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reyhanemyr/
distilbert-base-cased-finetuned-ner-finetuned-ner

Name: distilbert-base-cased-finetuned-ner-finetuned-ner
Availability: InStock

generated_from_trainer

apache-2.0

autotrain_compatible

endpoints_compatible

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distilbert-base-cased-finetuned-ner-finetuned-ner

This model is a fine-tuned version of topmas/distilbert-base-cased-finetuned-ner on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.3797
Precision: 0.6051
Recall: 0.6048
F1: 0.6049
Accuracy: 0.9421

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: 200

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	17	0.4153	0.3326	0.1698	0.2249	0.8947
No log	2.0	34	0.3612	0.3385	0.2862	0.3102	0.9080
No log	3.0	51	0.3411	0.4266	0.3058	0.3562	0.9141
No log	4.0	68	0.3186	0.4545	0.3598	0.4017	0.9171
No log	5.0	85	0.2984	0.4233	0.4101	0.4166	0.9181
No log	6.0	102	0.2886	0.5030	0.3952	0.4427	0.9209
No log	7.0	119	0.2806	0.4884	0.4354	0.4604	0.9227
No log	8.0	136	0.2629	0.4994	0.4804	0.4898	0.9250
No log	9.0	153	0.2686	0.5052	0.4852	0.4950	0.9265
No log	10.0	170	0.2656	0.5278	0.4868	0.5065	0.9276
No log	11.0	187	0.2539	0.4924	0.5328	0.5118	0.9275
No log	12.0	204	0.2792	0.6093	0.4868	0.5412	0.9338
No log	13.0	221	0.2518	0.5263	0.5450	0.5355	0.9314
No log	14.0	238	0.2677	0.5171	0.5360	0.5264	0.9312
No log	15.0	255	0.2957	0.4611	0.5365	0.4960	0.9244
No log	16.0	272	0.2635	0.5638	0.5492	0.5564	0.9344
No log	17.0	289	0.2545	0.5052	0.5915	0.5450	0.9312
No log	18.0	306	0.2600	0.5660	0.5556	0.5607	0.9343
No log	19.0	323	0.2767	0.5880	0.5339	0.5596	0.9364
No log	20.0	340	0.2832	0.5782	0.5534	0.5656	0.9359
No log	21.0	357	0.2660	0.5621	0.5841	0.5729	0.9367
No log	22.0	374	0.2855	0.4915	0.6085	0.5437	0.9255
No log	23.0	391	0.2623	0.6019	0.5735	0.5874	0.9384
No log	24.0	408	0.2615	0.5711	0.5884	0.5796	0.9380
No log	25.0	425	0.2876	0.5545	0.5603	0.5574	0.9358
No log	26.0	442	0.2890	0.5796	0.5757	0.5776	0.9380
No log	27.0	459	0.2660	0.5731	0.5889	0.5809	0.9389
No log	28.0	476	0.2849	0.5818	0.5778	0.5798	0.9372
No log	29.0	493	0.2930	0.5817	0.5878	0.5847	0.9386
0.1365	30.0	510	0.2825	0.5310	0.6169	0.5707	0.9340
0.1365	31.0	527	0.2978	0.6088	0.5566	0.5815	0.9396
0.1365	32.0	544	0.2835	0.5860	0.6005	0.5932	0.9394
0.1365	33.0	561	0.2979	0.6040	0.5640	0.5833	0.9400
0.1365	34.0	578	0.2975	0.5842	0.5926	0.5884	0.9395
0.1365	35.0	595	0.2900	0.5910	0.5926	0.5918	0.9397
0.1365	36.0	612	0.2826	0.5751	0.6180	0.5958	0.9400
0.1365	37.0	629	0.2938	0.6038	0.5847	0.5941	0.9413
0.1365	38.0	646	0.3010	0.5871	0.5868	0.5869	0.9398
0.1365	39.0	663	0.3090	0.5720	0.5741	0.5730	0.9376
0.1365	40.0	680	0.3134	0.5872	0.5788	0.5830	0.9390
0.1365	41.0	697	0.2956	0.5781	0.6127	0.5949	0.9392
0.1365	42.0	714	0.3220	0.5854	0.5619	0.5734	0.9382
0.1365	43.0	731	0.2978	0.5560	0.6090	0.5813	0.9378
0.1365	44.0	748	0.3057	0.5973	0.5815	0.5893	0.9403
0.1365	45.0	765	0.3032	0.6193	0.5878	0.6031	0.9420
0.1365	46.0	782	0.3240	0.6094	0.5746	0.5915	0.9400
0.1365	47.0	799	0.2982	0.5999	0.6148	0.6073	0.9412
0.1365	48.0	816	0.3125	0.6052	0.5979	0.6015	0.9412
0.1365	49.0	833	0.3206	0.5729	0.5984	0.5854	0.9396
0.1365	50.0	850	0.3107	0.5823	0.6138	0.5976	0.9408
0.1365	51.0	867	0.3186	0.6107	0.5942	0.6023	0.9409
0.1365	52.0	884	0.3132	0.5971	0.5889	0.5930	0.9410
0.1365	53.0	901	0.3138	0.5709	0.6138	0.5915	0.9398
0.1365	54.0	918	0.3202	0.5868	0.5905	0.5886	0.9406
0.1365	55.0	935	0.3036	0.5892	0.6201	0.6043	0.9420
0.1365	56.0	952	0.3158	0.5783	0.6016	0.5897	0.9407
0.1365	57.0	969	0.3083	0.6024	0.6053	0.6039	0.9421
0.1365	58.0	986	0.3262	0.5831	0.5905	0.5868	0.9392
0.018	59.0	1003	0.3194	0.5783	0.6037	0.5907	0.9398
0.018	60.0	1020	0.3348	0.6130	0.5741	0.5929	0.9413
0.018	61.0	1037	0.3273	0.6053	0.6085	0.6069	0.9421
0.018	62.0	1054	0.3162	0.5936	0.6159	0.6045	0.9412
0.018	63.0	1071	0.3269	0.6105	0.5804	0.5951	0.9417
0.018	64.0	1088	0.3253	0.5941	0.6111	0.6025	0.9420
0.018	65.0	1105	0.3299	0.5884	0.6037	0.5960	0.9414
0.018	66.0	1122	0.3361	0.5970	0.6074	0.6022	0.9408
0.018	67.0	1139	0.3286	0.6032	0.6	0.6016	0.9407
0.018	68.0	1156	0.3384	0.5857	0.5984	0.5920	0.9404
0.018	69.0	1173	0.3371	0.5968	0.5984	0.5976	0.9409
0.018	70.0	1190	0.3352	0.5962	0.6148	0.6054	0.9409
0.018	71.0	1207	0.3318	0.5865	0.6153	0.6006	0.9409
0.018	72.0	1224	0.3494	0.5980	0.5878	0.5928	0.9408
0.018	73.0	1241	0.3303	0.6030	0.6148	0.6089	0.9415
0.018	74.0	1258	0.3455	0.5878	0.5931	0.5905	0.9403
0.018	75.0	1275	0.3311	0.5948	0.6291	0.6115	0.9411
0.018	76.0	1292	0.3396	0.6007	0.5963	0.5985	0.9419
0.018	77.0	1309	0.3372	0.5830	0.6153	0.5987	0.9404
0.018	78.0	1326	0.3484	0.5993	0.5942	0.5967	0.9408
0.018	79.0	1343	0.3408	0.6108	0.6011	0.6059	0.9422
0.018	80.0	1360	0.3462	0.6067	0.5910	0.5988	0.9415
0.018	81.0	1377	0.3505	0.6158	0.5995	0.6075	0.9418
0.018	82.0	1394	0.3347	0.6105	0.6153	0.6129	0.9424
0.018	83.0	1411	0.3449	0.5721	0.6212	0.5956	0.9390
0.018	84.0	1428	0.3428	0.5754	0.6296	0.6013	0.9398
0.018	85.0	1445	0.3456	0.5802	0.6222	0.6005	0.9399
0.018	86.0	1462	0.3524	0.6109	0.5989	0.6049	0.9418
0.018	87.0	1479	0.3563	0.6160	0.5899	0.6027	0.9421
0.018	88.0	1496	0.3571	0.6101	0.5937	0.6018	0.9423
0.0069	89.0	1513	0.3489	0.6076	0.5974	0.6025	0.9424
0.0069	90.0	1530	0.3418	0.5983	0.6217	0.6098	0.9421
0.0069	91.0	1547	0.3495	0.5851	0.5984	0.5917	0.9403
0.0069	92.0	1564	0.3483	0.6114	0.6085	0.6099	0.9428
0.0069	93.0	1581	0.3508	0.6026	0.6090	0.6058	0.9413
0.0069	94.0	1598	0.3499	0.6117	0.6026	0.6071	0.9415
0.0069	95.0	1615	0.3394	0.5999	0.6164	0.6080	0.9420
0.0069	96.0	1632	0.3610	0.6007	0.5995	0.6001	0.9410
0.0069	97.0	1649	0.3471	0.5979	0.6206	0.6090	0.9416
0.0069	98.0	1666	0.3559	0.5902	0.5974	0.5937	0.9408
0.0069	99.0	1683	0.3528	0.6140	0.6101	0.6120	0.9426
0.0069	100.0	1700	0.3552	0.5901	0.6153	0.6024	0.9415
0.0069	101.0	1717	0.3684	0.6201	0.5847	0.6019	0.9420
0.0069	102.0	1734	0.3432	0.6075	0.6011	0.6043	0.9424
0.0069	103.0	1751	0.3567	0.6270	0.5905	0.6082	0.9427
0.0069	104.0	1768	0.3539	0.6009	0.5958	0.5983	0.9417
0.0069	105.0	1785	0.3456	0.5976	0.6169	0.6071	0.9424
0.0069	106.0	1802	0.3551	0.6211	0.6037	0.6123	0.9429
0.0069	107.0	1819	0.3571	0.6035	0.6063	0.6049	0.9423
0.0069	108.0	1836	0.3512	0.5910	0.6222	0.6062	0.9413
0.0069	109.0	1853	0.3527	0.6031	0.6079	0.6055	0.9420
0.0069	110.0	1870	0.3593	0.6213	0.5894	0.6049	0.9421
0.0069	111.0	1887	0.3552	0.6179	0.5905	0.6039	0.9423
0.0069	112.0	1904	0.3552	0.5891	0.6138	0.6012	0.9410
0.0069	113.0	1921	0.3649	0.6290	0.5884	0.6080	0.9414
0.0069	114.0	1938	0.3677	0.6052	0.5968	0.6010	0.9412
0.0069	115.0	1955	0.3615	0.6067	0.5884	0.5974	0.9405
0.0069	116.0	1972	0.3659	0.5927	0.5937	0.5932	0.9404
0.0069	117.0	1989	0.3652	0.6099	0.5931	0.6014	0.9410
0.0042	118.0	2006	0.3668	0.6080	0.6048	0.6064	0.9419
0.0042	119.0	2023	0.3632	0.6173	0.6026	0.6099	0.9428
0.0042	120.0	2040	0.3548	0.6004	0.6090	0.6047	0.9414
0.0042	121.0	2057	0.3617	0.6077	0.6	0.6038	0.9417
0.0042	122.0	2074	0.3676	0.6119	0.5857	0.5985	0.9414
0.0042	123.0	2091	0.3621	0.5965	0.6085	0.6024	0.9406
0.0042	124.0	2108	0.3687	0.6063	0.6111	0.6087	0.9412
0.0042	125.0	2125	0.3635	0.6144	0.6011	0.6076	0.9417
0.0042	126.0	2142	0.3709	0.6020	0.5931	0.5975	0.9406
0.0042	127.0	2159	0.3571	0.6023	0.6217	0.6118	0.9415
0.0042	128.0	2176	0.3722	0.6229	0.5926	0.6074	0.9416
0.0042	129.0	2193	0.3647	0.6156	0.6085	0.6120	0.9420
0.0042	130.0	2210	0.3595	0.5844	0.6228	0.6030	0.9404
0.0042	131.0	2227	0.3750	0.6243	0.5820	0.6024	0.9421
0.0042	132.0	2244	0.3704	0.5986	0.5862	0.5924	0.9411
0.0042	133.0	2261	0.3660	0.6039	0.5952	0.5995	0.9413
0.0042	134.0	2278	0.3762	0.6178	0.5926	0.6049	0.9415
0.0042	135.0	2295	0.3784	0.5993	0.5825	0.5908	0.9405
0.0042	136.0	2312	0.3718	0.5925	0.6101	0.6011	0.9411
0.0042	137.0	2329	0.3832	0.6111	0.5862	0.5984	0.9415
0.0042	138.0	2346	0.3765	0.5919	0.6101	0.6008	0.9412
0.0042	139.0	2363	0.3834	0.6042	0.5984	0.6013	0.9413
0.0042	140.0	2380	0.3763	0.6042	0.6026	0.6034	0.9415
0.0042	141.0	2397	0.3814	0.6075	0.5905	0.5989	0.9416
0.0042	142.0	2414	0.3781	0.6027	0.5963	0.5995	0.9412
0.0042	143.0	2431	0.3829	0.6085	0.5921	0.6002	0.9414
0.0042	144.0	2448	0.3742	0.5907	0.6032	0.5969	0.9406
0.0042	145.0	2465	0.3740	0.6082	0.6037	0.6059	0.9418
0.0042	146.0	2482	0.3703	0.6081	0.6175	0.6128	0.9425
0.0042	147.0	2499	0.3781	0.6189	0.5921	0.6052	0.9422
0.0032	148.0	2516	0.3754	0.5985	0.6042	0.6014	0.9408
0.0032	149.0	2533	0.3712	0.6055	0.5984	0.6019	0.9416
0.0032	150.0	2550	0.3688	0.6095	0.6053	0.6074	0.9423
0.0032	151.0	2567	0.3736	0.6036	0.6058	0.6047	0.9419
0.0032	152.0	2584	0.3684	0.6165	0.6048	0.6106	0.9421
0.0032	153.0	2601	0.3677	0.6050	0.6053	0.6051	0.9421
0.0032	154.0	2618	0.3732	0.5877	0.5995	0.5935	0.9404
0.0032	155.0	2635	0.3717	0.6156	0.6	0.6077	0.9424
0.0032	156.0	2652	0.3711	0.5961	0.6037	0.5999	0.9410
0.0032	157.0	2669	0.3731	0.6018	0.6037	0.6027	0.9420
0.0032	158.0	2686	0.3712	0.6175	0.6063	0.6119	0.9426
0.0032	159.0	2703	0.3744	0.6041	0.6079	0.6060	0.9418
0.0032	160.0	2720	0.3788	0.5946	0.6085	0.6015	0.9412
0.0032	161.0	2737	0.3836	0.6108	0.5878	0.5991	0.9419
0.0032	162.0	2754	0.3748	0.6063	0.6127	0.6095	0.9418
0.0032	163.0	2771	0.3763	0.6243	0.6005	0.6122	0.9426
0.0032	164.0	2788	0.3772	0.6025	0.6111	0.6068	0.9413
0.0032	165.0	2805	0.3780	0.6044	0.6063	0.6054	0.9413
0.0032	166.0	2822	0.3788	0.6074	0.6016	0.6045	0.9417
0.0032	167.0	2839	0.3720	0.5984	0.6127	0.6055	0.9419
0.0032	168.0	2856	0.3767	0.5960	0.6111	0.6034	0.9418
0.0032	169.0	2873	0.3730	0.5948	0.6106	0.6026	0.9417
0.0032	170.0	2890	0.3709	0.6	0.6079	0.6039	0.9419
0.0032	171.0	2907	0.3695	0.5958	0.6106	0.6031	0.9417
0.0032	172.0	2924	0.3752	0.5878	0.6127	0.6	0.9413
0.0032	173.0	2941	0.3748	0.6007	0.6058	0.6033	0.9419
0.0032	174.0	2958	0.3742	0.6054	0.5958	0.6005	0.9419
0.0032	175.0	2975	0.3717	0.5996	0.6037	0.6016	0.9417
0.0032	176.0	2992	0.3741	0.5970	0.6011	0.5990	0.9417
0.0027	177.0	3009	0.3741	0.5986	0.6021	0.6004	0.9418
0.0027	178.0	3026	0.3768	0.5959	0.5984	0.5971	0.9416
0.0027	179.0	3043	0.3783	0.5925	0.5963	0.5944	0.9413
0.0027	180.0	3060	0.3793	0.6022	0.5952	0.5987	0.9418
0.0027	181.0	3077	0.3827	0.6061	0.5952	0.6006	0.9417
0.0027	182.0	3094	0.3834	0.6090	0.5926	0.6007	0.9422
0.0027	183.0	3111	0.3790	0.6037	0.6005	0.6021	0.9419
0.0027	184.0	3128	0.3838	0.6075	0.5905	0.5989	0.9419
0.0027	185.0	3145	0.3780	0.6009	0.6063	0.6036	0.9418
0.0027	186.0	3162	0.3785	0.5998	0.6058	0.6028	0.9418
0.0027	187.0	3179	0.3790	0.6031	0.5989	0.6010	0.9419
0.0027	188.0	3196	0.3808	0.6060	0.5915	0.5987	0.9418
0.0027	189.0	3213	0.3831	0.6095	0.5905	0.5998	0.9419
0.0027	190.0	3230	0.3820	0.6068	0.5952	0.6010	0.9419
0.0027	191.0	3247	0.3812	0.6024	0.5989	0.6007	0.9419
0.0027	192.0	3264	0.3808	0.6054	0.6	0.6027	0.9418
0.0027	193.0	3281	0.3809	0.6071	0.6	0.6035	0.9421
0.0027	194.0	3298	0.3803	0.6054	0.6016	0.6035	0.9421
0.0027	195.0	3315	0.3799	0.6048	0.6032	0.6040	0.9421
0.0027	196.0	3332	0.3805	0.6056	0.6021	0.6039	0.9421
0.0027	197.0	3349	0.3798	0.6053	0.6037	0.6045	0.9421
0.0027	198.0	3366	0.3795	0.6050	0.6053	0.6051	0.9421
0.0027	199.0	3383	0.3798	0.6048	0.6048	0.6048	0.9421
0.0027	200.0	3400	0.3797	0.6051	0.6048	0.6049	0.9421

Framework versions

Transformers 4.26.1
Pytorch 1.13.1+cu116
Datasets 2.10.1
Tokenizers 0.13.2

reyhanemyr/distilbert-base-cased-finetuned-ner-finetuned-ner

distilbert-base-cased-finetuned-ner-finetuned-ner

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

reyhanemyr/
distilbert-base-cased-finetuned-ner-finetuned-ner