Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0157	0.06	500	0.0286	0.8569	0.8605	0.8587	0.9908
0.0156	0.11	1000	0.0287	0.8340	0.8940	0.8630	0.9910
0.0141	0.17	1500	0.0296	0.8368	0.8853	0.8604	0.9908
0.014	0.23	2000	0.0296	0.8356	0.8915	0.8627	0.9910
0.0144	0.28	2500	0.0306	0.8310	0.8896	0.8593	0.9906
0.0136	0.34	3000	0.0290	0.8384	0.8842	0.8607	0.9910
0.0134	0.4	3500	0.0306	0.8514	0.8779	0.8645	0.9912
0.0138	0.46	4000	0.0307	0.8475	0.8790	0.8630	0.9910
0.0139	0.51	4500	0.0301	0.8208	0.9002	0.8587	0.9908
0.014	0.57	5000	0.0320	0.8307	0.8981	0.8631	0.9909
0.0155	0.63	5500	0.0307	0.8329	0.8992	0.8648	0.9909
0.0154	0.68	6000	0.0268	0.8403	0.8971	0.8677	0.9913
0.0178	0.74	6500	0.0269	0.8548	0.8869	0.8705	0.9916
0.0177	0.8	7000	0.0268	0.8552	0.8904	0.8725	0.9917
0.0178	0.85	7500	0.0267	0.8498	0.8972	0.8729	0.9917
0.017	0.91	8000	0.0259	0.8517	0.8969	0.8737	0.9917
0.0176	0.97	8500	0.0249	0.8523	0.8921	0.8717	0.9916
0.0157	1.02	9000	0.0274	0.8535	0.8990	0.8757	0.9918
0.0134	1.08	9500	0.0293	0.8375	0.9060	0.8704	0.9913
0.0132	1.14	10000	0.0278	0.8648	0.8864	0.8755	0.9919
0.0135	1.19	10500	0.0273	0.8540	0.8958	0.8744	0.9917
0.0133	1.25	11000	0.0277	0.8442	0.9034	0.8728	0.9917
0.0138	1.31	11500	0.0276	0.8484	0.9035	0.8751	0.9917
0.0141	1.37	12000	0.0274	0.8501	0.9016	0.8751	0.9918
0.0137	1.42	12500	0.0274	0.8529	0.9010	0.8763	0.9918
0.014	1.48	13000	0.0269	0.8509	0.9022	0.8758	0.9919
0.0141	1.54	13500	0.0260	0.8653	0.8926	0.8787	0.9920
0.0149	1.59	14000	0.0258	0.8521	0.9048	0.8777	0.9919
0.0149	1.65	14500	0.0257	0.8607	0.8980	0.8790	0.9921
0.0152	1.71	15000	0.0257	0.8596	0.9001	0.8794	0.9920
0.015	1.76	15500	0.0257	0.8556	0.9032	0.8788	0.9920
0.0157	1.82	16000	0.0248	0.8620	0.8993	0.8802	0.9922
0.0158	1.88	16500	0.0251	0.8573	0.9036	0.8798	0.9921
0.0163	1.93	17000	0.0248	0.8579	0.9034	0.8800	0.9921
0.017	1.99	17500	0.0245	0.8602	0.9015	0.8804	0.9921

Framework versions

Transformers 4.15.0
Pytorch 1.10.0
Datasets 2.3.2
Tokenizers 0.10.3

Lilya/distilbert-base-uncased-ner-invoiceSenderRecipient-all-inv-26-12

distilbert-base-uncased-ner-invoiceSenderRecipient-all-inv-26-12

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Lilya/
distilbert-base-uncased-ner-invoiceSenderRecipient-all-inv-26-12