roberta-large-finetuned-ours-DS

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

• Loss: 2.3369
• Accuracy: 0.75
• Precision: 0.7054
• Recall: 0.6949
• F1: 0.6974

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: 1e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 43
• 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
1.0561	0.99	99	0.8773	0.615	0.4054	0.5584	0.4591
0.762	1.98	198	0.6514	0.715	0.6735	0.6672	0.6588
0.5661	2.97	297	0.6806	0.71	0.6764	0.6608	0.6435
0.3699	3.96	396	0.8358	0.71	0.6611	0.6691	0.6570
0.2184	4.95	495	1.1627	0.7	0.6597	0.6337	0.6414
0.1743	5.94	594	1.0544	0.725	0.6831	0.6949	0.6831
0.098	6.93	693	1.4757	0.73	0.6885	0.6902	0.6892
0.0813	7.92	792	1.8146	0.73	0.6840	0.6772	0.6800
0.0435	8.91	891	1.6697	0.755	0.7141	0.7127	0.7132
0.0209	9.9	990	1.8931	0.755	0.7102	0.7070	0.7082
0.0201	10.89	1089	2.1934	0.74	0.6971	0.6866	0.6907
0.0095	11.88	1188	2.1389	0.75	0.7014	0.6915	0.6932
0.0141	12.87	1287	2.1902	0.74	0.6942	0.6943	0.6936
0.0112	13.86	1386	2.5021	0.73	0.6889	0.6669	0.6741
0.0054	14.85	1485	2.3840	0.73	0.6819	0.6715	0.6746
0.0088	15.84	1584	2.3224	0.74	0.6909	0.6825	0.6787
0.003	16.83	1683	2.2641	0.75	0.7054	0.6949	0.6974
0.0017	17.82	1782	2.3361	0.75	0.7077	0.6968	0.7012
0.0014	18.81	1881	2.3041	0.755	0.7131	0.7009	0.7051
0.0083	19.8	1980	2.3369	0.75	0.7054	0.6949	0.6974

Framework versions

• Transformers 4.20.1
• Pytorch 1.10.1+cu111
• Datasets 2.3.2
• Tokenizers 0.12.1