clinico-xlm-roberta-large-finetuned

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

Loss: 1.3788
Precision: 0.5418
Recall: 0.6453
F1: 0.5890
Accuracy: 0.8708

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: 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: 100
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	49	0.5993	0.2242	0.4577	0.3010	0.8168
No log	2.0	98	0.5762	0.2365	0.4668	0.3140	0.8280
No log	3.0	147	0.5627	0.2460	0.4989	0.3296	0.8326
No log	4.0	196	0.5260	0.3029	0.5435	0.3890	0.8534
No log	5.0	245	0.5838	0.3058	0.5343	0.3890	0.8559
No log	6.0	294	0.6085	0.3369	0.5686	0.4232	0.8545
No log	7.0	343	0.6481	0.3532	0.5824	0.4397	0.8591
No log	8.0	392	0.6809	0.3523	0.5744	0.4367	0.8580
No log	9.0	441	0.8040	0.3864	0.5778	0.4631	0.8568
No log	10.0	490	0.7505	0.3775	0.5870	0.4595	0.8635
0.3143	11.0	539	0.8028	0.4350	0.6007	0.5046	0.8610
0.3143	12.0	588	0.8103	0.4253	0.6190	0.5042	0.8672
0.3143	13.0	637	0.8302	0.4506	0.6362	0.5275	0.8637
0.3143	14.0	686	0.9385	0.4581	0.6247	0.5286	0.8583
0.3143	15.0	735	0.9407	0.4304	0.6156	0.5066	0.8639
0.3143	16.0	784	0.9105	0.4421	0.6201	0.5162	0.8679
0.3143	17.0	833	0.9616	0.4691	0.6247	0.5358	0.8654
0.3143	18.0	882	0.9695	0.4799	0.6281	0.5441	0.8680
0.3143	19.0	931	1.0195	0.4996	0.6396	0.5610	0.8735
0.3143	20.0	980	1.0073	0.4670	0.6224	0.5336	0.8642
0.0259	21.0	1029	1.0354	0.4783	0.6316	0.5444	0.8673
0.0259	22.0	1078	1.1327	0.5258	0.6419	0.5781	0.8646
0.0259	23.0	1127	1.0605	0.5055	0.6281	0.5602	0.8668
0.0259	24.0	1176	1.0120	0.5158	0.6350	0.5692	0.8657
0.0259	25.0	1225	1.0205	0.4920	0.6339	0.5540	0.8729
0.0259	26.0	1274	1.0583	0.4995	0.6259	0.5556	0.8688
0.0259	27.0	1323	1.1157	0.5066	0.6545	0.5711	0.8698
0.0259	28.0	1372	1.1049	0.5048	0.6568	0.5709	0.8694
0.0259	29.0	1421	1.1167	0.4978	0.6487	0.5633	0.8685
0.0259	30.0	1470	1.1614	0.5	0.6625	0.5699	0.8644
0.0062	31.0	1519	1.1521	0.4991	0.6453	0.5629	0.8647
0.0062	32.0	1568	1.1951	0.4938	0.6419	0.5582	0.8661
0.0062	33.0	1617	1.2044	0.4815	0.6419	0.5503	0.8676
0.0062	34.0	1666	1.1952	0.5242	0.6556	0.5826	0.8712
0.0062	35.0	1715	1.1598	0.5283	0.6625	0.5878	0.8768
0.0062	36.0	1764	1.1716	0.5221	0.6613	0.5835	0.8720
0.0062	37.0	1813	1.2127	0.5236	0.6465	0.5786	0.8707
0.0062	38.0	1862	1.2747	0.5259	0.6499	0.5814	0.8692
0.0062	39.0	1911	1.2397	0.5363	0.6590	0.5914	0.8676
0.0062	40.0	1960	1.2358	0.5477	0.6568	0.5973	0.8746
0.0014	41.0	2009	1.2332	0.5367	0.6602	0.5921	0.8745
0.0014	42.0	2058	1.2239	0.5106	0.6602	0.5758	0.8685
0.0014	43.0	2107	1.2163	0.5224	0.6533	0.5806	0.8679
0.0014	44.0	2156	1.2335	0.5349	0.6568	0.5896	0.8694
0.0014	45.0	2205	1.3374	0.5348	0.6236	0.5758	0.8680
0.0014	46.0	2254	1.2287	0.5417	0.6533	0.5923	0.8730
0.0014	47.0	2303	1.2268	0.5536	0.6796	0.6102	0.8789
0.0014	48.0	2352	1.2153	0.4974	0.6568	0.5661	0.8737
0.0014	49.0	2401	1.2180	0.5222	0.6590	0.5827	0.8747
0.0014	50.0	2450	1.2906	0.5500	0.6476	0.5949	0.8698
0.0014	51.0	2499	1.2547	0.5386	0.6384	0.5843	0.8686
0.0018	52.0	2548	1.2792	0.5307	0.6430	0.5815	0.8681
0.0018	53.0	2597	1.1972	0.5040	0.6510	0.5681	0.8705
0.0018	54.0	2646	1.2189	0.5215	0.6533	0.5800	0.8782
0.0018	55.0	2695	1.2239	0.5602	0.6602	0.6061	0.8789
0.0018	56.0	2744	1.2620	0.5410	0.6648	0.5965	0.8773
0.0018	57.0	2793	1.2828	0.5513	0.6522	0.5975	0.8747
0.0018	58.0	2842	1.2633	0.5518	0.6522	0.5978	0.8749
0.0018	59.0	2891	1.2619	0.5356	0.6796	0.5991	0.8738
0.0018	60.0	2940	1.2076	0.5385	0.6716	0.5978	0.8775
0.0018	61.0	2989	1.2996	0.5357	0.6442	0.5849	0.8686
0.0011	62.0	3038	1.2614	0.5483	0.6693	0.6028	0.8773
0.0011	63.0	3087	1.2713	0.5524	0.6579	0.6005	0.8757
0.0011	64.0	3136	1.2920	0.5550	0.6579	0.6021	0.8739
0.0011	65.0	3185	1.3319	0.5623	0.6716	0.6121	0.8713
0.0011	66.0	3234	1.3345	0.5433	0.6533	0.5932	0.8720
0.0011	67.0	3283	1.3146	0.5305	0.6465	0.5828	0.8657
0.0011	68.0	3332	1.3354	0.5452	0.6556	0.5953	0.8691
0.0011	69.0	3381	1.3474	0.5519	0.6693	0.6050	0.8759
0.0011	70.0	3430	1.3498	0.5403	0.6590	0.5938	0.8686
0.0011	71.0	3479	1.3340	0.5387	0.6602	0.5933	0.8749
0.0005	72.0	3528	1.3475	0.5615	0.6636	0.6083	0.8745
0.0005	73.0	3577	1.3530	0.5425	0.6648	0.5974	0.8746
0.0005	74.0	3626	1.3494	0.5491	0.6648	0.6014	0.8738
0.0005	75.0	3675	1.3368	0.5620	0.6590	0.6066	0.8749
0.0005	76.0	3724	1.3382	0.5467	0.6625	0.5991	0.8752
0.0005	77.0	3773	1.3486	0.5377	0.6533	0.5899	0.8759
0.0005	78.0	3822	1.3485	0.5483	0.6499	0.5948	0.8731
0.0005	79.0	3871	1.3512	0.5340	0.6556	0.5886	0.8751
0.0005	80.0	3920	1.3486	0.5513	0.6636	0.6023	0.8772
0.0005	81.0	3969	1.3530	0.5481	0.6579	0.5980	0.8772
0.0001	82.0	4018	1.3940	0.5536	0.6499	0.5979	0.8751
0.0001	83.0	4067	1.3657	0.5296	0.6453	0.5817	0.8742
0.0001	84.0	4116	1.3538	0.5412	0.6384	0.5858	0.8719
0.0001	85.0	4165	1.3550	0.5418	0.6373	0.5857	0.8693
0.0001	86.0	4214	1.3810	0.5187	0.6362	0.5714	0.8685
0.0001	87.0	4263	1.3625	0.5370	0.6396	0.5838	0.8707
0.0001	88.0	4312	1.3605	0.5389	0.6419	0.5859	0.8712
0.0001	89.0	4361	1.3616	0.5388	0.6430	0.5863	0.8711
0.0001	90.0	4410	1.3560	0.5431	0.6419	0.5884	0.8719
0.0001	91.0	4459	1.3558	0.5399	0.6430	0.5869	0.8716
0.0001	92.0	4508	1.3586	0.5342	0.6430	0.5836	0.8717
0.0001	93.0	4557	1.3727	0.5349	0.6487	0.5863	0.8716
0.0001	94.0	4606	1.3810	0.5539	0.6465	0.5966	0.8707
0.0001	95.0	4655	1.3813	0.5540	0.6453	0.5962	0.8705
0.0001	96.0	4704	1.3879	0.5625	0.6487	0.6026	0.8699
0.0001	97.0	4753	1.3886	0.5614	0.6487	0.6019	0.8702
0.0001	98.0	4802	1.3811	0.5470	0.6453	0.5921	0.8704
0.0001	99.0	4851	1.3788	0.5407	0.6453	0.5884	0.8709
0.0001	100.0	4900	1.3788	0.5418	0.6453	0.5890	0.8708

Framework versions

Transformers 4.27.0.dev0
Pytorch 1.13.0
Datasets 2.8.0
Tokenizers 0.12.1

joheras/clinico-xlm-roberta-large-finetuned

clinico-xlm-roberta-large-finetuned

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

为此模型做种