bert-covid

This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the covid_qa_deepset dataset. It achieves the following results on the evaluation set:

• Loss: 0.6900

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: 3e-05
• 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
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
5.474	0.04	5	4.3730
3.9933	0.09	10	3.2783
3.0206	0.13	15	2.0289
1.9741	0.18	20	1.3879
1.4351	0.22	25	1.1733
1.5916	0.26	30	1.1623
0.5383	0.31	35	1.1952
0.7776	0.35	40	1.1920
1.1785	0.39	45	1.1216
1.1334	0.44	50	1.0412
0.7445	0.48	55	1.0829
0.6512	0.53	60	1.0443
0.7516	0.57	65	1.0089
0.5953	0.61	70	0.9273
0.8589	0.66	75	0.8947
0.7561	0.7	80	0.9009
0.9561	0.75	85	0.9006
0.7731	0.79	90	0.8482
0.8269	0.83	95	0.8380
0.9884	0.88	100	0.8200
0.9187	0.92	105	0.8775
0.585	0.96	110	0.8499
0.6835	1.01	115	0.8314
0.6668	1.05	120	0.7491
0.5558	1.1	125	0.7154
0.4491	1.14	130	0.8212
1.0667	1.18	135	0.8477
0.4472	1.23	140	0.7636
0.6892	1.27	145	0.7493
0.66	1.32	150	0.6932
0.5044	1.36	155	0.7675
0.5329	1.4	160	0.7406
0.2223	1.45	165	0.8099
0.5495	1.49	170	0.8758
0.5534	1.54	175	0.8476
0.4962	1.58	180	0.7953
0.7477	1.62	185	0.7610
0.7293	1.67	190	0.8357
0.6205	1.71	195	0.7339
0.5687	1.75	200	0.6908
0.884	1.8	205	0.6706
0.5928	1.84	210	0.6546
0.3209	1.89	215	0.6505
0.7585	1.93	220	0.6486
0.8501	1.97	225	0.6272
0.1664	2.02	230	0.6211
0.4483	2.06	235	0.6550
0.3361	2.11	240	0.6604
0.3085	2.15	245	0.6520
0.2407	2.19	250	0.6695
0.3418	2.24	255	0.6687
0.3165	2.28	260	0.6730
0.5811	2.32	265	0.6546
0.3516	2.37	270	0.6579
0.3136	2.41	275	0.6688
0.2508	2.46	280	0.6921
0.3463	2.5	285	0.7124
0.3603	2.54	290	0.7160
0.4455	2.59	295	0.6995
0.5433	2.63	300	0.6919
0.3411	2.68	305	0.6898
0.6065	2.72	310	0.6922
0.6258	2.76	315	0.6955
0.283	2.81	320	0.7008
0.6233	2.85	325	0.6988
0.3899	2.89	330	0.6949
0.238	2.94	335	0.6916
0.3166	2.98	340	0.6900

Framework versions

• Transformers 4.34.1
• Pytorch 2.1.0+cu118
• Datasets 2.14.6
• Tokenizers 0.14.1