bertmulti-finetuned-token-reqadjzar

This model is a fine-tuned version of bert-base-multilingual-cased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.0215
• Precision: 0.3729
• Recall: 0.4783
• F1: 0.4190
• Accuracy: 0.8899

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: 0.0001
• train_batch_size: 8
• eval_batch_size: 8
• 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
0.71	1.0	24	0.6444	0.0	0.0	0.0	0.5695
0.5158	2.0	48	0.7621	0.0	0.0	0.0	0.6544
0.4702	3.0	72	0.4411	0.0	0.0	0.0	0.7961
0.3326	4.0	96	0.6711	0.0	0.0	0.0	0.7482
0.3134	5.0	120	0.4577	0.0	0.0	0.0	0.8160
0.2036	6.0	144	0.7211	0.0266	0.1087	0.0427	0.7232
0.1918	7.0	168	0.5596	0.0342	0.1087	0.0521	0.8284
0.1388	8.0	192	0.6039	0.0539	0.1957	0.0845	0.8242
0.1525	9.0	216	0.4642	0.0743	0.2391	0.1134	0.8436
0.0965	10.0	240	0.4855	0.1241	0.3913	0.1885	0.8758
0.0757	11.0	264	0.4353	0.0966	0.3043	0.1466	0.8543
0.0689	12.0	288	1.5112	0.0963	0.2826	0.1436	0.6792
0.1298	13.0	312	0.6653	0.1746	0.4783	0.2558	0.8430
0.0656	14.0	336	0.6830	0.0935	0.2826	0.1405	0.8443
0.041	15.0	360	0.6495	0.1231	0.3478	0.1818	0.8411
0.039	16.0	384	0.5259	0.1377	0.4130	0.2065	0.8585
0.031	17.0	408	0.6282	0.2054	0.5	0.2911	0.8479
0.0615	18.0	432	0.6453	0.1959	0.4130	0.2657	0.8559
0.0282	19.0	456	0.7038	0.1324	0.3913	0.1978	0.8462
0.0204	20.0	480	0.5582	0.1759	0.4130	0.2468	0.8669
0.0202	21.0	504	0.9386	0.1852	0.3261	0.2362	0.8224
0.0255	22.0	528	0.8781	0.1714	0.3913	0.2384	0.7990
0.0281	23.0	552	0.6537	0.1875	0.4565	0.2658	0.8833
0.0303	24.0	576	0.6140	0.2319	0.3478	0.2783	0.8735
0.0404	25.0	600	0.8073	0.2062	0.4348	0.2797	0.7813
0.0378	26.0	624	0.7925	0.1852	0.4348	0.2597	0.8601
0.0185	27.0	648	0.6131	0.1835	0.4348	0.2581	0.8879
0.0217	28.0	672	0.8677	0.2347	0.5	0.3194	0.8151
0.0147	29.0	696	0.6513	0.2	0.3913	0.2647	0.8622
0.0228	30.0	720	0.8354	0.2787	0.3696	0.3178	0.8490
0.0298	31.0	744	0.7063	0.1858	0.4565	0.2642	0.8858
0.018	32.0	768	0.9348	0.3188	0.4783	0.3826	0.8688
0.0179	33.0	792	0.7260	0.3014	0.4783	0.3697	0.8814
0.0139	34.0	816	1.6266	0.2427	0.5435	0.3356	0.7781
0.0435	35.0	840	0.5877	0.225	0.3913	0.2857	0.8827
0.0172	36.0	864	0.9602	0.1545	0.4130	0.2249	0.8379
0.02	37.0	888	0.7676	0.2473	0.5	0.3309	0.8696
0.012	38.0	912	0.6857	0.2118	0.3913	0.2748	0.8836
0.0088	39.0	936	0.8894	0.2857	0.5217	0.3692	0.8697
0.0055	40.0	960	0.7700	0.2340	0.4783	0.3143	0.8678
0.014	41.0	984	0.9191	0.2692	0.4565	0.3387	0.8396
0.0061	42.0	1008	0.8042	0.2785	0.4783	0.352	0.8753
0.0059	43.0	1032	1.0644	0.1959	0.4130	0.2657	0.8203
0.0032	44.0	1056	1.1174	0.2949	0.5	0.3710	0.8242
0.0057	45.0	1080	1.3623	0.2963	0.5217	0.3780	0.8346
0.019	46.0	1104	1.0958	0.1932	0.3696	0.2537	0.8465
0.0167	47.0	1128	0.9388	0.1848	0.3696	0.2464	0.8355
0.0131	48.0	1152	1.2771	0.1826	0.4565	0.2609	0.7805
0.0095	49.0	1176	1.0477	0.1944	0.4565	0.2727	0.8332
0.0058	50.0	1200	0.9822	0.2941	0.5435	0.3817	0.8407
0.0056	51.0	1224	1.1512	0.2360	0.4565	0.3111	0.8361
0.0045	52.0	1248	0.8875	0.2468	0.4130	0.3089	0.8693
0.0035	53.0	1272	0.9689	0.2346	0.4130	0.2992	0.8551
0.0066	54.0	1296	0.9921	0.2299	0.4348	0.3008	0.8587
0.0026	55.0	1320	0.8510	0.2817	0.4348	0.3419	0.8758
0.0033	56.0	1344	0.9234	0.2115	0.4783	0.2933	0.8436
0.0125	57.0	1368	1.0792	0.2308	0.3913	0.2903	0.8486
0.0034	58.0	1392	1.1353	0.2609	0.5217	0.3478	0.8274
0.0065	59.0	1416	1.3812	0.2738	0.5	0.3538	0.7993
0.0082	60.0	1440	1.0929	0.2233	0.5	0.3087	0.8429
0.0202	61.0	1464	0.9371	0.1709	0.4348	0.2454	0.8399
0.0063	62.0	1488	0.6318	0.2099	0.3696	0.2677	0.8543
0.0047	63.0	1512	0.8257	0.2018	0.5	0.2875	0.8514
0.0036	64.0	1536	0.8545	0.1963	0.4565	0.2745	0.8484
0.0027	65.0	1560	0.8684	0.2421	0.5	0.3262	0.8539
0.002	66.0	1584	0.8609	0.25	0.5	0.3333	0.8630
0.0022	67.0	1608	0.7618	0.2347	0.5	0.3194	0.8804
0.0026	68.0	1632	0.8460	0.23	0.5	0.3151	0.8654
0.0019	69.0	1656	0.7437	0.2857	0.5217	0.3692	0.8933
0.0027	70.0	1680	0.7911	0.2727	0.4565	0.3415	0.8898
0.0025	71.0	1704	0.8172	0.3333	0.4783	0.3929	0.8880
0.0037	72.0	1728	0.7807	0.2680	0.5652	0.3636	0.8873
0.0032	73.0	1752	0.9164	0.2683	0.4783	0.3438	0.8760
0.0092	74.0	1776	0.6410	0.2976	0.5435	0.3846	0.8836
0.0029	75.0	1800	0.7780	0.2857	0.5217	0.3692	0.8854
0.0017	76.0	1824	0.9096	0.2683	0.4783	0.3438	0.8656
0.0017	77.0	1848	0.8843	0.2911	0.5	0.368	0.8773
0.0019	78.0	1872	0.7888	0.2410	0.4348	0.3101	0.8613
0.0032	79.0	1896	0.9426	0.2241	0.5652	0.3210	0.8490
0.0019	80.0	1920	0.9566	0.25	0.3913	0.3051	0.8708
0.0017	81.0	1944	1.0507	0.2588	0.4783	0.3359	0.8669
0.0015	82.0	1968	1.1118	0.2174	0.4348	0.2899	0.8614
0.0014	83.0	1992	1.1422	0.2299	0.4348	0.3008	0.8548
0.0015	84.0	2016	1.1422	0.2716	0.4783	0.3465	0.8556
0.0013	85.0	2040	1.0874	0.2371	0.5	0.3217	0.8557
0.0015	86.0	2064	1.0420	0.2277	0.5	0.3129	0.8624
0.0013	87.0	2088	1.0851	0.2418	0.4783	0.3212	0.8579
0.0015	88.0	2112	1.1249	0.2556	0.5	0.3382	0.8622
0.0015	89.0	2136	1.0589	0.2667	0.5217	0.3529	0.8617
0.0014	90.0	2160	1.0879	0.2674	0.5	0.3485	0.8497
0.0019	91.0	2184	1.0425	0.2651	0.4783	0.3411	0.8551
0.0015	92.0	2208	1.0137	0.2716	0.4783	0.3465	0.8579
0.0015	93.0	2232	1.0084	0.2716	0.4783	0.3465	0.8619
0.0015	94.0	2256	1.0231	0.2727	0.5217	0.3582	0.8529
0.0014	95.0	2280	1.1031	0.3067	0.5	0.3802	0.8522
0.0014	96.0	2304	1.0001	0.2796	0.5652	0.3741	0.8642
0.0012	97.0	2328	1.0274	0.3253	0.5870	0.4186	0.8683
0.0015	98.0	2352	1.1420	0.3559	0.4565	0.4000	0.8579
0.0154	99.0	2376	0.8248	0.4706	0.5217	0.4948	0.8894
0.0041	100.0	2400	0.8580	0.2892	0.5217	0.3721	0.8768
0.0046	101.0	2424	1.0790	0.1792	0.4130	0.25	0.8623
0.0021	102.0	2448	1.0016	0.25	0.4348	0.3175	0.8766
0.0028	103.0	2472	0.8267	0.2899	0.4348	0.3478	0.8907
0.0026	104.0	2496	1.1740	0.2212	0.5	0.3067	0.8511
0.0018	105.0	2520	1.2264	0.1759	0.4130	0.2468	0.8389
0.0017	106.0	2544	1.1772	0.2451	0.5435	0.3378	0.8468
0.0014	107.0	2568	1.2155	0.2556	0.5	0.3382	0.8386
0.0018	108.0	2592	1.1990	0.2558	0.4783	0.3333	0.8411
0.0022	109.0	2616	1.0769	0.3425	0.5435	0.4202	0.8679
0.0016	110.0	2640	1.0793	0.3538	0.5	0.4144	0.8629
0.0019	111.0	2664	0.8828	0.2680	0.5652	0.3636	0.8823
0.0014	112.0	2688	1.0073	0.3548	0.4783	0.4074	0.8810
0.0016	113.0	2712	0.9562	0.3667	0.4783	0.4151	0.8827
0.0014	114.0	2736	0.9590	0.3438	0.4783	0.4	0.8802
0.0014	115.0	2760	1.0293	0.4	0.5217	0.4528	0.8814
0.0014	116.0	2784	1.0419	0.4068	0.5217	0.4571	0.8804
0.0012	117.0	2808	1.0451	0.4138	0.5217	0.4615	0.8805
0.005	118.0	2832	1.0514	0.4068	0.5217	0.4571	0.8803
0.0019	119.0	2856	1.0440	0.4068	0.5217	0.4571	0.8805
0.0015	120.0	2880	1.0782	0.4	0.5217	0.4528	0.8768
0.0015	121.0	2904	1.0736	0.4211	0.5217	0.4660	0.8765
0.0014	122.0	2928	1.0565	0.3934	0.5217	0.4486	0.8776
0.0013	123.0	2952	1.0496	0.4444	0.5217	0.48	0.8814
0.0012	124.0	2976	1.0805	0.4286	0.5217	0.4706	0.8805
0.0012	125.0	3000	1.1119	0.4211	0.5217	0.4660	0.8809
0.0013	126.0	3024	1.0880	0.4528	0.5217	0.4848	0.8812
0.0014	127.0	3048	1.0198	0.3729	0.4783	0.4190	0.8796
0.0013	128.0	3072	1.0028	0.4	0.5217	0.4528	0.8790
0.0014	129.0	3096	1.0229	0.3529	0.5217	0.4211	0.8835
0.0013	130.0	3120	1.0440	0.3380	0.5217	0.4103	0.8747
0.0013	131.0	3144	1.1109	0.4615	0.5217	0.4898	0.8781
0.0012	132.0	3168	1.1082	0.4706	0.5217	0.4948	0.8812
0.0013	133.0	3192	1.1031	0.4444	0.5217	0.48	0.8806
0.0011	134.0	3216	1.1345	0.3529	0.5217	0.4211	0.8713
0.0012	135.0	3240	1.1631	0.3485	0.5	0.4107	0.8716
0.0012	136.0	3264	1.1461	0.3429	0.5217	0.4138	0.8708
0.0012	137.0	3288	1.1592	0.4138	0.5217	0.4615	0.8683
0.0012	138.0	3312	1.0969	0.4138	0.5217	0.4615	0.8754
0.0013	139.0	3336	1.0575	0.3429	0.5217	0.4138	0.8787
0.0013	140.0	3360	1.0560	0.3636	0.5217	0.4286	0.8826
0.0013	141.0	3384	1.0525	0.3380	0.5217	0.4103	0.8796
0.0011	142.0	3408	1.0548	0.3380	0.5217	0.4103	0.8792
0.0013	143.0	3432	1.0593	0.3478	0.5217	0.4174	0.8802
0.0012	144.0	3456	1.0402	0.375	0.5217	0.4364	0.8827
0.0011	145.0	3480	1.0401	0.375	0.5217	0.4364	0.8828
0.0012	146.0	3504	1.0319	0.3810	0.5217	0.4404	0.8840
0.0012	147.0	3528	1.0328	0.3692	0.5217	0.4324	0.8838
0.0012	148.0	3552	1.1021	0.3433	0.5	0.4071	0.8730
0.0012	149.0	3576	1.0402	0.3485	0.5	0.4107	0.8817
0.0013	150.0	3600	0.9619	0.3086	0.5435	0.3937	0.8883
0.0014	151.0	3624	0.9578	0.3382	0.5	0.4035	0.8843
0.0012	152.0	3648	1.0303	0.3692	0.5217	0.4324	0.8830
0.0013	153.0	3672	1.0571	0.3934	0.5217	0.4486	0.8812
0.0012	154.0	3696	1.0793	0.3692	0.5217	0.4324	0.8812
0.0011	155.0	3720	1.0766	0.375	0.5217	0.4364	0.8803
0.0011	156.0	3744	1.0824	0.3934	0.5217	0.4486	0.8810
0.0012	157.0	3768	1.0841	0.4	0.5217	0.4528	0.8810
0.0011	158.0	3792	1.0866	0.4068	0.5217	0.4571	0.8812
0.0012	159.0	3816	1.1016	0.4	0.5217	0.4528	0.8808
0.0011	160.0	3840	1.1114	0.3810	0.5217	0.4404	0.8793
0.0013	161.0	3864	1.1427	0.2892	0.5217	0.3721	0.8577
0.0011	162.0	3888	1.0292	0.3582	0.5217	0.4248	0.8875
0.0012	163.0	3912	0.9894	0.375	0.5217	0.4364	0.8872
0.0011	164.0	3936	0.9877	0.3636	0.5217	0.4286	0.8870
0.0011	165.0	3960	0.9887	0.3692	0.5217	0.4324	0.8890
0.0012	166.0	3984	0.9874	0.3243	0.5217	0.4	0.8871
0.0011	167.0	4008	0.9992	0.3636	0.5217	0.4286	0.8896
0.0012	168.0	4032	0.9835	0.3692	0.5217	0.4324	0.8903
0.0011	169.0	4056	0.9918	0.3284	0.4783	0.3894	0.8910
0.0011	170.0	4080	0.9960	0.3438	0.4783	0.4	0.8914
0.0011	171.0	4104	1.0065	0.3729	0.4783	0.4190	0.8915
0.0012	172.0	4128	1.0266	0.3929	0.4783	0.4314	0.8908
0.0011	173.0	4152	1.0318	0.3929	0.4783	0.4314	0.8908
0.0011	174.0	4176	1.0329	0.3793	0.4783	0.4231	0.8908
0.0012	175.0	4200	1.0254	0.3860	0.4783	0.4272	0.8910
0.0011	176.0	4224	1.0183	0.4	0.4783	0.4356	0.8912
0.0012	177.0	4248	1.0205	0.3860	0.4783	0.4272	0.8909
0.0011	178.0	4272	1.0232	0.3793	0.4783	0.4231	0.8908
0.0011	179.0	4296	1.0246	0.3860	0.4783	0.4272	0.8908
0.0012	180.0	4320	1.0245	0.3793	0.4783	0.4231	0.8905
0.0012	181.0	4344	1.0223	0.375	0.4565	0.4118	0.8902
0.0011	182.0	4368	1.0169	0.3929	0.4783	0.4314	0.8894
0.0011	183.0	4392	1.0172	0.3929	0.4783	0.4314	0.8893
0.0012	184.0	4416	1.0147	0.3860	0.4783	0.4272	0.8894
0.0012	185.0	4440	1.0145	0.3860	0.4783	0.4272	0.8894
0.0011	186.0	4464	1.0128	0.3729	0.4783	0.4190	0.8897
0.0011	187.0	4488	1.0146	0.3729	0.4783	0.4190	0.8897
0.0011	188.0	4512	1.0160	0.3729	0.4783	0.4190	0.8897
0.0011	189.0	4536	1.0178	0.3729	0.4783	0.4190	0.8898
0.0011	190.0	4560	1.0185	0.3729	0.4783	0.4190	0.8898
0.0011	191.0	4584	1.0171	0.3793	0.4783	0.4231	0.8899
0.0011	192.0	4608	1.0179	0.3729	0.4783	0.4190	0.8898
0.0011	193.0	4632	1.0196	0.3793	0.4783	0.4231	0.8899
0.0012	194.0	4656	1.0188	0.3793	0.4783	0.4231	0.8899
0.0011	195.0	4680	1.0185	0.3793	0.4783	0.4231	0.8899
0.0011	196.0	4704	1.0194	0.3860	0.4783	0.4272	0.8898
0.0011	197.0	4728	1.0206	0.3729	0.4783	0.4190	0.8900
0.0011	198.0	4752	1.0207	0.3729	0.4783	0.4190	0.8900
0.0012	199.0	4776	1.0215	0.3729	0.4783	0.4190	0.8899
0.0011	200.0	4800	1.0215	0.3729	0.4783	0.4190	0.8899

Framework versions

• Transformers 4.31.0.dev0
• Pytorch 2.0.0
• Datasets 2.1.0
• Tokenizers 0.13.3