deberta-v3-base-finetuned-ner

This model is a fine-tuned version of microsoft/deberta-v3-base on the generator dataset. It achieves the following results on the evaluation set:

• Loss: 0.7679
• Overall Precision: 0.4915
• Overall Recall: 0.6463
• Overall F1: 0.5584
• Overall Accuracy: 0.9555
• Datasetname F1: 0.3304
• Hyperparametername F1: 0.6341
• Hyperparametervalue F1: 0.7463
• Methodname F1: 0.6093
• Metricname F1: 0.7089
• Metricvalue F1: 0.7500
• Taskname F1: 0.4426

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: 8
• eval_batch_size: 8
• 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	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	0.5046	0.2771	0.5041	0.3576	0.9356	0.2405	0.1988	0.4545	0.4638	0.4539	0.6486	0.2793
No log	2.0	264	0.3928	0.3344	0.6463	0.4407	0.9376	0.2449	0.3968	0.6292	0.5641	0.5373	0.4583	0.3359
No log	3.0	396	0.4714	0.4419	0.6179	0.5153	0.9533	0.3822	0.5310	0.7536	0.6262	0.6328	0.6857	0.3291
0.5663	4.0	528	0.3741	0.4493	0.7114	0.5507	0.9509	0.4717	0.7241	0.6353	0.5918	0.5714	0.6275	0.4372
0.5663	5.0	660	0.4202	0.3930	0.6870	0.5	0.9458	0.2759	0.6525	0.65	0.5596	0.7097	0.7368	0.3573
0.5663	6.0	792	0.4676	0.4244	0.6850	0.5241	0.9473	0.3333	0.5949	0.7397	0.5653	0.6988	0.7568	0.3652
0.5663	7.0	924	0.5744	0.4328	0.5955	0.5013	0.9517	0.2585	0.6167	0.5915	0.5825	0.6386	0.7500	0.3824
0.1503	8.0	1056	0.5340	0.4309	0.6585	0.5209	0.9499	0.2976	0.6299	0.7105	0.6140	0.6708	0.7568	0.3544
0.1503	9.0	1188	0.5229	0.4628	0.6829	0.5517	0.9531	0.4630	0.5103	0.6087	0.625	0.6541	0.7778	0.4493
0.1503	10.0	1320	0.6287	0.4978	0.6748	0.5729	0.9563	0.4314	0.6500	0.7463	0.6413	0.7432	0.7568	0.4108
0.1503	11.0	1452	0.5163	0.4571	0.7033	0.5540	0.9519	0.3925	0.5256	0.6024	0.6828	0.6626	0.7368	0.4466
0.0735	12.0	1584	0.6737	0.5046	0.6687	0.5752	0.9555	0.3883	0.6615	0.6757	0.6074	0.7051	0.7778	0.4577
0.0735	13.0	1716	0.5849	0.44	0.6931	0.5383	0.9480	0.3770	0.6555	0.6479	0.5922	0.6957	0.6512	0.4071
0.0735	14.0	1848	0.8314	0.5018	0.5793	0.5377	0.9539	0.3	0.6549	0.6667	0.5613	0.7361	0.7368	0.4294
0.0735	15.0	1980	0.5986	0.4549	0.6768	0.5441	0.9506	0.3793	0.6000	0.6667	0.6181	0.7089	0.6829	0.3978
0.0408	16.0	2112	0.7579	0.4900	0.6443	0.5566	0.9541	0.4103	0.6032	0.6765	0.6238	0.7123	0.6667	0.4217
0.0408	17.0	2244	0.9175	0.5285	0.6037	0.5636	0.9565	0.4	0.6789	0.7692	0.5949	0.7101	0.6857	0.4122
0.0408	18.0	2376	0.7771	0.5041	0.6179	0.5553	0.9562	0.3684	0.6207	0.7246	0.5842	0.7383	0.6667	0.4353
0.0226	19.0	2508	0.7992	0.5213	0.6463	0.5771	0.9569	0.32	0.6724	0.7353	0.6485	0.7114	0.7179	0.4510
0.0226	20.0	2640	0.7679	0.4915	0.6463	0.5584	0.9555	0.3304	0.6341	0.7463	0.6093	0.7089	0.7500	0.4426

Framework versions

• Transformers 4.23.1
• Pytorch 1.12.1+cu102
• Datasets 2.6.1
• Tokenizers 0.13.1