roberta-large-finetuned-TRAC-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: 2.8198
• Accuracy: 0.7190
• Precision: 0.6955
• Recall: 0.6979
• F1: 0.6963

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: 32
• 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
0.9538	1.0	612	0.8083	0.6111	0.6192	0.6164	0.5994
0.7924	2.0	1224	0.7594	0.6601	0.6688	0.6751	0.6424
0.6844	3.0	1836	0.6986	0.7042	0.6860	0.6969	0.6858
0.5715	3.99	2448	0.7216	0.7075	0.6957	0.6978	0.6925
0.45	4.99	3060	0.7963	0.7288	0.7126	0.7074	0.7073
0.352	5.99	3672	1.0824	0.7141	0.6999	0.6774	0.6818
0.2546	6.99	4284	1.0884	0.7230	0.7006	0.7083	0.7028
0.1975	7.99	4896	1.5338	0.7337	0.7090	0.7063	0.7074
0.1656	8.99	5508	1.8182	0.7100	0.6882	0.6989	0.6896
0.1358	9.98	6120	2.1623	0.7173	0.6917	0.6959	0.6934
0.1235	10.98	6732	2.3249	0.7141	0.6881	0.6914	0.6888
0.1003	11.98	7344	2.3474	0.7124	0.6866	0.6920	0.6887
0.0826	12.98	7956	2.3574	0.7083	0.6853	0.6959	0.6874
0.0727	13.98	8568	2.4989	0.7116	0.6858	0.6934	0.6883
0.0553	14.98	9180	2.8090	0.7026	0.6747	0.6710	0.6725
0.0433	15.97	9792	2.6647	0.7255	0.7010	0.7028	0.7018
0.0449	16.97	10404	2.6568	0.7247	0.7053	0.6997	0.7010
0.0373	17.97	11016	2.7632	0.7149	0.6888	0.6938	0.6909
0.0278	18.97	11628	2.8245	0.7124	0.6866	0.6930	0.6889
0.0288	19.97	12240	2.8198	0.7190	0.6955	0.6979	0.6963

Framework versions

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