distilbert-base-uncased-finetuned-sprint-meds

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

• Loss: 0.8121
• Accuracy: 0.8843
• F1: 0.8655

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: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.4894	1.0	21	0.9107	0.8612	0.8354
0.4471	2.0	42	0.8964	0.8630	0.8363
0.4086	3.0	63	0.8796	0.8612	0.8348
0.3651	4.0	84	0.8581	0.8665	0.8415
0.3365	5.0	105	0.8546	0.8683	0.8429
0.3241	6.0	126	0.8448	0.8701	0.8467
0.299	7.0	147	0.8372	0.8683	0.8461
0.2498	8.0	168	0.8340	0.8737	0.8500
0.2579	9.0	189	0.8199	0.8737	0.8498
0.2526	10.0	210	0.8191	0.8772	0.8549
0.2243	11.0	231	0.8227	0.8719	0.8476
0.1888	12.0	252	0.8254	0.8719	0.8489
0.2159	13.0	273	0.8163	0.8772	0.8541
0.1845	14.0	294	0.8117	0.8754	0.8533
0.1774	15.0	315	0.8107	0.8772	0.8529
0.1503	16.0	336	0.8109	0.8790	0.8589
0.1565	17.0	357	0.8141	0.8772	0.8533
0.1539	18.0	378	0.8174	0.8772	0.8556
0.1393	19.0	399	0.8132	0.8790	0.8587
0.1279	20.0	420	0.8171	0.8826	0.8602
0.1231	21.0	441	0.8134	0.8808	0.8603
0.119	22.0	462	0.8132	0.8843	0.8628
0.1058	23.0	483	0.8043	0.8826	0.8631
0.1106	24.0	504	0.8159	0.8808	0.8596
0.1036	25.0	525	0.8090	0.8826	0.8612
0.0895	26.0	546	0.8093	0.8879	0.8666
0.1001	27.0	567	0.8121	0.8843	0.8636
0.0956	28.0	588	0.8113	0.8808	0.8609
0.0954	29.0	609	0.8099	0.8790	0.8581
0.0856	30.0	630	0.8169	0.8826	0.8616
0.0819	31.0	651	0.8204	0.8790	0.8590
0.0888	32.0	672	0.8125	0.8826	0.8644
0.0806	33.0	693	0.8144	0.8826	0.8628
0.0836	34.0	714	0.8153	0.8790	0.8583
0.0832	35.0	735	0.8139	0.8843	0.8644
0.0719	36.0	756	0.8134	0.8826	0.8623
0.0843	37.0	777	0.8141	0.8826	0.8637
0.0768	38.0	798	0.8157	0.8826	0.8616
0.0765	39.0	819	0.8183	0.8808	0.8621
0.0685	40.0	840	0.8139	0.8808	0.8628
0.0696	41.0	861	0.8149	0.8808	0.8631
0.0747	42.0	882	0.8144	0.8843	0.8655
0.0709	43.0	903	0.8136	0.8843	0.8655
0.0666	44.0	924	0.8140	0.8843	0.8661
0.071	45.0	945	0.8123	0.8808	0.8634
0.0682	46.0	966	0.8137	0.8843	0.8661
0.0743	47.0	987	0.8119	0.8843	0.8661
0.069	48.0	1008	0.8113	0.8843	0.8661
0.0624	49.0	1029	0.8119	0.8843	0.8655
0.0713	50.0	1050	0.8121	0.8843	0.8655

Framework versions

• Transformers 4.28.1
• Pytorch 2.0.0+cu118
• Datasets 2.11.0
• Tokenizers 0.13.3