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

Lilya/distilbert-base-uncased-ner-invoiceSenderRecipient_clean_inv_27_02

distilbert-base-uncased-ner-invoiceSenderRecipient_clean_inv_27_02