bert-covid

hung200504/bert-covid

bert-covid is a question answering model from hung200504. Use it when the input is a question plus a passage. It is set up for transformers. The card lists the license as mit.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Question Answering
Library: transformers
License: mit
Model type: bert
Access: Public
Created: Oct 24, 2023
Updated: Oct 24, 2023
SHA: b8968a75

Try a prompt

Base models

Datasets

covid_qa_deepset

Model card

From the Hugging Face model README

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

Task: Question Answering
Library: transformers
Type: bert
License: mit
Created: Oct 24, 2023
Updated: Oct 24, 2023