distilbert-base-cased-finetuned-ner_0220_J_ORIDATA

This model is a fine-tuned version of distilbert-base-cased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.5445
• Precision: 0.8915
• Recall: 0.9466
• F1: 0.9182
• Accuracy: 0.9486

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: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	111	0.2828	0.7395	0.8322	0.7831	0.9293
No log	2.0	222	0.2260	0.8029	0.8941	0.8460	0.9413
No log	3.0	333	0.2586	0.8131	0.9034	0.8559	0.9425
No log	4.0	444	0.2411	0.8541	0.9178	0.8848	0.9441
0.3027	5.0	555	0.2776	0.8817	0.9220	0.9014	0.9466
0.3027	6.0	666	0.2348	0.8647	0.9314	0.8968	0.9445
0.3027	7.0	777	0.2870	0.8762	0.9297	0.9021	0.9471
0.3027	8.0	888	0.3006	0.8650	0.9288	0.8958	0.9428
0.3027	9.0	999	0.3099	0.8751	0.9263	0.9000	0.9440
0.1147	10.0	1110	0.3325	0.8781	0.9398	0.9079	0.9464
0.1147	11.0	1221	0.3437	0.8909	0.9415	0.9155	0.9461
0.1147	12.0	1332	0.3512	0.8935	0.9390	0.9157	0.9468
0.1147	13.0	1443	0.3664	0.8791	0.9424	0.9096	0.9477
0.0673	14.0	1554	0.4068	0.8767	0.9398	0.9072	0.9445
0.0673	15.0	1665	0.4015	0.8808	0.9390	0.9089	0.9481
0.0673	16.0	1776	0.4220	0.8874	0.9483	0.9168	0.9467
0.0673	17.0	1887	0.4313	0.8847	0.9432	0.9130	0.9451
0.0673	18.0	1998	0.4440	0.8762	0.9415	0.9077	0.9408
0.041	19.0	2109	0.4524	0.9034	0.9508	0.9265	0.9484
0.041	20.0	2220	0.4455	0.8978	0.9458	0.9212	0.9470
0.041	21.0	2331	0.4822	0.8861	0.9492	0.9165	0.9426
0.041	22.0	2442	0.4677	0.8855	0.9441	0.9139	0.9446
0.0253	23.0	2553	0.4945	0.8858	0.9466	0.9152	0.9473
0.0253	24.0	2664	0.4882	0.8794	0.9458	0.9114	0.9443
0.0253	25.0	2775	0.5073	0.8953	0.9424	0.9182	0.9448
0.0253	26.0	2886	0.5012	0.8986	0.9466	0.9220	0.9473
0.0253	27.0	2997	0.4975	0.8850	0.9458	0.9144	0.9456
0.0166	28.0	3108	0.4944	0.8879	0.9466	0.9163	0.9474
0.0166	29.0	3219	0.5137	0.8915	0.9466	0.9182	0.9472
0.0166	30.0	3330	0.4924	0.8890	0.9432	0.9153	0.9463
0.0166	31.0	3441	0.5129	0.8884	0.9508	0.9185	0.9466
0.0114	32.0	3552	0.5184	0.8940	0.9508	0.9216	0.9448
0.0114	33.0	3663	0.5237	0.9012	0.9508	0.9254	0.9463
0.0114	34.0	3774	0.5153	0.8937	0.9475	0.9198	0.9474
0.0114	35.0	3885	0.5182	0.8947	0.95	0.9215	0.9482
0.0114	36.0	3996	0.5311	0.8937	0.9475	0.9198	0.9481
0.0087	37.0	4107	0.5334	0.8935	0.9525	0.9221	0.9483
0.0087	38.0	4218	0.5397	0.8900	0.9466	0.9175	0.9475
0.0087	39.0	4329	0.5331	0.8941	0.9449	0.9188	0.9468
0.0087	40.0	4440	0.5381	0.8962	0.9441	0.9195	0.9460
0.0069	41.0	4551	0.5394	0.8937	0.9475	0.9198	0.9479
0.0069	42.0	4662	0.5516	0.8950	0.9466	0.9201	0.9461
0.0069	43.0	4773	0.5442	0.8949	0.9449	0.9192	0.9452
0.0069	44.0	4884	0.5427	0.8927	0.9449	0.9181	0.9482
0.0069	45.0	4995	0.5515	0.8907	0.9458	0.9174	0.9461
0.0058	46.0	5106	0.5448	0.8930	0.9475	0.9194	0.9481
0.0058	47.0	5217	0.5475	0.896	0.9492	0.9218	0.9487
0.0058	48.0	5328	0.5444	0.8907	0.9466	0.9178	0.9484
0.0058	49.0	5439	0.5452	0.8945	0.9483	0.9206	0.9487
0.005	50.0	5550	0.5445	0.8915	0.9466	0.9182	0.9486

Framework versions

• Transformers 4.20.1
• Pytorch 1.13.0+cu117
• Datasets 2.8.0
• Tokenizers 0.12.1