distilbert-base-cased-finetuned-ner_0301_J_DATA

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: 0.0356
• Precision: 0.9588
• Recall: 0.9664
• F1: 0.9626
• Accuracy: 0.9933

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: 2
• eval_batch_size: 2
• 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
0.2272	1.0	705	0.0711	0.8795	0.9249	0.9016	0.9777
0.0551	2.0	1410	0.0439	0.9222	0.9563	0.9389	0.9878
0.0253	3.0	2115	0.0397	0.9312	0.9563	0.9436	0.9898
0.0277	4.0	2820	0.0500	0.9492	0.9641	0.9566	0.9898
0.018	5.0	3525	0.0414	0.9524	0.9652	0.9588	0.9902
0.0154	6.0	4230	0.0383	0.9397	0.9608	0.9501	0.9892
0.0133	7.0	4935	0.0454	0.9408	0.9619	0.9512	0.9888
0.0073	8.0	5640	0.0343	0.9496	0.9709	0.9601	0.9917
0.0071	9.0	6345	0.0295	0.9524	0.9652	0.9588	0.9923
0.0053	10.0	7050	0.0307	0.9449	0.9619	0.9533	0.9929
0.0048	11.0	7755	0.0221	0.9304	0.9585	0.9442	0.9935
0.0022	12.0	8460	0.0338	0.9450	0.9630	0.9539	0.9923
0.0024	13.0	9165	0.0263	0.9578	0.9675	0.9626	0.9938
0.0017	14.0	9870	0.0345	0.9547	0.9697	0.9622	0.9935
0.0016	15.0	10575	0.0356	0.9588	0.9664	0.9626	0.9933

Framework versions

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