deberta-v3-large-model-edit-classifier

This model is a fine-tuned version of microsoft/deberta-v3-large on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.9102
• Krippendorff: 0.7991
• Accuracy: 0.8571
• Macro F1: 0.8377
• Macro Precision: 0.8426
• Macro Recall: 0.8333
• Micro F1: 0.8571
• Micro Precision: 0.8571
• Micro Recall: 0.8571

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: 6e-06
• 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
• lr_scheduler_warmup_steps: 1000
• num_epochs: 20
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Krippendorff	Accuracy	Macro F1	Macro Precision	Macro Recall	Micro F1	Micro Precision	Micro Recall
No log	1.0	83	1.0596	-0.2872	0.5076	0.2245	0.1692	0.3333	0.5076	0.5076	0.5076
No log	2.0	166	0.9677	-0.2872	0.5076	0.2245	0.1692	0.3333	0.5076	0.5076	0.5076
No log	3.0	249	0.8841	-0.2872	0.5076	0.2245	0.1692	0.3333	0.5076	0.5076	0.5076
No log	4.0	332	0.8515	-0.2872	0.5076	0.2245	0.1692	0.3333	0.5076	0.5076	0.5076
No log	5.0	415	0.8048	0.2020	0.6140	0.5405	0.6953	0.5634	0.6140	0.6140	0.6140
No log	6.0	498	0.6971	0.5211	0.7112	0.6850	0.7512	0.7224	0.7112	0.7112	0.7112
0.9047	7.0	581	0.6050	0.6242	0.7599	0.7236	0.7615	0.7092	0.7599	0.7599	0.7599
0.9047	8.0	664	0.4781	0.7468	0.8116	0.7820	0.7974	0.7747	0.8116	0.8116	0.8116
0.9047	9.0	747	0.4446	0.7413	0.8267	0.7986	0.8219	0.8000	0.8267	0.8267	0.8267
0.9047	10.0	830	0.4541	0.7510	0.8480	0.8261	0.8455	0.8134	0.8480	0.8480	0.8480
0.9047	11.0	913	0.4691	0.7527	0.8419	0.8189	0.8353	0.8069	0.8419	0.8419	0.8419
0.9047	12.0	996	0.6103	0.7107	0.8237	0.8011	0.8170	0.8011	0.8237	0.8237	0.8237
0.4334	13.0	1079	0.5507	0.7751	0.8511	0.8360	0.8312	0.8423	0.8511	0.8511	0.8511
0.4334	14.0	1162	0.6642	0.7548	0.8359	0.8168	0.8174	0.8163	0.8359	0.8359	0.8359
0.4334	15.0	1245	0.6858	0.8054	0.8632	0.8443	0.8454	0.8433	0.8632	0.8632	0.8632
0.4334	16.0	1328	0.8332	0.7152	0.8176	0.8007	0.7995	0.8025	0.8176	0.8176	0.8176
0.4334	17.0	1411	0.8105	0.7753	0.8480	0.8308	0.8314	0.8303	0.8480	0.8480	0.8480
0.4334	18.0	1494	0.8472	0.8009	0.8602	0.8416	0.8454	0.8382	0.8602	0.8602	0.8602
0.1213	19.0	1577	0.9164	0.7734	0.8450	0.8273	0.8263	0.8283	0.8450	0.8450	0.8450
0.1213	20.0	1660	0.9102	0.7991	0.8571	0.8377	0.8426	0.8333	0.8571	0.8571	0.8571

Framework versions

• Transformers 4.26.0
• Pytorch 1.13.1
• Datasets 2.10.1
• Tokenizers 0.12.1