Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	63	0.7317	0.6254	0.6378	0.6315	0.9019
No log	2.0	126	0.7668	0.6130	0.6482	0.6301	0.9
No log	3.0	189	0.7691	0.6123	0.6545	0.6327	0.8992
No log	4.0	252	0.7907	0.6061	0.6232	0.6145	0.8956
No log	5.0	315	0.8165	0.5798	0.6482	0.6121	0.8957
No log	6.0	378	0.7758	0.6008	0.6534	0.6260	0.8999
No log	7.0	441	0.8109	0.6018	0.6357	0.6183	0.8984
0.0018	8.0	504	0.7892	0.6018	0.6388	0.6197	0.8992
0.0018	9.0	567	0.8051	0.5878	0.6461	0.6156	0.8964
0.0018	10.0	630	0.7913	0.6123	0.6430	0.6273	0.8999
0.0018	11.0	693	0.8088	0.6012	0.6545	0.6267	0.8979
0.0018	12.0	756	0.8206	0.6072	0.6534	0.6295	0.8974
0.0018	13.0	819	0.8240	0.5858	0.6482	0.6155	0.8962
0.0018	14.0	882	0.8369	0.5961	0.6409	0.6177	0.8971
0.0018	15.0	945	0.8515	0.5951	0.6367	0.6152	0.8960
0.0012	16.0	1008	0.8743	0.5881	0.6096	0.5987	0.8949
0.0012	17.0	1071	0.8835	0.5945	0.6336	0.6134	0.8960
0.0012	18.0	1134	0.8633	0.5803	0.6409	0.6091	0.8946
0.0012	19.0	1197	0.8553	0.5899	0.6127	0.6011	0.8942
0.0012	20.0	1260	0.8715	0.5841	0.6232	0.6030	0.8938
0.0012	21.0	1323	0.8922	0.5881	0.6305	0.6086	0.8909
0.0012	22.0	1386	0.8716	0.5926	0.6482	0.6191	0.8935
0.0012	23.0	1449	0.8853	0.5915	0.6545	0.6214	0.8942
0.0008	24.0	1512	0.8494	0.6132	0.6388	0.6258	0.8973
0.0008	25.0	1575	0.8698	0.5901	0.6461	0.6168	0.8937
0.0008	26.0	1638	0.8622	0.5996	0.6409	0.6196	0.8946
0.0008	27.0	1701	0.8517	0.6057	0.6430	0.6238	0.8970
0.0008	28.0	1764	0.8696	0.6108	0.6388	0.6245	0.8977
0.0008	29.0	1827	0.8753	0.5979	0.6503	0.6230	0.8978
0.0008	30.0	1890	0.8519	0.6026	0.6409	0.6211	0.8973
0.0008	31.0	1953	0.8588	0.6086	0.6524	0.6297	0.8992
0.0007	32.0	2016	0.8713	0.5968	0.6305	0.6132	0.8970
0.0007	33.0	2079	0.8761	0.5982	0.6388	0.6179	0.8975
0.0007	34.0	2142	0.8733	0.5947	0.6357	0.6145	0.8967
0.0007	35.0	2205	0.8793	0.5996	0.6378	0.6181	0.8977
0.0007	36.0	2268	0.8959	0.5950	0.6503	0.6214	0.8971
0.0007	37.0	2331	0.8795	0.6078	0.6534	0.6298	0.8986
0.0007	38.0	2394	0.8856	0.6208	0.6597	0.6397	0.9
0.0007	39.0	2457	0.8897	0.6155	0.6534	0.6339	0.8992
0.0005	40.0	2520	0.8901	0.6098	0.6524	0.6304	0.8988
0.0005	41.0	2583	0.8881	0.6142	0.6482	0.6308	0.8984
0.0005	42.0	2646	0.8857	0.6193	0.6503	0.6344	0.8989
0.0005	43.0	2709	0.8911	0.6121	0.6524	0.6316	0.8973
0.0005	44.0	2772	0.8988	0.6015	0.6493	0.6245	0.8968
0.0005	45.0	2835	0.8927	0.6169	0.6472	0.6317	0.8978
0.0005	46.0	2898	0.8974	0.6137	0.6649	0.6383	0.8978
0.0005	47.0	2961	0.8991	0.6115	0.6555	0.6327	0.8968
0.0004	48.0	3024	0.9001	0.6087	0.6545	0.6308	0.8966
0.0004	49.0	3087	0.9015	0.6071	0.6566	0.6309	0.8968
0.0004	50.0	3150	0.8986	0.6109	0.6524	0.6310	0.8968
0.0004	51.0	3213	0.9014	0.6083	0.6597	0.6329	0.8984
0.0004	52.0	3276	0.9018	0.6091	0.6587	0.6329	0.8988
0.0004	53.0	3339	0.8991	0.6107	0.6534	0.6314	0.8986
0.0004	54.0	3402	0.9000	0.6084	0.6534	0.6301	0.8985
0.0004	55.0	3465	0.9015	0.6081	0.6545	0.6305	0.8988
0.0003	56.0	3528	0.9019	0.6054	0.6503	0.6271	0.8982
0.0003	57.0	3591	0.9011	0.6086	0.6524	0.6297	0.8982
0.0003	58.0	3654	0.9017	0.6080	0.6524	0.6294	0.8984
0.0003	59.0	3717	0.9019	0.6121	0.6555	0.6331	0.8985
0.0003	60.0	3780	0.9020	0.6105	0.6545	0.6317	0.8984

Framework versions

Transformers 4.45.2
Pytorch 2.4.1
Datasets 2.18.0
Tokenizers 0.20.0

roncmic/roberta-base-finetuned-ner

roberta-base-finetuned-ner

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

roncmic/
roberta-base-finetuned-ner