Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0363	0.01	500	0.0338	0.7846	0.7969	0.7907	0.9884
0.0392	0.02	1000	0.0346	0.7665	0.8211	0.7929	0.9881
0.0363	0.04	1500	0.0347	0.7701	0.8075	0.7884	0.9880
0.0396	0.05	2000	0.0347	0.7454	0.8375	0.7888	0.9879
0.0366	0.06	2500	0.0350	0.7519	0.8345	0.7911	0.9879
0.0382	0.07	3000	0.0356	0.7500	0.8434	0.7939	0.9877
0.0424	0.09	3500	0.0358	0.7517	0.8287	0.7883	0.9877
0.0385	0.1	4000	0.0352	0.7605	0.8225	0.7903	0.9880
0.0382	0.11	4500	0.0361	0.7494	0.8159	0.7813	0.9874
0.0372	0.12	5000	0.0345	0.7817	0.8044	0.7929	0.9885
0.0377	0.14	5500	0.0346	0.7749	0.8238	0.7986	0.9884
0.0383	0.15	6000	0.0359	0.7568	0.8341	0.7936	0.9879
0.0372	0.16	6500	0.0356	0.7548	0.8356	0.7932	0.9879
0.0371	0.17	7000	0.0352	0.7540	0.8477	0.7981	0.9880
0.0368	0.19	7500	0.0349	0.7662	0.8310	0.7973	0.9881
0.0388	0.2	8000	0.0339	0.7648	0.8336	0.7977	0.9883
0.0368	0.21	8500	0.0336	0.7729	0.8305	0.8006	0.9886
0.0389	0.22	9000	0.0340	0.7750	0.8208	0.7972	0.9884
0.0384	0.24	9500	0.0349	0.7549	0.8499	0.7996	0.9880
0.0376	0.25	10000	0.0358	0.7531	0.8390	0.7938	0.9875
0.0354	0.26	10500	0.0346	0.7650	0.8318	0.7970	0.9882
0.0358	0.27	11000	0.0338	0.7694	0.8397	0.8030	0.9886
0.0389	0.28	11500	0.0341	0.7586	0.8502	0.8018	0.9882
0.0383	0.3	12000	0.0342	0.7688	0.8275	0.7971	0.9881
0.0355	0.31	12500	0.0337	0.7783	0.8281	0.8024	0.9885
0.0372	0.32	13000	0.0338	0.7703	0.8399	0.8036	0.9884
0.0369	0.33	13500	0.0331	0.7683	0.8427	0.8038	0.9886
0.0361	0.35	14000	0.0336	0.7699	0.8322	0.7999	0.9885
0.0361	0.36	14500	0.0336	0.7735	0.8390	0.8049	0.9885
0.0372	0.37	15000	0.0333	0.7747	0.8343	0.8034	0.9887
0.0366	0.38	15500	0.0343	0.7646	0.8468	0.8036	0.9883
0.0345	0.4	16000	0.0333	0.7790	0.8334	0.8053	0.9887
0.0363	0.41	16500	0.0329	0.7783	0.8301	0.8034	0.9887
0.0348	0.42	17000	0.0341	0.7626	0.8533	0.8054	0.9884
0.0391	0.43	17500	0.0324	0.7873	0.8295	0.8079	0.9889
0.0344	0.45	18000	0.0334	0.7769	0.8369	0.8058	0.9887
0.0378	0.46	18500	0.0337	0.7741	0.8394	0.8054	0.9886
0.035	0.47	19000	0.0328	0.7827	0.8323	0.8067	0.9888
0.0351	0.48	19500	0.0327	0.7815	0.8371	0.8083	0.9889
0.037	0.5	20000	0.0328	0.7793	0.8388	0.8079	0.9888
0.0346	0.51	20500	0.0325	0.7804	0.8416	0.8099	0.9890
0.0364	0.52	21000	0.0323	0.7861	0.8339	0.8093	0.9889
0.0356	0.53	21500	0.0327	0.7729	0.8510	0.8101	0.9889
0.0346	0.54	22000	0.0325	0.7791	0.8407	0.8087	0.9889
0.0342	0.56	22500	0.0334	0.7790	0.8443	0.8104	0.9889
0.0368	0.57	23000	0.0322	0.7869	0.8323	0.8089	0.9890
0.0371	0.58	23500	0.0320	0.7890	0.8356	0.8116	0.9891
0.0344	0.59	24000	0.0321	0.7910	0.8321	0.8110	0.9892
0.0342	0.61	24500	0.0319	0.7881	0.8356	0.8111	0.9892
0.0339	0.62	25000	0.0320	0.7889	0.8317	0.8097	0.9892
0.0347	0.63	25500	0.0316	0.7909	0.8347	0.8122	0.9892
0.034	0.64	26000	0.0318	0.7887	0.8324	0.8100	0.9891
0.0347	0.66	26500	0.0317	0.7791	0.8525	0.8141	0.9891
0.0345	0.67	27000	0.0318	0.7870	0.8384	0.8119	0.9892
0.0347	0.68	27500	0.0317	0.7903	0.8426	0.8157	0.9893
0.0371	0.69	28000	0.0311	0.7965	0.8332	0.8144	0.9894
0.0338	0.71	28500	0.0316	0.7863	0.8442	0.8142	0.9892
0.0352	0.72	29000	0.0315	0.7810	0.8537	0.8157	0.9892
0.0344	0.73	29500	0.0314	0.7953	0.8353	0.8148	0.9894
0.0322	0.74	30000	0.0320	0.7836	0.8449	0.8131	0.9891
0.0355	0.76	30500	0.0312	0.7877	0.8480	0.8167	0.9894
0.035	0.77	31000	0.0313	0.7864	0.8504	0.8171	0.9893
0.0346	0.78	31500	0.0310	0.7931	0.8424	0.8170	0.9895
0.0339	0.79	32000	0.0316	0.7857	0.8501	0.8166	0.9893
0.033	0.8	32500	0.0311	0.7975	0.8406	0.8185	0.9895
0.0337	0.82	33000	0.0314	0.7886	0.8457	0.8162	0.9894
0.0357	0.83	33500	0.0311	0.7923	0.8437	0.8172	0.9894
0.0348	0.84	34000	0.0312	0.7909	0.8490	0.8189	0.9894
0.0343	0.85	34500	0.0311	0.7856	0.8528	0.8179	0.9893
0.0323	0.87	35000	0.0311	0.7884	0.8505	0.8183	0.9894
0.0329	0.88	35500	0.0307	0.7981	0.8399	0.8185	0.9896
0.0324	0.89	36000	0.0313	0.7830	0.8576	0.8186	0.9893
0.0336	0.9	36500	0.0312	0.7836	0.8566	0.8185	0.9893
0.0327	0.92	37000	0.0309	0.7887	0.8501	0.8182	0.9895
0.0338	0.93	37500	0.0312	0.7887	0.8514	0.8188	0.9894
0.0327	0.94	38000	0.0311	0.7873	0.8534	0.8190	0.9894
0.0326	0.95	38500	0.0308	0.7953	0.8459	0.8198	0.9895
0.0338	0.97	39000	0.0307	0.7932	0.8488	0.8201	0.9895
0.0354	0.98	39500	0.0308	0.7916	0.8502	0.8198	0.9895
0.0313	0.99	40000	0.0309	0.7897	0.8523	0.8198	0.9895

Framework versions

Transformers 4.22.0
Pytorch 1.10.0
Tokenizers 0.12.1

Lilya/gpt2-ner-invoiceSenderRecipient_all_inv_03_01

gpt2-ner-invoiceSenderRecipient_all_inv_03_01

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Lilya/
gpt2-ner-invoiceSenderRecipient_all_inv_03_01