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DouglasPontes/
2020-Q1-90p-filtered

Name: 2020-Q1-90p-filtered
Availability: InStock

generated_from_trainer

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

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2020-Q1-90p-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: 3.2574

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	0.16	8000	3.4495
3.5684	0.33	16000	3.4166
3.5684	0.49	24000	3.3847
3.3755	0.66	32000	3.3665
3.3755	0.82	40000	3.3654
3.3533	0.98	48000	3.3654
3.3533	1.15	56000	3.3328
3.3014	1.31	64000	3.3210
3.3014	1.48	72000	3.3492
3.2888	1.64	80000	3.3213
3.2888	1.8	88000	3.2708
3.2609	1.97	96000	3.2908
3.2609	2.13	104000	3.2767
3.2159	2.29	112000	3.2592
3.2159	2.46	120000	3.2411
3.2167	2.62	128000	3.2377
3.2167	2.79	136000	3.2485
3.199	2.95	144000	3.2609
3.199	3.11	152000	3.2553
3.1905	3.28	160000	3.2425
3.1905	3.44	168000	3.2422
3.1822	3.61	176000	3.2624
3.1822	3.77	184000	3.2507
3.1852	3.93	192000	3.2483
3.1852	4.1	200000	3.2514
3.1767	4.26	208000	3.2426
3.1767	4.43	216000	3.2348
3.1767	4.59	224000	3.2735
3.1767	4.75	232000	3.2472
3.1973	4.92	240000	3.2596
3.1973	5.08	248000	3.2606
3.1781	5.24	256000	3.2815
3.1781	5.41	264000	3.2734
3.1803	5.57	272000	3.2739
3.1803	5.74	280000	3.2712
3.1989	5.9	288000	3.2734
3.1989	6.06	296000	3.2939
3.1929	6.23	304000	3.2880
3.1929	6.39	312000	3.2894
3.2083	6.56	320000	3.3086
3.2083	6.72	328000	3.3067
3.2013	6.88	336000	3.2787
3.2013	7.05	344000	3.3153
3.2111	7.21	352000	3.3246
3.2111	7.38	360000	3.3323
3.2186	7.54	368000	3.2938
3.2186	7.7	376000	3.3500
3.2268	7.87	384000	3.3180
3.2268	8.03	392000	3.3171
3.233	8.2	400000	3.3462
3.233	8.36	408000	3.3413
3.2432	8.52	416000	3.3281
3.2432	8.69	424000	3.3420
3.2586	8.85	432000	3.3609
3.2586	9.01	440000	3.3527
3.2567	9.18	448000	3.3594
3.2567	9.34	456000	3.3497
3.2592	9.51	464000	3.3607
3.2592	9.67	472000	3.3840
3.2793	9.83	480000	3.3668
3.2793	10.0	488000	3.3609
3.257	10.16	496000	3.3682
3.257	10.33	504000	3.4006
3.2656	10.49	512000	3.3588
3.2656	10.65	520000	3.3799
3.2727	10.82	528000	3.3833
3.2727	10.98	536000	3.3566
3.2705	11.15	544000	3.3794
3.2705	11.31	552000	3.3838
3.2676	11.47	560000	3.3660
3.2676	11.64	568000	3.3938
3.258	11.8	576000	3.3661
3.258	11.97	584000	3.3490
3.2646	12.13	592000	3.3716
3.2646	12.29	600000	3.3877
3.2578	12.46	608000	3.3930
3.2578	12.62	616000	3.3921
3.2719	12.78	624000	3.3957
3.2719	12.95	632000	3.4196
3.2828	13.11	640000	3.4078
3.2828	13.28	648000	3.4203
3.2805	13.44	656000	3.3900
3.2805	13.6	664000	3.4038
3.2975	13.77	672000	3.4056
3.2975	13.93	680000	3.4284
3.2965	14.1	688000	3.4180
3.2965	14.26	696000	3.4196
3.3069	14.42	704000	3.4257
3.3069	14.59	712000	3.4299
3.3152	14.75	720000	3.4788
3.3152	14.92	728000	3.4425
3.3125	15.08	736000	3.4301
3.3125	15.24	744000	3.4441
3.3174	15.41	752000	3.4396
3.3174	15.57	760000	3.4639
3.3242	15.73	768000	3.4524
3.3242	15.9	776000	3.4560
3.3385	16.06	784000	3.4780
3.3385	16.23	792000	3.4774
3.3371	16.39	800000	3.4772
3.3371	16.55	808000	3.4955
3.3633	16.72	816000	3.4861
3.3633	16.88	824000	3.5063
3.3678	17.05	832000	3.5044
3.3678	17.21	840000	3.5202
3.3634	17.37	848000	3.4941
3.3634	17.54	856000	3.5223
3.3797	17.7	864000	3.5028
3.3797	17.87	872000	3.5264
3.3802	18.03	880000	3.5313
3.3802	18.19	888000	3.4963
3.357	18.36	896000	3.5171
3.357	18.52	904000	3.5307
3.3866	18.69	912000	3.5222
3.3866	18.85	920000	3.5319
3.3818	19.01	928000	3.5326
3.3818	19.18	936000	3.5116
3.3754	19.34	944000	3.5229
3.3754	19.5	952000	3.5383
3.3893	19.67	960000	3.5445
3.3893	19.83	968000	3.5231
3.3899	20.0	976000	3.5310
3.3899	20.16	984000	3.5329
3.3918	20.32	992000	3.5159
3.3918	20.49	1000000	3.5628
3.3786	20.65	1008000	3.5291
3.3786	20.82	1016000	3.5163
3.3862	20.98	1024000	3.5312
3.3862	21.14	1032000	3.5140
3.3855	21.31	1040000	3.5617
3.3855	21.47	1048000	3.5375
3.3872	21.64	1056000	3.5328
3.3872	21.8	1064000	3.5616
3.3931	21.96	1072000	3.5648
3.3931	22.13	1080000	3.5443
3.3708	22.29	1088000	3.5401
3.3708	22.45	1096000	3.5529
3.4099	22.62	1104000	3.5334
3.4099	22.78	1112000	3.5325
3.4027	22.95	1120000	3.5819
3.4027	23.11	1128000	3.5471
3.4035	23.27	1136000	3.5486
3.4035	23.44	1144000	3.5470
3.3964	23.6	1152000	3.5722
3.3964	23.77	1160000	3.5510
3.4115	23.93	1168000	3.5610
3.4115	24.09	1176000	3.5757
3.4173	24.26	1184000	3.5541
3.4173	24.42	1192000	3.5777
3.4169	24.59	1200000	3.5638
3.4169	24.75	1208000	3.5463
3.4031	24.91	1216000	3.5300
3.4031	25.08	1224000	3.5584
3.4094	25.24	1232000	3.5682
3.4094	25.41	1240000	3.5558
3.4116	25.57	1248000	3.5629
3.4116	25.73	1256000	3.5490
3.4199	25.9	1264000	3.5679
3.4199	26.06	1272000	3.5885
3.412	26.22	1280000	3.5579
3.412	26.39	1288000	3.5465
3.4123	26.55	1296000	3.5726
3.4123	26.72	1304000	3.5775
3.4132	26.88	1312000	3.5478
3.4132	27.04	1320000	3.5589
3.4161	27.21	1328000	3.5662
3.4161	27.37	1336000	3.5895
3.4097	27.54	1344000	3.5941
3.4097	27.7	1352000	3.5912
3.415	27.86	1360000	3.5658
3.415	28.03	1368000	3.5554
3.4193	28.19	1376000	3.5899
3.4193	28.36	1384000	3.5652
3.4136	28.52	1392000	3.5832
3.4136	28.68	1400000	3.5885
3.4294	28.85	1408000	3.5832
3.4294	29.01	1416000	3.6025
3.4243	29.17	1424000	3.6040
3.4243	29.34	1432000	3.5890
3.4427	29.5	1440000	3.5835
3.4427	29.67	1448000	3.6185
3.4293	29.83	1456000	3.6029
3.4293	29.99	1464000	3.6162
3.4363	30.16	1472000	3.6258
3.4363	30.32	1480000	3.6038
3.4532	30.49	1488000	3.6039
3.4532	30.65	1496000	3.6054
3.4401	30.81	1504000	3.6269
3.4401	30.98	1512000	3.6004
3.4491	31.14	1520000	3.6096
3.4491	31.31	1528000	3.6217
3.4438	31.47	1536000	3.6081
3.4438	31.63	1544000	3.6190
3.4337	31.8	1552000	3.6120
3.4337	31.96	1560000	3.5861
3.4475	32.13	1568000	3.6209
3.4475	32.29	1576000	3.6302
3.4406	32.45	1584000	3.6053
3.4406	32.62	1592000	3.5934
3.4392	32.78	1600000	3.5942
3.4392	32.94	1608000	3.6013
3.4514	33.11	1616000	3.6506
3.4514	33.27	1624000	3.6049
3.4406	33.44	1632000	3.6285
3.4406	33.6	1640000	3.6107
3.4522	33.76	1648000	3.6081
3.4522	33.93	1656000	3.6121
3.4592	34.09	1664000	3.6396
3.4592	34.26	1672000	3.6284
3.4587	34.42	1680000	3.6195
3.4587	34.58	1688000	3.6168
3.4589	34.75	1696000	3.6315
3.4589	34.91	1704000	3.6045
3.4703	35.08	1712000	3.6251
3.4703	35.24	1720000	3.6252
3.4565	35.4	1728000	3.6254
3.4565	35.57	1736000	3.6544
3.4634	35.73	1744000	3.6290
3.4634	35.9	1752000	3.6124
3.4625	36.06	1760000	3.6262
3.4625	36.22	1768000	3.6318
3.457	36.39	1776000	3.6408
3.457	36.55	1784000	3.6433
3.4618	36.71	1792000	3.6276
3.4618	36.88	1800000	3.6314
3.4611	37.04	1808000	3.6416
3.4611	37.21	1816000	3.6658
3.4651	37.37	1824000	3.6382
3.4651	37.53	1832000	3.6562
3.4625	37.7	1840000	3.6376
3.4625	37.86	1848000	3.6520
3.4561	38.03	1856000	3.6301
3.4561	38.19	1864000	3.6195
3.4655	38.35	1872000	3.6279
3.4655	38.52	1880000	3.6365
3.4637	38.68	1888000	3.6386
3.4637	38.85	1896000	3.6434
3.458	39.01	1904000	3.6519
3.458	39.17	1912000	3.6438
3.4523	39.34	1920000	3.6408
3.4523	39.5	1928000	3.6513
3.4743	39.66	1936000	3.6178
3.4743	39.83	1944000	3.6399
3.4626	39.99	1952000	3.6243
3.4626	40.16	1960000	3.6326
3.4692	40.32	1968000	3.6723
3.4692	40.48	1976000	3.6456
3.4765	40.65	1984000	3.6437
3.4765	40.81	1992000	3.6477
3.4747	40.98	2000000	3.6384
3.4747	41.14	2008000	3.6370
3.4683	41.3	2016000	3.6625
3.4683	41.47	2024000	3.6453
3.4599	41.63	2032000	3.6489
3.4599	41.8	2040000	3.6311
3.4713	41.96	2048000	3.6192
3.4713	42.12	2056000	3.6511
3.4677	42.29	2064000	3.6426
3.4677	42.45	2072000	3.6363
3.4689	42.62	2080000	3.6378
3.4689	42.78	2088000	3.6450
3.4598	42.94	2096000	3.6481
3.4598	43.11	2104000	3.6675
3.4487	43.27	2112000	3.6558
3.4487	43.43	2120000	3.6451
3.4555	43.6	2128000	3.6431
3.4555	43.76	2136000	3.6470
3.4727	43.93	2144000	3.6265
3.4727	44.09	2152000	3.6335
3.4626	44.25	2160000	3.6396
3.4626	44.42	2168000	3.6537
3.4724	44.58	2176000	3.6168
3.4724	44.75	2184000	3.6444
3.4545	44.91	2192000	3.6440
3.4545	45.07	2200000	3.6327
3.461	45.24	2208000	3.6363
3.461	45.4	2216000	3.6537
3.4702	45.57	2224000	3.6123
3.4702	45.73	2232000	3.6554
3.4565	45.89	2240000	3.6523
3.4565	46.06	2248000	3.6340
3.4517	46.22	2256000	3.6459
3.4517	46.38	2264000	3.6561
3.4631	46.55	2272000	3.6548
3.4631	46.71	2280000	3.6229
3.4518	46.88	2288000	3.6350
3.4518	47.04	2296000	3.6483
3.4592	47.2	2304000	3.6263
3.4592	47.37	2312000	3.6339
3.4569	47.53	2320000	3.6594
3.4569	47.7	2328000	3.6385
3.4524	47.86	2336000	3.6434
3.4524	48.02	2344000	3.6502
3.4644	48.19	2352000	3.6176
3.4644	48.35	2360000	3.6293
3.4586	48.52	2368000	3.6304
3.4586	48.68	2376000	3.6343
3.4439	48.84	2384000	3.6090
3.4439	49.01	2392000	3.6414
3.4474	49.17	2400000	3.6208

Framework versions

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

DouglasPontes/2020-Q1-90p-filtered

2020-Q1-90p-filtered

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

DouglasPontes/
2020-Q1-90p-filtered