clinico-roberta-biomedical-finetuned-augmented1

This model is a fine-tuned version of joheras/roberta-base-biomedical-clinical-es-finetuned-clinais on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.2547
Precision: 0.5202
Recall: 0.6368
F1: 0.5726
Accuracy: 0.8678

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: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	90	0.5952	0.3894	0.4968	0.4366	0.8389
No log	2.0	180	0.5302	0.3351	0.4653	0.3896	0.8506
No log	3.0	270	0.5888	0.3925	0.5863	0.4702	0.8587
No log	4.0	360	0.6183	0.4100	0.6116	0.4909	0.8644
No log	5.0	450	0.6412	0.4702	0.6232	0.5360	0.8696
0.3362	6.0	540	0.7014	0.4808	0.6326	0.5464	0.8693
0.3362	7.0	630	0.7379	0.4500	0.6305	0.5252	0.8682
0.3362	8.0	720	0.7744	0.4722	0.6358	0.5419	0.8653
0.3362	9.0	810	0.7712	0.4896	0.6432	0.5560	0.8716
0.3362	10.0	900	0.7924	0.4904	0.6484	0.5585	0.8687
0.3362	11.0	990	0.8283	0.4984	0.6463	0.5628	0.8691
0.0237	12.0	1080	0.8286	0.5131	0.64	0.5696	0.8710
0.0237	13.0	1170	0.8492	0.5098	0.6558	0.5737	0.8687
0.0237	14.0	1260	0.8649	0.5137	0.6516	0.5745	0.8676
0.0237	15.0	1350	0.8748	0.5232	0.6526	0.5808	0.8702
0.0237	16.0	1440	0.8653	0.5183	0.6421	0.5736	0.8685
0.0086	17.0	1530	0.8938	0.5219	0.6526	0.5800	0.8722
0.0086	18.0	1620	0.9006	0.5083	0.6432	0.5678	0.8682
0.0086	19.0	1710	0.9220	0.5238	0.6484	0.5795	0.8693
0.0086	20.0	1800	0.8676	0.5151	0.6463	0.5733	0.8724
0.0086	21.0	1890	0.9404	0.5185	0.6495	0.5766	0.8643
0.0086	22.0	1980	0.9477	0.5409	0.6537	0.5920	0.8678
0.0052	23.0	2070	0.9441	0.5342	0.6568	0.5892	0.8736
0.0052	24.0	2160	0.9786	0.5373	0.6368	0.5829	0.8685
0.0052	25.0	2250	0.9510	0.5243	0.6463	0.5790	0.8722
0.0052	26.0	2340	0.9876	0.5261	0.6463	0.5801	0.8683
0.0052	27.0	2430	1.0049	0.5265	0.6484	0.5811	0.8652
0.0033	28.0	2520	1.0204	0.5347	0.6495	0.5865	0.8630
0.0033	29.0	2610	1.0027	0.5101	0.6411	0.5681	0.8647
0.0033	30.0	2700	1.0345	0.5243	0.6347	0.5743	0.8649
0.0033	31.0	2790	1.0199	0.5222	0.6316	0.5717	0.8663
0.0033	32.0	2880	1.0424	0.5243	0.6368	0.5751	0.8669
0.0033	33.0	2970	1.0341	0.5294	0.6453	0.5816	0.8662
0.0025	34.0	3060	1.0367	0.5419	0.6474	0.5899	0.8667
0.0025	35.0	3150	1.0629	0.5225	0.6484	0.5787	0.8660
0.0025	36.0	3240	1.0406	0.5227	0.6432	0.5767	0.8672
0.0025	37.0	3330	1.0168	0.5324	0.6495	0.5851	0.8701
0.0025	38.0	3420	1.0375	0.5332	0.6505	0.5861	0.8693
0.0015	39.0	3510	1.0921	0.5378	0.6442	0.5862	0.8649
0.0015	40.0	3600	1.0742	0.5330	0.6453	0.5838	0.8657
0.0015	41.0	3690	1.1234	0.5189	0.6347	0.5710	0.8619
0.0015	42.0	3780	1.0940	0.5407	0.6505	0.5905	0.8659
0.0015	43.0	3870	1.0612	0.5493	0.6505	0.5957	0.8704
0.0015	44.0	3960	1.0730	0.5445	0.6505	0.5928	0.8696
0.0008	45.0	4050	1.0834	0.5484	0.6558	0.5973	0.8675
0.0008	46.0	4140	1.1115	0.5487	0.6463	0.5935	0.8688
0.0008	47.0	4230	1.1153	0.5491	0.6474	0.5942	0.8661
0.0008	48.0	4320	1.1142	0.5456	0.6421	0.5899	0.8674
0.0008	49.0	4410	1.0922	0.5285	0.6537	0.5845	0.8704
0.0007	50.0	4500	1.0873	0.5448	0.6463	0.5912	0.8700
0.0007	51.0	4590	1.1141	0.5342	0.6337	0.5797	0.8686
0.0007	52.0	4680	1.2066	0.5116	0.6263	0.5632	0.8617
0.0007	53.0	4770	1.0850	0.5224	0.6379	0.5744	0.8704
0.0007	54.0	4860	1.1132	0.5308	0.6526	0.5855	0.8728
0.0007	55.0	4950	1.1540	0.5118	0.64	0.5688	0.8667
0.001	56.0	5040	1.1314	0.5314	0.6495	0.5846	0.8683
0.001	57.0	5130	1.0893	0.5456	0.6547	0.5952	0.8713
0.001	58.0	5220	1.0910	0.5354	0.6453	0.5852	0.8685
0.001	59.0	5310	1.1131	0.5527	0.6568	0.6003	0.8742
0.001	60.0	5400	1.1434	0.5339	0.6463	0.5848	0.8694
0.001	61.0	5490	1.1186	0.5313	0.6516	0.5853	0.8731
0.0007	62.0	5580	1.1584	0.5381	0.6474	0.5877	0.8684
0.0007	63.0	5670	1.1687	0.5429	0.6463	0.5901	0.8662
0.0007	64.0	5760	1.1296	0.5223	0.6421	0.5760	0.8756
0.0007	65.0	5850	1.1499	0.5345	0.6516	0.5873	0.8710
0.0007	66.0	5940	1.1771	0.5318	0.6516	0.5856	0.8713
0.0005	67.0	6030	1.1531	0.5219	0.6526	0.5800	0.8741
0.0005	68.0	6120	1.1781	0.5383	0.6358	0.5830	0.8713
0.0005	69.0	6210	1.1989	0.5164	0.6316	0.5682	0.8684
0.0005	70.0	6300	1.1986	0.5389	0.6495	0.5890	0.8695
0.0005	71.0	6390	1.1720	0.5603	0.6411	0.5979	0.8720
0.0005	72.0	6480	1.1699	0.5308	0.6432	0.5816	0.8725
0.0005	73.0	6570	1.1781	0.5541	0.6411	0.5944	0.8708
0.0005	74.0	6660	1.2327	0.5304	0.6337	0.5775	0.8664
0.0005	75.0	6750	1.2070	0.5537	0.6463	0.5964	0.8718
0.0005	76.0	6840	1.2032	0.5502	0.6463	0.5944	0.8728
0.0005	77.0	6930	1.2100	0.5525	0.6484	0.5966	0.8713
0.0003	78.0	7020	1.2171	0.5336	0.6442	0.5837	0.8715
0.0003	79.0	7110	1.2256	0.5241	0.64	0.5763	0.8704
0.0003	80.0	7200	1.2238	0.5323	0.6421	0.5821	0.8696
0.0003	81.0	7290	1.2219	0.5342	0.6326	0.5793	0.8693
0.0003	82.0	7380	1.2251	0.5325	0.6379	0.5805	0.8694
0.0003	83.0	7470	1.2187	0.5468	0.6389	0.5893	0.8681
0.0003	84.0	7560	1.2309	0.5365	0.6421	0.5846	0.8683
0.0003	85.0	7650	1.2445	0.5350	0.6432	0.5841	0.8676
0.0003	86.0	7740	1.2561	0.5288	0.6474	0.5821	0.8680
0.0003	87.0	7830	1.2567	0.5263	0.6421	0.5785	0.8678
0.0003	88.0	7920	1.2470	0.5346	0.6421	0.5835	0.8679
0.0002	89.0	8010	1.2458	0.5468	0.6453	0.5920	0.8684
0.0002	90.0	8100	1.2448	0.5484	0.6442	0.5924	0.8689
0.0002	91.0	8190	1.2439	0.5469	0.6379	0.5889	0.8684
0.0002	92.0	8280	1.2453	0.5338	0.64	0.5821	0.8695
0.0002	93.0	8370	1.2462	0.5315	0.64	0.5807	0.8692
0.0002	94.0	8460	1.2472	0.5328	0.6411	0.5819	0.8691
0.0002	95.0	8550	1.2502	0.5311	0.6379	0.5796	0.8686
0.0002	96.0	8640	1.2464	0.5330	0.6368	0.5803	0.8691
0.0002	97.0	8730	1.2526	0.5185	0.6337	0.5703	0.8681
0.0002	98.0	8820	1.2543	0.5167	0.6347	0.5697	0.8679
0.0002	99.0	8910	1.2544	0.5211	0.6358	0.5728	0.8680
0.0002	100.0	9000	1.2547	0.5202	0.6368	0.5726	0.8678

Framework versions

Transformers 4.27.0.dev0
Pytorch 1.13.0
Datasets 2.8.0
Tokenizers 0.12.1

joheras/clinico-roberta-biomedical-finetuned-augmented1

clinico-roberta-biomedical-finetuned-augmented1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Seed this model