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DouglasPontes/
2020-Q2-75p-filtered

Name: 2020-Q2-75p-filtered
Availability: InStock

generated_from_trainer

cardiffnlp/twitter-roberta-base-2019-90m

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2020-Q2-75p-filtered

This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-2019-90m on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 3.0187

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	3.1520
3.3704	0.13	16000	3.1286
3.3704	0.2	24000	3.1079
3.2908	0.27	32000	3.0845
3.2908	0.34	40000	3.0868
3.2742	0.4	48000	3.0768
3.2742	0.47	56000	3.0706
3.2579	0.54	64000	3.0621
3.2579	0.61	72000	3.0659
3.2448	0.67	80000	3.0457
3.2448	0.74	88000	3.0554
3.2416	0.81	96000	3.0335
3.2416	0.88	104000	3.0321
3.23	0.94	112000	3.0137
3.23	1.01	120000	3.0061
3.2084	1.08	128000	3.0251
3.2084	1.15	136000	3.0092
3.2055	1.21	144000	3.0043
3.2055	1.28	152000	3.0055
3.2026	1.35	160000	3.0066
3.2026	1.41	168000	3.0125
3.2069	1.48	176000	3.0032
3.2069	1.55	184000	2.9959
3.1904	1.62	192000	2.9960
3.1904	1.68	200000	3.0038
3.1989	1.75	208000	3.0016
3.1989	1.82	216000	3.0049
3.2113	1.89	224000	3.0086
3.2113	1.95	232000	3.0104
3.217	2.02	240000	3.0166
3.217	2.09	248000	3.0139
3.2029	2.16	256000	3.0217
3.2029	2.22	264000	3.0238
3.2226	2.29	272000	3.0234
3.2226	2.36	280000	3.0216
3.2199	2.43	288000	3.0175
3.2199	2.49	296000	3.0365
3.2254	2.56	304000	3.0282
3.2254	2.63	312000	3.0228
3.2349	2.7	320000	3.0205
3.2349	2.76	328000	3.0406
3.2424	2.83	336000	3.0307
3.2424	2.9	344000	3.0413
3.2347	2.96	352000	3.0401
3.2347	3.03	360000	3.0520
3.2476	3.1	368000	3.0489
3.2476	3.17	376000	3.0521
3.2506	3.23	384000	3.0685
3.2506	3.3	392000	3.0546
3.2547	3.37	400000	3.0542
3.2547	3.44	408000	3.0537
3.2519	3.5	416000	3.0588
3.2519	3.57	424000	3.0729
3.2679	3.64	432000	3.0842
3.2679	3.71	440000	3.0685
3.2656	3.77	448000	3.0942
3.2656	3.84	456000	3.0942
3.2908	3.91	464000	3.0918
3.2908	3.98	472000	3.0922
3.2944	4.04	480000	3.1093
3.2944	4.11	488000	3.1158
3.2917	4.18	496000	3.0997
3.2917	4.24	504000	3.1111
3.2916	4.31	512000	3.1133
3.2916	4.38	520000	3.1129
3.2836	4.45	528000	3.1134
3.2836	4.51	536000	3.1058
3.3068	4.58	544000	3.1211
3.3068	4.65	552000	3.0946
3.3026	4.72	560000	3.1079
3.3026	4.78	568000	3.1202
3.3078	4.85	576000	3.1155
3.3078	4.92	584000	3.1254
3.3168	4.99	592000	3.1279
3.3168	5.05	600000	3.1179
3.3113	5.12	608000	3.1277
3.3113	5.19	616000	3.1334
3.3102	5.26	624000	3.1233
3.3102	5.32	632000	3.1274
3.3235	5.39	640000	3.1434
3.3235	5.46	648000	3.1368
3.331	5.53	656000	3.1591
3.331	5.59	664000	3.1546
3.3308	5.66	672000	3.1663
3.3308	5.73	680000	3.1535
3.3396	5.79	688000	3.1558
3.3396	5.86	696000	3.1698
3.3558	5.93	704000	3.1651
3.3558	6.0	712000	3.1706
3.3474	6.06	720000	3.1942
3.3474	6.13	728000	3.1705
3.3513	6.2	736000	3.1834
3.3513	6.27	744000	3.1810
3.362	6.33	752000	3.1723
3.362	6.4	760000	3.1827
3.3694	6.47	768000	3.1937
3.3694	6.54	776000	3.2004
3.378	6.6	784000	3.2023
3.378	6.67	792000	3.1936
3.3703	6.74	800000	3.1948
3.3703	6.81	808000	3.2082
3.3838	6.87	816000	3.1974
3.3838	6.94	824000	3.2029
3.3871	7.01	832000	3.2160
3.3871	7.07	840000	3.2198
3.3839	7.14	848000	3.2190
3.3839	7.21	856000	3.2204
3.389	7.28	864000	3.2188
3.389	7.34	872000	3.2246
3.398	7.41	880000	3.2333
3.398	7.48	888000	3.2168
3.4001	7.55	896000	3.2311
3.4001	7.61	904000	3.2390
3.4255	7.68	912000	3.2447
3.4255	7.75	920000	3.2546
3.4218	7.82	928000	3.2510
3.4218	7.88	936000	3.2433
3.4326	7.95	944000	3.2509
3.4326	8.02	952000	3.2573
3.4268	8.09	960000	3.2499
3.4268	8.15	968000	3.2704
3.4165	8.22	976000	3.2579
3.4165	8.29	984000	3.2669
3.4425	8.36	992000	3.2723
3.4425	8.42	1000000	3.2718
3.4433	8.49	1008000	3.2655
3.4433	8.56	1016000	3.2794
3.4437	8.62	1024000	3.2808
3.4437	8.69	1032000	3.2731
3.4499	8.76	1040000	3.2785
3.4499	8.83	1048000	3.2823
3.4593	8.89	1056000	3.2844
3.4593	8.96	1064000	3.2877
3.4481	9.03	1072000	3.2969
3.4481	9.1	1080000	3.2870
3.4542	9.16	1088000	3.2946
3.4542	9.23	1096000	3.2901
3.4547	9.3	1104000	3.2813
3.4547	9.37	1112000	3.2910
3.4618	9.43	1120000	3.2978
3.4618	9.5	1128000	3.3055
3.46	9.57	1136000	3.2885
3.46	9.64	1144000	3.2871
3.4572	9.7	1152000	3.2905
3.4572	9.77	1160000	3.3006
3.4597	9.84	1168000	3.3081
3.4597	9.9	1176000	3.3031
3.4651	9.97	1184000	3.2883
3.4651	10.04	1192000	3.3189
3.4571	10.11	1200000	3.2978
3.4571	10.17	1208000	3.3091
3.4567	10.24	1216000	3.2755
3.4567	10.31	1224000	3.2968
3.4584	10.38	1232000	3.2991
3.4584	10.44	1240000	3.2818
3.4459	10.51	1248000	3.2823
3.4459	10.58	1256000	3.2800
3.4474	10.65	1264000	3.2856
3.4474	10.71	1272000	3.2845
3.4383	10.78	1280000	3.2804
3.4383	10.85	1288000	3.2707
3.4496	10.92	1296000	3.2824
3.4496	10.98	1304000	3.2765
3.4411	11.05	1312000	3.2838
3.4411	11.12	1320000	3.2839
3.4305	11.19	1328000	3.2748
3.4305	11.25	1336000	3.2821
3.4258	11.32	1344000	3.2746
3.4258	11.39	1352000	3.2861
3.4227	11.45	1360000	3.2710
3.4227	11.52	1368000	3.2788
3.4319	11.59	1376000	3.2794
3.4319	11.66	1384000	3.2766
3.436	11.72	1392000	3.2924
3.436	11.79	1400000	3.2812
3.4368	11.86	1408000	3.2851
3.4368	11.93	1416000	3.2822
3.4346	11.99	1424000	3.2657
3.4346	12.06	1432000	3.2748
3.4265	12.13	1440000	3.2685
3.4265	12.2	1448000	3.2947
3.4306	12.26	1456000	3.2841
3.4306	12.33	1464000	3.2748
3.4254	12.4	1472000	3.2794
3.4254	12.47	1480000	3.2774
3.4353	12.53	1488000	3.2726
3.4353	12.6	1496000	3.2763
3.4358	12.67	1504000	3.2659
3.4358	12.73	1512000	3.2710
3.4182	12.8	1520000	3.2777
3.4182	12.87	1528000	3.2824
3.4384	12.94	1536000	3.2887
3.4384	13.0	1544000	3.2667
3.4287	13.07	1552000	3.2713
3.4287	13.14	1560000	3.2640
3.4181	13.21	1568000	3.2607
3.4181	13.27	1576000	3.2643
3.4173	13.34	1584000	3.2630
3.4173	13.41	1592000	3.2572
3.4214	13.48	1600000	3.2728
3.4214	13.54	1608000	3.2822
3.4223	13.61	1616000	3.2704
3.4223	13.68	1624000	3.2634
3.417	13.75	1632000	3.2691
3.417	13.81	1640000	3.2550
3.4146	13.88	1648000	3.2529
3.4146	13.95	1656000	3.2713
3.4186	14.02	1664000	3.2672
3.4186	14.08	1672000	3.2542
3.4082	14.15	1680000	3.2576
3.4082	14.22	1688000	3.2680
3.4186	14.28	1696000	3.2667
3.4186	14.35	1704000	3.2694
3.4131	14.42	1712000	3.2606
3.4131	14.49	1720000	3.2622
3.4239	14.55	1728000	3.2678
3.4239	14.62	1736000	3.2708
3.4197	14.69	1744000	3.2622
3.4197	14.76	1752000	3.2605
3.4073	14.82	1760000	3.2647
3.4073	14.89	1768000	3.2619
3.4167	14.96	1776000	3.2816
3.4167	15.03	1784000	3.2603
3.413	15.09	1792000	3.2661
3.413	15.16	1800000	3.2589
3.4117	15.23	1808000	3.2688
3.4117	15.3	1816000	3.2678
3.4103	15.36	1824000	3.2661
3.4103	15.43	1832000	3.2705
3.4074	15.5	1840000	3.2670
3.4074	15.56	1848000	3.2619
3.4167	15.63	1856000	3.2624
3.4167	15.7	1864000	3.2552
3.4195	15.77	1872000	3.2503
3.4195	15.83	1880000	3.2606
3.4091	15.9	1888000	3.2812
3.4091	15.97	1896000	3.2837
3.4116	16.04	1904000	3.2658
3.4116	16.1	1912000	3.2676
3.4183	16.17	1920000	3.2770
3.4183	16.24	1928000	3.2756
3.4177	16.31	1936000	3.2876
3.4177	16.37	1944000	3.2612
3.4226	16.44	1952000	3.2748
3.4226	16.51	1960000	3.2679
3.4154	16.58	1968000	3.2659
3.4154	16.64	1976000	3.2689
3.4199	16.71	1984000	3.2701
3.4199	16.78	1992000	3.2564
3.4166	16.85	2000000	3.2714
3.4166	16.91	2008000	3.2738
3.4054	16.98	2016000	3.2633
3.4054	17.05	2024000	3.2574
3.4022	17.11	2032000	3.2637
3.4022	17.18	2040000	3.2688
3.408	17.25	2048000	3.2667
3.408	17.32	2056000	3.2578
3.4065	17.38	2064000	3.2605
3.4065	17.45	2072000	3.2768
3.4105	17.52	2080000	3.2569
3.4105	17.59	2088000	3.2519
3.4011	17.65	2096000	3.2555
3.4011	17.72	2104000	3.2488
3.4078	17.79	2112000	3.2516
3.4078	17.86	2120000	3.2527
3.4105	17.92	2128000	3.2561
3.4105	17.99	2136000	3.2580
3.4054	18.06	2144000	3.2453
3.4054	18.13	2152000	3.2426
3.3937	18.19	2160000	3.2517
3.3937	18.26	2168000	3.2446
3.4001	18.33	2176000	3.2449
3.4001	18.39	2184000	3.2527
3.413	18.46	2192000	3.2557
3.413	18.53	2200000	3.2483
3.3882	18.6	2208000	3.2520
3.3882	18.66	2216000	3.2354
3.3974	18.73	2224000	3.2540
3.3974	18.8	2232000	3.2426
3.3864	18.87	2240000	3.2341
3.3864	18.93	2248000	3.2408
3.3896	19.0	2256000	3.2342
3.3896	19.07	2264000	3.2415
3.3845	19.14	2272000	3.2445
3.3845	19.2	2280000	3.2422
3.3916	19.27	2288000	3.2379
3.3916	19.34	2296000	3.2411
3.3919	19.41	2304000	3.2429
3.3919	19.47	2312000	3.2372
3.39	19.54	2320000	3.2380
3.39	19.61	2328000	3.2353
3.3905	19.68	2336000	3.2327
3.3905	19.74	2344000	3.2494
3.3826	19.81	2352000	3.2369
3.3826	19.88	2360000	3.2390
3.3935	19.94	2368000	3.2415
3.3935	20.01	2376000	3.2486
3.3846	20.08	2384000	3.2354
3.3846	20.15	2392000	3.2466
3.3875	20.21	2400000	3.2425

Framework versions

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

DouglasPontes/2020-Q2-75p-filtered

2020-Q2-75p-filtered

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

DouglasPontes/
2020-Q2-75p-filtered