clinico-bsc-bio-ehr-es

This model is a fine-tuned version of PlanTL-GOB-ES/bsc-bio-ehr-es on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.9988
Precision: 0.4916
Recall: 0.6526
F1: 0.5608
Accuracy: 0.8586

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	25	1.2185	0.0189	0.0359	0.0247	0.6197
No log	2.0	50	0.7442	0.1562	0.1975	0.1744	0.7996
No log	3.0	75	0.6502	0.2108	0.2640	0.2344	0.8180
No log	4.0	100	0.6404	0.3453	0.4572	0.3935	0.8258
No log	5.0	125	0.6131	0.3639	0.4657	0.4085	0.8303
No log	6.0	150	0.6123	0.3356	0.4256	0.3752	0.8341
No log	7.0	175	0.6093	0.3411	0.4498	0.3880	0.8370
No log	8.0	200	0.6198	0.3840	0.4931	0.4318	0.8379
No log	9.0	225	0.6490	0.3878	0.5037	0.4382	0.8378
No log	10.0	250	0.6653	0.3810	0.5005	0.4327	0.8371
No log	11.0	275	0.6456	0.3223	0.4847	0.3872	0.8387
No log	12.0	300	0.6475	0.3377	0.4847	0.3981	0.8474
No log	13.0	325	0.6620	0.4004	0.5734	0.4716	0.8506
No log	14.0	350	0.6798	0.3914	0.5649	0.4624	0.8533
No log	15.0	375	0.6880	0.3969	0.5671	0.4670	0.8520
No log	16.0	400	0.7012	0.4192	0.5913	0.4906	0.8551
No log	17.0	425	0.7224	0.4143	0.5924	0.4876	0.8517
No log	18.0	450	0.7510	0.4302	0.6051	0.5029	0.8553
No log	19.0	475	0.7388	0.4271	0.6030	0.5	0.8532
0.3652	20.0	500	0.7524	0.4374	0.6125	0.5103	0.8569
0.3652	21.0	525	0.7408	0.4427	0.6082	0.5125	0.8580
0.3652	22.0	550	0.7430	0.4448	0.6125	0.5153	0.8610
0.3652	23.0	575	0.7726	0.4193	0.6093	0.4968	0.8582
0.3652	24.0	600	0.7876	0.4316	0.6061	0.5042	0.8562
0.3652	25.0	625	0.7777	0.4620	0.6294	0.5329	0.8595
0.3652	26.0	650	0.8009	0.4521	0.6272	0.5254	0.8570
0.3652	27.0	675	0.8153	0.4583	0.6378	0.5333	0.8572
0.3652	28.0	700	0.8215	0.4611	0.6262	0.5311	0.8580
0.3652	29.0	725	0.8296	0.4699	0.6336	0.5396	0.8595
0.3652	30.0	750	0.8174	0.4597	0.6378	0.5343	0.8603
0.3652	31.0	775	0.8442	0.4765	0.6410	0.5466	0.8599
0.3652	32.0	800	0.8281	0.4646	0.6315	0.5354	0.8610
0.3652	33.0	825	0.8322	0.4583	0.6389	0.5337	0.8591
0.3652	34.0	850	0.8153	0.4559	0.6272	0.528	0.8623
0.3652	35.0	875	0.8529	0.4861	0.6294	0.5486	0.8589
0.3652	36.0	900	0.8826	0.4699	0.6272	0.5373	0.8559
0.3652	37.0	925	0.8856	0.4654	0.6325	0.5363	0.8571
0.3652	38.0	950	0.8983	0.4819	0.6315	0.5466	0.8560
0.3652	39.0	975	0.8723	0.4641	0.6272	0.5335	0.8556
0.0269	40.0	1000	0.8788	0.4662	0.6399	0.5394	0.8550
0.0269	41.0	1025	0.8952	0.4805	0.6378	0.5481	0.8611
0.0269	42.0	1050	0.8901	0.4657	0.6304	0.5357	0.8574
0.0269	43.0	1075	0.9015	0.4746	0.6410	0.5454	0.8574
0.0269	44.0	1100	0.8838	0.4655	0.6420	0.5397	0.8591
0.0269	45.0	1125	0.9093	0.4718	0.6441	0.5446	0.8598
0.0269	46.0	1150	0.9154	0.4826	0.6441	0.5518	0.8553
0.0269	47.0	1175	0.9214	0.4614	0.6315	0.5332	0.8538
0.0269	48.0	1200	0.9313	0.4639	0.6315	0.5349	0.8546
0.0269	49.0	1225	0.9137	0.4807	0.6431	0.5501	0.8582
0.0269	50.0	1250	0.9235	0.4939	0.6463	0.5599	0.8571
0.0269	51.0	1275	0.9263	0.4900	0.6441	0.5566	0.8580
0.0269	52.0	1300	0.9190	0.4787	0.6420	0.5485	0.8613
0.0269	53.0	1325	0.9159	0.4700	0.6441	0.5434	0.8616
0.0269	54.0	1350	0.9302	0.4806	0.6399	0.5489	0.8614
0.0269	55.0	1375	0.9391	0.4877	0.6515	0.5579	0.8581
0.0269	56.0	1400	0.9392	0.4959	0.6452	0.5608	0.8580
0.0269	57.0	1425	0.9444	0.4798	0.6410	0.5488	0.8570
0.0269	58.0	1450	0.9394	0.4777	0.6441	0.5486	0.8596
0.0269	59.0	1475	0.9562	0.4833	0.6420	0.5515	0.8586
0.0098	60.0	1500	0.9485	0.4801	0.6484	0.5517	0.8582
0.0098	61.0	1525	0.9521	0.4679	0.6463	0.5428	0.8582
0.0098	62.0	1550	0.9603	0.4759	0.6463	0.5481	0.8563
0.0098	63.0	1575	0.9663	0.4831	0.6473	0.5532	0.8561
0.0098	64.0	1600	0.9641	0.4780	0.6526	0.5518	0.8580
0.0098	65.0	1625	0.9607	0.4767	0.6494	0.5498	0.8606
0.0098	66.0	1650	0.9782	0.4849	0.6463	0.5541	0.8563
0.0098	67.0	1675	0.9806	0.4916	0.6484	0.5592	0.8562
0.0098	68.0	1700	0.9728	0.4889	0.6494	0.5578	0.8578
0.0098	69.0	1725	0.9766	0.4885	0.6494	0.5576	0.8584
0.0098	70.0	1750	0.9738	0.4862	0.6526	0.5573	0.8575
0.0098	71.0	1775	0.9788	0.4916	0.6505	0.56	0.8571
0.0098	72.0	1800	0.9845	0.4845	0.6452	0.5534	0.8563
0.0098	73.0	1825	0.9729	0.4876	0.6463	0.5559	0.8573
0.0098	74.0	1850	0.9854	0.4846	0.6494	0.5551	0.8569
0.0098	75.0	1875	0.9903	0.4885	0.6505	0.5580	0.8562
0.0098	76.0	1900	0.9825	0.4886	0.6558	0.5600	0.8568
0.0098	77.0	1925	0.9994	0.4876	0.6463	0.5559	0.8554
0.0098	78.0	1950	0.9922	0.4905	0.6515	0.5596	0.8546
0.0098	79.0	1975	1.0084	0.4928	0.6484	0.5600	0.8578
0.0057	80.0	2000	0.9931	0.4976	0.6526	0.5646	0.8580
0.0057	81.0	2025	0.9864	0.4826	0.6452	0.5522	0.8595
0.0057	82.0	2050	0.9929	0.4900	0.6484	0.5582	0.8595
0.0057	83.0	2075	0.9902	0.4916	0.6473	0.5588	0.8588
0.0057	84.0	2100	1.0021	0.4872	0.6431	0.5544	0.8573
0.0057	85.0	2125	1.0013	0.4964	0.6473	0.5619	0.8582
0.0057	86.0	2150	0.9814	0.4865	0.6484	0.5559	0.8625
0.0057	87.0	2175	0.9841	0.4932	0.6558	0.5630	0.8622
0.0057	88.0	2200	0.9888	0.4866	0.6515	0.5571	0.8610
0.0057	89.0	2225	0.9898	0.4924	0.6515	0.5609	0.8610
0.0057	90.0	2250	0.9860	0.4870	0.6526	0.5578	0.8607
0.0057	91.0	2275	0.9925	0.4912	0.6484	0.5589	0.8589
0.0057	92.0	2300	0.9904	0.4956	0.6536	0.5638	0.8599
0.0057	93.0	2325	0.9902	0.4980	0.6526	0.5649	0.8602
0.0057	94.0	2350	0.9925	0.5041	0.6547	0.5696	0.8602
0.0057	95.0	2375	0.9959	0.4897	0.6515	0.5591	0.8589
0.0057	96.0	2400	0.9951	0.4901	0.6505	0.5590	0.8591
0.0057	97.0	2425	0.9962	0.4924	0.6505	0.5605	0.8588
0.0057	98.0	2450	0.9972	0.5008	0.6505	0.5659	0.8585
0.0057	99.0	2475	0.9988	0.4920	0.6526	0.5611	0.8588
0.0045	100.0	2500	0.9988	0.4916	0.6526	0.5608	0.8586

Framework versions

Transformers 4.25.1
Pytorch 1.13.0
Datasets 2.8.0
Tokenizers 0.12.1

joheras/clinico-bsc-bio-ehr-es

clinico-bsc-bio-ehr-es

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Seed this model