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Brizape/
Variome_5e-05_0404_ES6

Name: Variome_5e-05_0404_ES6
Availability: InStock

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Variome_5e-05_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: 0.0695
Precision: 0.6584
Recall: 0.6123
F1: 0.6345
Accuracy: 0.9866

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: 5e-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
training_steps: 2000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.56	0.13	25	0.1838	0.0	0.0	0.0	0.9759
0.1843	0.26	50	0.1810	0.0	0.0	0.0	0.9759
0.1822	0.39	75	0.1823	0.0	0.0	0.0	0.9759
0.1797	0.52	100	0.1800	0.0	0.0	0.0	0.9759
0.1618	0.65	125	0.1838	0.0	0.0	0.0	0.9759
0.1735	0.79	150	0.1784	0.0	0.0	0.0	0.9759
0.1654	0.92	175	0.1673	0.0	0.0	0.0	0.9759
0.1171	1.05	200	0.1367	0.1538	0.0038	0.0075	0.9760
0.1366	1.18	225	0.1333	0.1881	0.1881	0.1881	0.9752
0.1262	1.31	250	0.1110	0.2137	0.1411	0.1699	0.9771
0.1102	1.44	275	0.1016	0.3440	0.2582	0.2950	0.9789
0.099	1.57	300	0.0918	0.4712	0.3695	0.4142	0.9813
0.0843	1.7	325	0.0868	0.4654	0.3359	0.3902	0.9812
0.0833	1.83	350	0.0830	0.5755	0.3512	0.4362	0.9818
0.0689	1.96	375	0.0807	0.5766	0.3973	0.4705	0.9828
0.0656	2.09	400	0.0781	0.5361	0.4923	0.5133	0.9839
0.0625	2.23	425	0.0769	0.5802	0.4722	0.5206	0.9839
0.0707	2.36	450	0.0748	0.5979	0.4453	0.5105	0.9837
0.0568	2.49	475	0.0732	0.5515	0.5345	0.5429	0.9844
0.0659	2.62	500	0.0703	0.6244	0.5154	0.5647	0.9845
0.0601	2.75	525	0.0674	0.6174	0.5528	0.5833	0.9848
0.0554	2.88	550	0.0667	0.6230	0.5614	0.5906	0.9855
0.0634	3.01	575	0.0699	0.6605	0.5509	0.6007	0.9856
0.0449	3.14	600	0.0694	0.6249	0.5691	0.5957	0.9850
0.0435	3.27	625	0.0693	0.5868	0.5710	0.5788	0.9847
0.0338	3.4	650	0.0710	0.6321	0.5672	0.5979	0.9858
0.0405	3.53	675	0.0669	0.6245	0.5825	0.6028	0.9859
0.0459	3.66	700	0.0665	0.6808	0.5710	0.6211	0.9862
0.0471	3.8	725	0.0669	0.6162	0.5902	0.6029	0.9845
0.0477	3.93	750	0.0657	0.6655	0.5595	0.6079	0.9859
0.0476	4.06	775	0.0652	0.6483	0.6084	0.6277	0.9859
0.0419	4.19	800	0.0679	0.6134	0.5969	0.6051	0.9854
0.036	4.32	825	0.0693	0.6598	0.5864	0.6209	0.9854
0.0372	4.45	850	0.0666	0.6474	0.6008	0.6232	0.9859
0.0309	4.58	875	0.0702	0.6811	0.5883	0.6313	0.9864
0.0301	4.71	900	0.0659	0.6804	0.5864	0.6299	0.9866
0.0364	4.84	925	0.0651	0.6738	0.6008	0.6352	0.9867
0.0321	4.97	950	0.0640	0.6638	0.5931	0.6265	0.9867
0.0296	5.1	975	0.0657	0.6920	0.5845	0.6337	0.9868
0.0278	5.24	1000	0.0668	0.6739	0.5988	0.6341	0.9866
0.0273	5.37	1025	0.0673	0.6568	0.5969	0.6254	0.9867
0.0254	5.5	1050	0.0677	0.6969	0.5893	0.6386	0.9871
0.0271	5.63	1075	0.0658	0.6667	0.6104	0.6373	0.9861
0.025	5.76	1100	0.0695	0.6584	0.6123	0.6345	0.9866

Framework versions

Transformers 4.27.4
Pytorch 2.0.0+cu118
Datasets 2.11.0
Tokenizers 0.13.2

Brizape/Variome_5e-05_0404_ES6

Variome_5e-05_0404_ES6

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Brizape/
Variome_5e-05_0404_ES6