bert-finetuned-ner

This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0209
• Precision: 0.8249
• Recall: 0.8825
• F1: 0.8527
• Accuracy: 0.9946
• B-location-precision: 0.9446
• B-location-recall: 0.9653
• B-location-f1: 0.9549
• I-location-precision: 0.9358
• I-location-recall: 0.9745
• I-location-f1: 0.9548
• B-group-precision: 0.8819
• B-group-recall: 0.8485
• B-group-f1: 0.8649
• I-group-precision: 0.8879
• I-group-recall: 0.8358
• I-group-f1: 0.8610
• B-corporation-precision: 0.8475
• B-corporation-recall: 0.8552
• B-corporation-f1: 0.8514
• I-corporation-precision: 0.8158
• I-corporation-recall: 0.7294
• I-corporation-f1: 0.7702
• B-person-precision: 0.9583
• B-person-recall: 0.9742
• B-person-f1: 0.9662
• I-person-precision: 0.9596
• I-person-recall: 0.95
• I-person-f1: 0.9548
• B-creative-work-precision: 0.8102
• B-creative-work-recall: 0.7929
• B-creative-work-f1: 0.8014
• I-creative-work-precision: 0.8131
• I-creative-work-recall: 0.8354
• I-creative-work-f1: 0.8241
• B-product-precision: 0.8682
• B-product-recall: 0.7887
• B-product-f1: 0.8266
• I-product-precision: 0.8862
• I-product-recall: 0.8886
• I-product-f1: 0.8874
• Corporation-precision: 0.6972
• Corporation-recall: 0.7919
• Corporation-f1: 0.7415
• Corporation-number: 221
• Creative-work-precision: 0.6433
• Creative-work-recall: 0.7214
• Creative-work-f1: 0.6801
• Creative-work-number: 140
• Group-precision: 0.7465
• Group-recall: 0.8144
• Group-f1: 0.7790
• Group-number: 264
• Location-precision: 0.9026
• Location-recall: 0.9471
• Location-f1: 0.9243
• Location-number: 548
• Person-precision: 0.9101
• Person-recall: 0.9515
• Person-f1: 0.9304
• Person-number: 660
• Product-precision: 0.6908
• Product-recall: 0.7394
• Product-f1: 0.7143
• Product-number: 142

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: 0.0002
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy	B-location-precision	B-location-recall	B-location-f1	I-location-precision	I-location-recall	I-location-f1	B-group-precision	B-group-recall	B-group-f1	I-group-precision	I-group-recall	I-group-f1	B-corporation-precision	B-corporation-recall	B-corporation-f1	I-corporation-precision	I-corporation-recall	I-corporation-f1	B-person-precision	B-person-recall	B-person-f1	I-person-precision	I-person-recall	I-person-f1	B-creative-work-precision	B-creative-work-recall	B-creative-work-f1	I-creative-work-precision	I-creative-work-recall	I-creative-work-f1	B-product-precision	B-product-recall	B-product-f1	I-product-precision	I-product-recall	I-product-f1	Corporation-precision	Corporation-recall	Corporation-f1	Corporation-number	Creative-work-precision	Creative-work-recall	Creative-work-f1	Creative-work-number	Group-precision	Group-recall	Group-f1	Group-number	Location-precision	Location-recall	Location-f1	Location-number	Person-precision	Person-recall	Person-f1	Person-number	Product-precision	Product-recall	Product-f1	Product-number
No log	1.0	107	0.1175	0.5693	0.4076	0.4751	0.9701	0.6320	0.7646	0.6920	0.7752	0.3929	0.5215	1.0	0.0114	0.0225	0.6667	0.0176	0.0343	0.9787	0.2081	0.3433	nan	0.0	nan	0.8123	0.7409	0.7750	0.9117	0.555	0.6900	nan	0.0	nan	nan	0.0	nan	nan	0.0	nan	nan	0.0	nan	0.9787	0.2081	0.3433	221	0.0	0.0	0.0	140	0.3333	0.0152	0.0290	264	0.4682	0.6040	0.5275	548	0.6543	0.6424	0.6483	660	0.0	0.0	0.0	142
No log	2.0	214	0.0411	0.6931	0.7489	0.7199	0.9886	0.8194	0.9270	0.8699	0.8701	0.9214	0.8950	0.7919	0.5909	0.6768	0.6897	0.7625	0.7242	0.8297	0.6833	0.7494	0.8548	0.3118	0.4569	0.9139	0.9485	0.9309	0.8996	0.9075	0.9035	0.7541	0.3286	0.4577	0.7952	0.5091	0.6208	0.7407	0.5634	0.64	0.6740	0.8315	0.7445	0.6515	0.5837	0.6158	221	0.2941	0.2143	0.2479	140	0.5051	0.5682	0.5348	264	0.7617	0.8923	0.8218	548	0.8470	0.9227	0.8832	660	0.4091	0.5070	0.4528	142
No log	3.0	321	0.0209	0.8249	0.8825	0.8527	0.9946	0.9446	0.9653	0.9549	0.9358	0.9745	0.9548	0.8819	0.8485	0.8649	0.8879	0.8358	0.8610	0.8475	0.8552	0.8514	0.8158	0.7294	0.7702	0.9583	0.9742	0.9662	0.9596	0.95	0.9548	0.8102	0.7929	0.8014	0.8131	0.8354	0.8241	0.8682	0.7887	0.8266	0.8862	0.8886	0.8874	0.6972	0.7919	0.7415	221	0.6433	0.7214	0.6801	140	0.7465	0.8144	0.7790	264	0.9026	0.9471	0.9243	548	0.9101	0.9515	0.9304	660	0.6908	0.7394	0.7143	142

Framework versions

• Transformers 4.35.0
• Pytorch 2.1.0+cpu
• Datasets 2.14.6
• Tokenizers 0.14.1