scenario-non-kd-scr-ner-full-mdeberta_data-univner_half55

This model is a fine-tuned version of microsoft/mdeberta-v3-base on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.6455
• Precision: 0.3193
• Recall: 0.4493
• F1: 0.3733
• Accuracy: 0.9207

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: 3e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 55
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.317	0.5828	500	0.3371	0.1842	0.2834	0.2233	0.8991
0.1673	1.1655	1000	0.3697	0.1837	0.3940	0.2506	0.8810
0.1177	1.7483	1500	0.2918	0.2548	0.3929	0.3092	0.9117
0.0791	2.3310	2000	0.3014	0.2738	0.4096	0.3282	0.9157
0.0646	2.9138	2500	0.3112	0.2756	0.4478	0.3412	0.9109
0.0406	3.4965	3000	0.3701	0.2616	0.4389	0.3278	0.9006
0.0361	4.0793	3500	0.3449	0.3060	0.4209	0.3543	0.9190
0.0234	4.6620	4000	0.3659	0.2855	0.4458	0.3481	0.9137
0.0205	5.2448	4500	0.3909	0.2873	0.4336	0.3456	0.9135
0.0149	5.8275	5000	0.4244	0.2749	0.4483	0.3408	0.9093
0.011	6.4103	5500	0.4345	0.2883	0.4444	0.3497	0.9144
0.0106	6.9930	6000	0.4857	0.2653	0.4435	0.3320	0.9081
0.0072	7.5758	6500	0.5048	0.2799	0.4545	0.3464	0.9085
0.0073	8.1585	7000	0.5049	0.2807	0.4546	0.3471	0.9109
0.0057	8.7413	7500	0.4684	0.3143	0.4334	0.3643	0.9206
0.0048	9.3240	8000	0.5157	0.2970	0.4364	0.3535	0.9167
0.0048	9.9068	8500	0.5046	0.3156	0.4359	0.3661	0.9207
0.0043	10.4895	9000	0.4924	0.3386	0.4207	0.3752	0.9267
0.0033	11.0723	9500	0.5727	0.2896	0.4464	0.3513	0.9120
0.0028	11.6550	10000	0.5599	0.2930	0.4438	0.3530	0.9148
0.0026	12.2378	10500	0.5541	0.3140	0.4403	0.3666	0.9214
0.0025	12.8205	11000	0.5515	0.3302	0.4249	0.3716	0.9245
0.0022	13.4033	11500	0.6032	0.2936	0.4478	0.3547	0.9153
0.0026	13.9860	12000	0.5689	0.3124	0.4461	0.3675	0.9207
0.0015	14.5688	12500	0.6279	0.2841	0.4644	0.3525	0.9102
0.0015	15.1515	13000	0.5866	0.3140	0.4323	0.3638	0.9192
0.0014	15.7343	13500	0.5930	0.3113	0.4432	0.3658	0.9203
0.0014	16.3170	14000	0.5985	0.3048	0.4486	0.3629	0.9178
0.0013	16.8998	14500	0.5673	0.3204	0.4470	0.3732	0.9229
0.0009	17.4825	15000	0.6289	0.2944	0.4468	0.3549	0.9144
0.001	18.0653	15500	0.6384	0.2912	0.4585	0.3562	0.9132
0.0008	18.6480	16000	0.6469	0.2999	0.4447	0.3582	0.9153
0.0009	19.2308	16500	0.6730	0.2885	0.4502	0.3517	0.9135
0.0006	19.8135	17000	0.7174	0.2737	0.4689	0.3457	0.9077
0.0006	20.3963	17500	0.6647	0.2984	0.4549	0.3604	0.9158
0.0006	20.9790	18000	0.6393	0.3090	0.4553	0.3682	0.9184
0.0006	21.5618	18500	0.6794	0.2895	0.4566	0.3543	0.9134
0.0005	22.1445	19000	0.6455	0.3193	0.4493	0.3733	0.9207

Framework versions

• Transformers 4.44.2
• Pytorch 2.1.1+cu121
• Datasets 2.14.5
• Tokenizers 0.19.1