Yepes_0.0001_0404_ES6

This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.1207
• Precision: 0.4902
• Recall: 0.3743
• F1: 0.4244
• Accuracy: 0.9769

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: 0.0001
• 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
• training_steps: 2000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.4338	0.43	25	0.1979	0.0	0.0	0.0	0.9705
0.2051	0.86	50	0.1923	0.0	0.0	0.0	0.9705
0.1601	1.29	75	0.1618	0.0	0.0	0.0	0.9705
0.1742	1.72	100	0.1400	0.0	0.0	0.0	0.9705
0.1506	2.16	125	0.1462	0.0	0.0	0.0	0.9705
0.1507	2.59	150	0.1516	0.0	0.0	0.0	0.9705
0.1566	3.02	175	0.1382	0.0	0.0	0.0	0.9705
0.1467	3.45	200	0.1360	0.0	0.0	0.0	0.9705
0.1492	3.88	225	0.1400	0.0	0.0	0.0	0.9705
0.1543	4.31	250	0.1364	0.0	0.0	0.0	0.9705
0.1435	4.74	275	0.1384	0.0	0.0	0.0	0.9705
0.1369	5.17	300	0.1282	0.0	0.0	0.0	0.9705
0.1284	5.6	325	0.1337	0.2381	0.1198	0.1594	0.9704
0.1235	6.03	350	0.1215	0.0	0.0	0.0	0.9705
0.1165	6.47	375	0.1337	0.3613	0.1677	0.2290	0.9739
0.1184	6.9	400	0.1228	0.2303	0.1228	0.1602	0.9718
0.1076	7.33	425	0.1174	0.2646	0.3263	0.2922	0.9671
0.0964	7.76	450	0.1094	0.3972	0.2545	0.3102	0.9751
0.0902	8.19	475	0.1217	0.4264	0.2515	0.3164	0.9742
0.0891	8.62	500	0.1075	0.3746	0.3263	0.3488	0.9736
0.0813	9.05	525	0.1295	0.4354	0.2725	0.3352	0.9738
0.078	9.48	550	0.1067	0.375	0.3413	0.3574	0.9742
0.0751	9.91	575	0.1042	0.4905	0.3084	0.3787	0.9765
0.0683	10.34	600	0.1028	0.4672	0.3413	0.3945	0.9761
0.0687	10.78	625	0.1070	0.4975	0.2994	0.3738	0.9762
0.0664	11.21	650	0.1225	0.3256	0.3383	0.3319	0.9703
0.0565	11.64	675	0.1000	0.4487	0.3144	0.3697	0.9767
0.0555	12.07	700	0.1033	0.4463	0.3234	0.375	0.9757
0.045	12.5	725	0.1150	0.4237	0.3323	0.3725	0.9746
0.0514	12.93	750	0.1126	0.6	0.3503	0.4423	0.9774
0.0387	13.36	775	0.1409	0.3986	0.3473	0.3712	0.9742
0.0419	13.79	800	0.1096	0.4336	0.4401	0.4368	0.9723
0.0349	14.22	825	0.1207	0.4902	0.3743	0.4244	0.9769

Framework versions

• Transformers 4.27.4
• Pytorch 2.0.0+cu118
• Datasets 2.11.0
• Tokenizers 0.13.2