bertweet-2020-Q1-filtered

This model is a fine-tuned version of vinai/bertweet-base on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 2.6866

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: 1e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 1400
• training_steps: 2400000

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.07	8000	2.6350
2.7848	0.13	16000	2.6556
2.7848	0.2	24000	2.6695
2.7545	0.26	32000	2.6713
2.7545	0.33	40000	2.7089
2.7717	0.39	48000	2.7144
2.7717	0.46	56000	2.7240
2.8043	0.52	64000	2.7499
2.8043	0.59	72000	2.7704
2.8401	0.65	80000	2.7820
2.8401	0.72	88000	2.8068
2.8723	0.78	96000	2.8150
2.8723	0.85	104000	2.8410
2.9004	0.91	112000	2.8657
2.9004	0.98	120000	2.8826
2.9396	1.04	128000	2.9071
2.9396	1.11	136000	2.9490
2.9801	1.17	144000	2.9515
2.9801	1.24	152000	2.9863
3.0173	1.3	160000	2.9916
3.0173	1.37	168000	3.0231
3.0674	1.44	176000	3.0447
3.0674	1.5	184000	3.0638
3.1059	1.57	192000	3.0945
3.1059	1.63	200000	3.1008
3.1283	1.7	208000	3.1257
3.1283	1.76	216000	3.1262
3.1684	1.83	224000	3.1523
3.1684	1.89	232000	3.1842
3.1966	1.96	240000	3.1820
3.1966	2.02	248000	3.1976
3.2055	2.09	256000	3.2013
3.2055	2.15	264000	3.2197
3.2186	2.22	272000	3.2259
3.2186	2.28	280000	3.2410
3.2518	2.35	288000	3.2449
3.2518	2.41	296000	3.2686
3.2705	2.48	304000	3.2702
3.2705	2.54	312000	3.2716
3.2677	2.61	320000	3.2935
3.2677	2.67	328000	3.2942
3.2955	2.74	336000	3.3044
3.2955	2.8	344000	3.3110
3.2966	2.87	352000	3.3053
3.2966	2.94	360000	3.3276
3.311	3.0	368000	3.3256
3.311	3.07	376000	3.3292
3.3217	3.13	384000	3.3335
3.3217	3.2	392000	3.3160
3.3145	3.26	400000	3.3378
3.3145	3.33	408000	3.3307
3.3246	3.39	416000	3.3427
3.3246	3.46	424000	3.3543
3.3131	3.52	432000	3.3405
3.3131	3.59	440000	3.3361
3.3266	3.65	448000	3.3704
3.3266	3.72	456000	3.3549
3.3358	3.78	464000	3.3603
3.3358	3.85	472000	3.3642
3.3385	3.91	480000	3.3573
3.3385	3.98	488000	3.3658
3.3375	4.04	496000	3.3459
3.3375	4.11	504000	3.3703
3.3237	4.17	512000	3.3564
3.3237	4.24	520000	3.3553
3.34	4.31	528000	3.3576
3.34	4.37	536000	3.3548
3.3247	4.44	544000	3.3526
3.3247	4.5	552000	3.3674
3.318	4.57	560000	3.3608
3.318	4.63	568000	3.3527
3.3318	4.7	576000	3.3600
3.3318	4.76	584000	3.3662
3.3211	4.83	592000	3.3603
3.3211	4.89	600000	3.3640
3.3344	4.96	608000	3.3760
3.3344	5.02	616000	3.3876
3.331	5.09	624000	3.3519
3.331	5.15	632000	3.3734
3.3293	5.22	640000	3.3735
3.3293	5.28	648000	3.3703
3.3317	5.35	656000	3.3826
3.3317	5.41	664000	3.3826
3.3291	5.48	672000	3.3919
3.3291	5.54	680000	3.3786
3.3423	5.61	688000	3.3775
3.3423	5.68	696000	3.3734
3.3364	5.74	704000	3.3725
3.3364	5.81	712000	3.3855
3.347	5.87	720000	3.3774
3.347	5.94	728000	3.3717
3.3311	6.0	736000	3.3929
3.3311	6.07	744000	3.3899
3.3445	6.13	752000	3.3985
3.3445	6.2	760000	3.3866
3.345	6.26	768000	3.3943
3.345	6.33	776000	3.3734
3.3427	6.39	784000	3.3832
3.3427	6.46	792000	3.3966
3.3406	6.52	800000	3.3892
3.3406	6.59	808000	3.3904
3.3406	6.65	816000	3.3867
3.3406	6.72	824000	3.3902
3.3354	6.78	832000	3.3718
3.3354	6.85	840000	3.3831
3.3521	6.91	848000	3.3909
3.3521	6.98	856000	3.3799
3.3538	7.05	864000	3.3828
3.3538	7.11	872000	3.3785
3.3363	7.18	880000	3.3993
3.3363	7.24	888000	3.3850
3.3341	7.31	896000	3.3932
3.3341	7.37	904000	3.3981
3.3458	7.44	912000	3.3936
3.3458	7.5	920000	3.4032
3.3327	7.57	928000	3.3852
3.3327	7.63	936000	3.3865
3.3507	7.7	944000	3.3900
3.3507	7.76	952000	3.3772
3.3493	7.83	960000	3.3887
3.3493	7.89	968000	3.3951
3.3412	7.96	976000	3.3833
3.3412	8.02	984000	3.3816
3.3232	8.09	992000	3.3752
3.3232	8.15	1000000	3.3845
3.333	8.22	1008000	3.3907
3.333	8.28	1016000	3.3823
3.3449	8.35	1024000	3.3725
3.3449	8.41	1032000	3.3797
3.3336	8.48	1040000	3.3878
3.3336	8.55	1048000	3.3845
3.3307	8.61	1056000	3.3907
3.3307	8.68	1064000	3.3858
3.3267	8.74	1072000	3.3952
3.3267	8.81	1080000	3.3914
3.335	8.87	1088000	3.3904
3.335	8.94	1096000	3.3895
3.3411	9.0	1104000	3.3959
3.3411	9.07	1112000	3.3915
3.3324	9.13	1120000	3.4030
3.3324	9.2	1128000	3.4084
3.3297	9.26	1136000	3.4023
3.3297	9.33	1144000	3.3967
3.3492	9.39	1152000	3.3931
3.3492	9.46	1160000	3.4065
3.3317	9.52	1168000	3.3905
3.3317	9.59	1176000	3.4021
3.3447	9.65	1184000	3.4001
3.3447	9.72	1192000	3.3943
3.3377	9.78	1200000	3.3971
3.3377	9.85	1208000	3.3946
3.3486	9.92	1216000	3.3924
3.3486	9.98	1224000	3.3983
3.3471	10.05	1232000	3.4141
3.3471	10.11	1240000	3.4220
3.3457	10.18	1248000	3.4085
3.3457	10.24	1256000	3.4243
3.3278	10.31	1264000	3.4058
3.3278	10.37	1272000	3.4033
3.325	10.44	1280000	3.3867
3.325	10.5	1288000	3.3879
3.3248	10.57	1296000	3.3801
3.3248	10.63	1304000	3.4027
3.3217	10.7	1312000	3.3781
3.3217	10.76	1320000	3.3871
3.3227	10.83	1328000	3.3861
3.3227	10.89	1336000	3.3789
3.3259	10.96	1344000	3.3865
3.3259	11.02	1352000	3.3863
3.3094	11.09	1360000	3.3827
3.3094	11.15	1368000	3.3880
3.3128	11.22	1376000	3.3652
3.3128	11.29	1384000	3.3813
3.3088	11.35	1392000	3.3853
3.3088	11.42	1400000	3.3709
3.3067	11.48	1408000	3.3831
3.3067	11.55	1416000	3.3703
3.311	11.61	1424000	3.3696
3.311	11.68	1432000	3.3769
3.3048	11.74	1440000	3.3740
3.3048	11.81	1448000	3.3731
3.3055	11.87	1456000	3.3655
3.3055	11.94	1464000	3.3697
3.3105	12.0	1472000	3.3742
3.3105	12.07	1480000	3.3614
3.2977	12.13	1488000	3.3705
3.2977	12.2	1496000	3.3746
3.2999	12.26	1504000	3.3691
3.2999	12.33	1512000	3.3745
3.2983	12.39	1520000	3.3717
3.2983	12.46	1528000	3.3682
3.2957	12.52	1536000	3.3693
3.2957	12.59	1544000	3.3764
3.293	12.65	1552000	3.3691
3.293	12.72	1560000	3.3802
3.2919	12.79	1568000	3.3626
3.2919	12.85	1576000	3.3604
3.3023	12.92	1584000	3.3749
3.3023	12.98	1592000	3.3688
3.2988	13.05	1600000	3.3666
3.2988	13.11	1608000	3.3695
3.2924	13.18	1616000	3.3650
3.2924	13.24	1624000	3.3651
3.2958	13.31	1632000	3.3692
3.2958	13.37	1640000	3.3855
3.2918	13.44	1648000	3.3706
3.2918	13.5	1656000	3.3680
3.2948	13.57	1664000	3.3534
3.2948	13.63	1672000	3.3699
3.2996	13.7	1680000	3.3733
3.2996	13.76	1688000	3.3764
3.2999	13.83	1696000	3.3793
3.2999	13.89	1704000	3.3683
3.291	13.96	1712000	3.3654
3.291	14.02	1720000	3.3721
3.2952	14.09	1728000	3.3674
3.2952	14.16	1736000	3.3762
3.2866	14.22	1744000	3.3699
3.2866	14.29	1752000	3.3690
3.2825	14.35	1760000	3.3653
3.2825	14.42	1768000	3.3687
3.2825	14.48	1776000	3.3618
3.2825	14.55	1784000	3.3609
3.2744	14.61	1792000	3.3552
3.2744	14.68	1800000	3.3549
3.2811	14.74	1808000	3.3504
3.2811	14.81	1816000	3.3575
3.2672	14.87	1824000	3.3588
3.2672	14.94	1832000	3.3560
3.2919	15.0	1840000	3.3598
3.2919	15.07	1848000	3.3445
3.2724	15.13	1856000	3.3517
3.2724	15.2	1864000	3.3593
3.277	15.26	1872000	3.3598
3.277	15.33	1880000	3.3458
3.2842	15.39	1888000	3.3583
3.2842	15.46	1896000	3.3448
3.2758	15.53	1904000	3.3593
3.2758	15.59	1912000	3.3552
3.2684	15.66	1920000	3.3715
3.2684	15.72	1928000	3.3544
3.2924	15.79	1936000	3.3515
3.2924	15.85	1944000	3.3646
3.2673	15.92	1952000	3.3538
3.2673	15.98	1960000	3.3437
3.2833	16.05	1968000	3.3443
3.2833	16.11	1976000	3.3619
3.2636	16.18	1984000	3.3511
3.2636	16.24	1992000	3.3448
3.2753	16.31	2000000	3.3560
3.2753	16.37	2008000	3.3525
3.2701	16.44	2016000	3.3558
3.2701	16.5	2024000	3.3559
3.2761	16.57	2032000	3.3440
3.2761	16.63	2040000	3.3506
3.2677	16.7	2048000	3.3474
3.2677	16.76	2056000	3.3615
3.2614	16.83	2064000	3.3507
3.2614	16.89	2072000	3.3444
3.2608	16.96	2080000	3.3527
3.2608	17.03	2088000	3.3398
3.2643	17.09	2096000	3.3498
3.2643	17.16	2104000	3.3349
3.2721	17.22	2112000	3.3560
3.2721	17.29	2120000	3.3421
3.266	17.35	2128000	3.3429
3.266	17.42	2136000	3.3371
3.2551	17.48	2144000	3.3404
3.2551	17.55	2152000	3.3494
3.26	17.61	2160000	3.3389
3.26	17.68	2168000	3.3456
3.2528	17.74	2176000	3.3249
3.2528	17.81	2184000	3.3452
3.2602	17.87	2192000	3.3376
3.2602	17.94	2200000	3.3511
3.2492	18.0	2208000	3.3475
3.2492	18.07	2216000	3.3497
3.2469	18.13	2224000	3.3378
3.2469	18.2	2232000	3.3326
3.2589	18.26	2240000	3.3277
3.2589	18.33	2248000	3.3457
3.2548	18.4	2256000	3.3343
3.2548	18.46	2264000	3.3362
3.2589	18.53	2272000	3.3431
3.2589	18.59	2280000	3.3428
3.2674	18.66	2288000	3.3401
3.2674	18.72	2296000	3.3375
3.2561	18.79	2304000	3.3334
3.2561	18.85	2312000	3.3321
3.2452	18.92	2320000	3.3446
3.2452	18.98	2328000	3.3525
3.259	19.05	2336000	3.3318
3.259	19.11	2344000	3.3452
3.2494	19.18	2352000	3.3355
3.2494	19.24	2360000	3.3322
3.2558	19.31	2368000	3.3255
3.2558	19.37	2376000	3.3330
3.2436	19.44	2384000	3.3358
3.2436	19.5	2392000	3.3287
3.2545	19.57	2400000	3.3321

Framework versions

• Transformers 4.35.0.dev0
• Pytorch 2.0.1+cu117
• Datasets 2.14.5
• Tokenizers 0.14.0