distilbert-base-uncased-ner-invoiceSenderRecipient_clean_inv_27_02

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

• Loss: 0.0160
• Precision: 0.9514
• Recall: 0.9593
• F1: 0.9553
• Accuracy: 0.9953

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: 2
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0078	0.09	500	0.0213	0.9269	0.9514	0.9390	0.9937
0.008	0.17	1000	0.0230	0.9246	0.9516	0.9379	0.9935
0.007	0.26	1500	0.0234	0.9400	0.9478	0.9439	0.9942
0.0065	0.34	2000	0.0238	0.9280	0.9537	0.9406	0.9936
0.0071	0.43	2500	0.0221	0.9291	0.9570	0.9428	0.9939
0.007	0.52	3000	0.0210	0.9393	0.9457	0.9425	0.9941
0.0072	0.6	3500	0.0197	0.9448	0.9490	0.9469	0.9945
0.0071	0.69	4000	0.0196	0.9400	0.9555	0.9477	0.9945
0.0109	0.77	4500	0.0178	0.9458	0.9499	0.9478	0.9946
0.01	0.86	5000	0.0191	0.9443	0.9489	0.9466	0.9945
0.0103	0.95	5500	0.0181	0.9466	0.9530	0.9498	0.9947
0.0081	1.03	6000	0.0191	0.9448	0.9578	0.9512	0.9948
0.0102	1.12	6500	0.0171	0.9454	0.9550	0.9502	0.9948
0.01	1.21	7000	0.0178	0.9460	0.9584	0.9521	0.9949
0.0107	1.29	7500	0.0164	0.9498	0.9552	0.9525	0.9950
0.0107	1.38	8000	0.0166	0.9461	0.9596	0.9528	0.9950
0.0095	1.46	8500	0.0170	0.9402	0.9626	0.9513	0.9949
0.0097	1.55	9000	0.0161	0.9455	0.9595	0.9524	0.9950
0.01	1.64	9500	0.0159	0.9502	0.9583	0.9542	0.9952
0.01	1.72	10000	0.0160	0.9488	0.9598	0.9543	0.9952
0.0095	1.81	10500	0.0157	0.9502	0.9602	0.9552	0.9953
0.0087	1.89	11000	0.0160	0.9514	0.9593	0.9553	0.9953
0.0089	1.98	11500	0.0160	0.9502	0.9608	0.9555	0.9953

Framework versions

• Transformers 4.15.0
• Pytorch 1.13.1
• Datasets 2.3.2
• Tokenizers 0.10.3