Products_NER8

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

• Loss: 0.2028
• Precision: 0.9227
• Recall: 0.9267
• F1: 0.9247
• Accuracy: 0.9446

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: 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: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1326	1.0	1235	0.1052	0.8887	0.9121	0.9003	0.9386
0.0959	2.0	2470	0.0927	0.8742	0.9085	0.8910	0.9417
0.0824	3.0	3705	0.0931	0.8970	0.9174	0.9070	0.9433
0.079	4.0	4940	0.0948	0.9067	0.9209	0.9137	0.9432
0.0762	5.0	6175	0.0962	0.8963	0.9179	0.9070	0.9437
0.0721	6.0	7410	0.1030	0.9095	0.9223	0.9159	0.9443
0.0683	7.0	8645	0.1070	0.9128	0.9233	0.9181	0.9439
0.0637	8.0	9880	0.1178	0.9157	0.9240	0.9199	0.9439
0.059	9.0	11115	0.1215	0.9176	0.9248	0.9212	0.9443
0.0527	10.0	12350	0.1367	0.9189	0.9247	0.9218	0.9438
0.0475	11.0	13585	0.1504	0.9199	0.9250	0.9224	0.9441
0.0431	12.0	14820	0.1484	0.9207	0.9259	0.9233	0.9446
0.0389	13.0	16055	0.1706	0.9224	0.9267	0.9246	0.9446
0.0368	14.0	17290	0.1847	0.9223	0.9265	0.9244	0.9445
0.0351	15.0	18525	0.2028	0.9227	0.9267	0.9247	0.9446

Framework versions

• Transformers 4.33.0
• Pytorch 1.13.1+cu117
• Datasets 2.1.0
• Tokenizers 0.13.3