• Tokenizers 0.14.1