Training Loss	Epoch	Step	Validation Loss	Overall Precision	Overall Recall	Overall F1	Overall Accuracy	Datasetname F1	Hyperparametername F1	Hyperparametervalue F1	Methodname F1	Metricname F1	Metricvalue F1	Taskname F1
No log	1.0	132	0.4127	0.3852	0.6646	0.4877	0.9411	0.3875	0.4690	0.6	0.6338	0.6438	0.5806	0.3670
No log	2.0	264	0.3424	0.3447	0.6972	0.4613	0.9353	0.3204	0.4103	0.5600	0.5691	0.5848	0.7027	0.3594
No log	3.0	396	0.3942	0.4767	0.6850	0.5621	0.9534	0.5385	0.6500	0.7429	0.6583	0.6437	0.6111	0.3830
0.4541	4.0	528	0.3542	0.4516	0.7012	0.5494	0.9503	0.4127	0.6552	0.5417	0.6068	0.6243	0.4762	0.4895
0.4541	5.0	660	0.4092	0.5076	0.6829	0.5823	0.9560	0.3857	0.5827	0.6933	0.6866	0.7465	0.6875	0.4865
0.4541	6.0	792	0.4450	0.4465	0.6870	0.5412	0.9491	0.3613	0.5985	0.6506	0.6278	0.6667	0.6857	0.4332
0.4541	7.0	924	0.4487	0.4985	0.6707	0.5719	0.9552	0.4407	0.6400	0.5789	0.6590	0.6980	0.7429	0.4667
0.1083	8.0	1056	0.4361	0.5068	0.6850	0.5825	0.9569	0.4553	0.6457	0.7429	0.6667	0.6887	0.6875	0.4536
0.1083	9.0	1188	0.5592	0.4954	0.6504	0.5624	0.9549	0.4538	0.6552	0.6753	0.6397	0.6581	0.7647	0.4118
0.1083	10.0	1320	0.5272	0.4686	0.6667	0.5503	0.9497	0.3816	0.6074	0.7	0.6340	0.7347	0.7429	0.3917
0.1083	11.0	1452	0.6108	0.5412	0.6809	0.6031	0.9562	0.4727	0.6724	0.7222	0.6615	0.7097	0.6857	0.5027
0.0491	12.0	1584	0.7836	0.5481	0.6138	0.5791	0.9546	0.5043	0.6446	0.7246	0.6286	0.7347	0.7273	0.4217
0.0491	13.0	1716	0.5258	0.4838	0.6667	0.5607	0.9527	0.4580	0.6299	0.6944	0.6234	0.7089	0.6667	0.4060
0.0491	14.0	1848	0.6477	0.5487	0.6301	0.5866	0.9576	0.4685	0.6909	0.7692	0.6312	0.6528	0.7273	0.4773
0.0491	15.0	1980	0.5891	0.5359	0.6972	0.6060	0.9577	0.4865	0.6777	0.7123	0.6667	0.7114	0.6875	0.4986
0.0288	16.0	2112	0.6913	0.5510	0.6809	0.6091	0.9575	0.5053	0.6783	0.7463	0.7063	0.6853	0.6842	0.4602
0.0288	17.0	2244	0.7530	0.5425	0.6484	0.5907	0.9572	0.5149	0.6446	0.8065	0.6796	0.6993	0.75	0.3974
0.0288	18.0	2376	0.7542	0.5815	0.6524	0.6149	0.9594	0.5306	0.6667	0.7353	0.6918	0.7077	0.7273	0.4706
0.0137	19.0	2508	0.7550	0.5529	0.6585	0.6011	0.9561	0.5333	0.6957	0.6765	0.6508	0.7746	0.7059	0.4389
0.0137	20.0	2640	0.6984	0.5335	0.6789	0.5975	0.9538	0.4828	0.6721	0.7353	0.6382	0.7518	0.6667	0.4731
0.0137	21.0	2772	0.6706	0.5221	0.7215	0.6058	0.9511	0.4640	0.6780	0.72	0.6389	0.7355	0.6667	0.5215
0.0137	22.0	2904	0.7129	0.5533	0.6646	0.6039	0.9561	0.5091	0.7	0.6667	0.6553	0.7761	0.6111	0.4673
0.0096	23.0	3036	0.7137	0.5601	0.6728	0.6113	0.9583	0.5185	0.6780	0.7879	0.6621	0.7328	0.6	0.4926
0.0096	24.0	3168	0.6871	0.5235	0.6789	0.5912	0.9534	0.4828	0.6891	0.6667	0.6414	0.7310	0.7273	0.4676
0.0096	25.0	3300	0.7823	0.5641	0.6524	0.6051	0.9567	0.4628	0.7009	0.7576	0.6716	0.7183	0.6875	0.4762
0.0096	26.0	3432	0.7905	0.5512	0.6565	0.5993	0.9556	0.5143	0.7368	0.7463	0.6332	0.7121	0.6875	0.4531
0.0061	27.0	3564	0.8666	0.5557	0.6585	0.6028	0.9553	0.4779	0.7130	0.7692	0.6689	0.7391	0.6667	0.4465
0.0061	28.0	3696	0.8848	0.5492	0.6240	0.5842	0.9552	0.4590	0.7273	0.7937	0.6227	0.7597	0.6250	0.4348

Framework versions

Transformers 4.23.1
Pytorch 1.12.1+cu102
Datasets 2.6.1
Tokenizers 0.13.1

sohamtiwari3120/scideberta-cs-finetuned-ner

scideberta-cs-finetuned-ner

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

sohamtiwari3120/
scideberta-cs-finetuned-ner