You can deploy this model and start earning money today!

DouglasPontes/
2020-Q2-full_tweets_combined90

Name: 2020-Q2-full_tweets_combined90
Availability: InStock

generated_from_trainer

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

Views

No views yet

2020-Q2-full_tweets_combined90

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: 1.9422

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: 4.1e-07
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
training_steps: 2400000

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.02	8000	2.2220
2.4154	0.03	16000	2.1427
2.4154	0.05	24000	2.1028
2.2273	0.07	32000	2.0824
2.2273	0.08	40000	2.0645
2.1774	0.1	48000	2.0478
2.1774	0.12	56000	2.0327
2.1569	0.13	64000	2.0248
2.1569	0.15	72000	2.0209
2.1439	0.17	80000	2.0049
2.1439	0.19	88000	2.0113
2.1271	0.2	96000	2.0038
2.1271	0.22	104000	2.0065
2.1211	0.24	112000	1.9987
2.1211	0.25	120000	1.9929
2.1194	0.27	128000	1.9922
2.1194	0.29	136000	1.9917
2.1118	0.3	144000	1.9885
2.1118	0.32	152000	1.9870
2.1047	0.34	160000	1.9843
2.1047	0.35	168000	1.9827
2.1015	0.37	176000	1.9826
2.1015	0.39	184000	1.9774
2.1042	0.4	192000	1.9771
2.1042	0.42	200000	1.9770
2.0919	0.44	208000	1.9752
2.0919	0.45	216000	1.9775
2.0953	0.47	224000	1.9684
2.0953	0.49	232000	1.9748
2.0848	0.51	240000	1.9714
2.0848	0.52	248000	1.9781
2.0882	0.54	256000	1.9709
2.0882	0.56	264000	1.9660
2.0922	0.57	272000	1.9651
2.0922	0.59	280000	1.9678
2.0938	0.61	288000	1.9667
2.0938	0.62	296000	1.9630
2.095	0.64	304000	1.9642
2.095	0.66	312000	1.9624
2.0908	0.67	320000	1.9603
2.0908	0.69	328000	1.9649
2.0927	0.71	336000	1.9641
2.0927	0.72	344000	1.9603
2.0931	0.74	352000	1.9590
2.0931	0.76	360000	1.9644
2.087	0.77	368000	1.9635
2.087	0.79	376000	1.9614
2.0792	0.81	384000	1.9591
2.0792	0.83	392000	1.9575
2.0899	0.84	400000	1.9592
2.0899	0.86	408000	1.9619
2.0812	0.88	416000	1.9582
2.0812	0.89	424000	1.9580
2.0948	0.91	432000	1.9587
2.0948	0.93	440000	1.9593
2.0895	0.94	448000	1.9608
2.0895	0.96	456000	1.9566
2.0756	0.98	464000	1.9525
2.0756	0.99	472000	1.9541
2.0842	1.01	480000	1.9601
2.0842	1.03	488000	1.9564
2.0935	1.04	496000	1.9522
2.0935	1.06	504000	1.9532
2.0836	1.08	512000	1.9537
2.0836	1.09	520000	1.9553
2.0876	1.11	528000	1.9469
2.0876	1.13	536000	1.9497
2.0778	1.15	544000	1.9542
2.0778	1.16	552000	1.9516
2.0829	1.18	560000	1.9506
2.0829	1.2	568000	1.9505
2.0864	1.21	576000	1.9531
2.0864	1.23	584000	1.9455
2.0893	1.25	592000	1.9471
2.0893	1.26	600000	1.9539
2.0808	1.28	608000	1.9455
2.0808	1.3	616000	1.9497
2.0838	1.31	624000	1.9466
2.0838	1.33	632000	1.9498
2.0812	1.35	640000	1.9510
2.0812	1.36	648000	1.9526
2.0793	1.38	656000	1.9471
2.0793	1.4	664000	1.9469
2.0789	1.41	672000	1.9455
2.0789	1.43	680000	1.9469
2.0883	1.45	688000	1.9439
2.0883	1.47	696000	1.9439
2.09	1.48	704000	1.9416
2.09	1.5	712000	1.9492
2.0845	1.52	720000	1.9430
2.0845	1.53	728000	1.9484
2.0742	1.55	736000	1.9456
2.0742	1.57	744000	1.9380
2.0839	1.58	752000	1.9418
2.0839	1.6	760000	1.9434
2.0806	1.62	768000	1.9450
2.0806	1.63	776000	1.9426
2.0805	1.65	784000	1.9441
2.0805	1.67	792000	1.9459
2.0833	1.68	800000	1.9435
2.0833	1.7	808000	1.9455
2.0763	1.72	816000	1.9421
2.0763	1.73	824000	1.9438
2.0758	1.75	832000	1.9371
2.0758	1.77	840000	1.9432
2.0888	1.79	848000	1.9414
2.0888	1.8	856000	1.9444
2.0786	1.82	864000	1.9408
2.0786	1.84	872000	1.9397
2.079	1.85	880000	1.9406
2.079	1.87	888000	1.9442
2.0817	1.89	896000	1.9404
2.0817	1.9	904000	1.9450
2.0792	1.92	912000	1.9380
2.0792	1.94	920000	1.9385
2.0741	1.95	928000	1.9449
2.0741	1.97	936000	1.9414
2.0832	1.99	944000	1.9402
2.0832	2.0	952000	1.9410
2.0695	2.02	960000	1.9371
2.0695	2.04	968000	1.9342
2.0813	2.05	976000	1.9376
2.0813	2.07	984000	1.9397
2.0804	2.09	992000	1.9394
2.0804	2.11	1000000	1.9370
2.0789	2.12	1008000	1.9350
2.0789	2.14	1016000	1.9327
2.0754	2.16	1024000	1.9421
2.0754	2.17	1032000	1.9371
2.0774	2.19	1040000	1.9411
2.0774	2.21	1048000	1.9337
2.0766	2.22	1056000	1.9387
2.0766	2.24	1064000	1.9334
2.079	2.26	1072000	1.9386
2.079	2.27	1080000	1.9335
2.068	2.29	1088000	1.9363
2.068	2.31	1096000	1.9420
2.0786	2.32	1104000	1.9331
2.0786	2.34	1112000	1.9327
2.0734	2.36	1120000	1.9391
2.0734	2.37	1128000	1.9363
2.0787	2.39	1136000	1.9321
2.0787	2.41	1144000	1.9333
2.0731	2.43	1152000	1.9369
2.0731	2.44	1160000	1.9357
2.0816	2.46	1168000	1.9353
2.0816	2.48	1176000	1.9319
2.0758	2.49	1184000	1.9366
2.0758	2.51	1192000	1.9301
2.0725	2.53	1200000	1.9329
2.0725	2.54	1208000	1.9370
2.085	2.56	1216000	1.9251
2.085	2.58	1224000	1.9369
2.0809	2.59	1232000	1.9377
2.0809	2.61	1240000	1.9398
2.0742	2.63	1248000	1.9368
2.0742	2.64	1256000	1.9389
2.0743	2.66	1264000	1.9287
2.0743	2.68	1272000	1.9337
2.0822	2.69	1280000	1.9323
2.0822	2.71	1288000	1.9348
2.0845	2.73	1296000	1.9328
2.0845	2.75	1304000	1.9324
2.0706	2.76	1312000	1.9304
2.0706	2.78	1320000	1.9322
2.0813	2.8	1328000	1.9320
2.0813	2.81	1336000	1.9379
2.0768	2.83	1344000	1.9283
2.0768	2.85	1352000	1.9352
2.0776	2.86	1360000	1.9266
2.0776	2.88	1368000	1.9339
2.0776	2.9	1376000	1.9371
2.0776	2.91	1384000	1.9353
2.072	2.93	1392000	1.9290
2.072	2.95	1400000	1.9337
2.077	2.96	1408000	1.9318
2.077	2.98	1416000	1.9326
2.0777	3.0	1424000	1.9338
2.0777	3.01	1432000	1.9307
2.0846	3.03	1440000	1.9305
2.0846	3.05	1448000	1.9312
2.0744	3.07	1456000	1.9332
2.0744	3.08	1464000	1.9313
2.0767	3.1	1472000	1.9311
2.0767	3.12	1480000	1.9322
2.082	3.13	1488000	1.9362
2.082	3.15	1496000	1.9329
2.0774	3.17	1504000	1.9335
2.0774	3.18	1512000	1.9342
2.0793	3.2	1520000	1.9326
2.0793	3.22	1528000	1.9313
2.0834	3.23	1536000	1.9302
2.0834	3.25	1544000	1.9299
2.0698	3.27	1552000	1.9288
2.0698	3.28	1560000	1.9311
2.0721	3.3	1568000	1.9262
2.0721	3.32	1576000	1.9320
2.0742	3.33	1584000	1.9278
2.0742	3.35	1592000	1.9333
2.0774	3.37	1600000	1.9252
2.0774	3.39	1608000	1.9301
2.0766	3.4	1616000	1.9344
2.0766	3.42	1624000	1.9320
2.0702	3.44	1632000	1.9307
2.0702	3.45	1640000	1.9304
2.0772	3.47	1648000	1.9280
2.0772	3.49	1656000	1.9324
2.0757	3.5	1664000	1.9343
2.0757	3.52	1672000	1.9312
2.0747	3.54	1680000	1.9304
2.0747	3.55	1688000	1.9360
2.068	3.57	1696000	1.9297
2.068	3.59	1704000	1.9337
2.0825	3.6	1712000	1.9293
2.0825	3.62	1720000	1.9295
2.0811	3.64	1728000	1.9315
2.0811	3.65	1736000	1.9279
2.0844	3.67	1744000	1.9289
2.0844	3.69	1752000	1.9279
2.0827	3.71	1760000	1.9283
2.0827	3.72	1768000	1.9295
2.0684	3.74	1776000	1.9281
2.0684	3.76	1784000	1.9330
2.0724	3.77	1792000	1.9294
2.0724	3.79	1800000	1.9276
2.074	3.81	1808000	1.9227
2.074	3.82	1816000	1.9320
2.0801	3.84	1824000	1.9275
2.0801	3.86	1832000	1.9302
2.0783	3.87	1840000	1.9333
2.0783	3.89	1848000	1.9296
2.0787	3.91	1856000	1.9302
2.0787	3.92	1864000	1.9347
2.0733	3.94	1872000	1.9298
2.0733	3.96	1880000	1.9302
2.0742	3.97	1888000	1.9279
2.0742	3.99	1896000	1.9258
2.0769	4.01	1904000	1.9255
2.0769	4.03	1912000	1.9282
2.0736	4.04	1920000	1.9298
2.0736	4.06	1928000	1.9325
2.0713	4.08	1936000	1.9296
2.0713	4.09	1944000	1.9293
2.0825	4.11	1952000	1.9345
2.0825	4.13	1960000	1.9346
2.0828	4.14	1968000	1.9311
2.0828	4.16	1976000	1.9307
2.0821	4.18	1984000	1.9336
2.0821	4.19	1992000	1.9265
2.0768	4.21	2000000	1.9284
2.0768	4.23	2008000	1.9290
2.0695	4.24	2016000	1.9306
2.0695	4.26	2024000	1.9299
2.0698	4.28	2032000	1.9230
2.0698	4.29	2040000	1.9272
2.0776	4.31	2048000	1.9306
2.0776	4.33	2056000	1.9243
2.0797	4.35	2064000	1.9266
2.0797	4.36	2072000	1.9249
2.0808	4.38	2080000	1.9279
2.0808	4.4	2088000	1.9262
2.0776	4.41	2096000	1.9350
2.0776	4.43	2104000	1.9297
2.0805	4.45	2112000	1.9337
2.0805	4.46	2120000	1.9302
2.0791	4.48	2128000	1.9337
2.0791	4.5	2136000	1.9298
2.0771	4.51	2144000	1.9268
2.0771	4.53	2152000	1.9370
2.0807	4.55	2160000	1.9307
2.0807	4.56	2168000	1.9292
2.0856	4.58	2176000	1.9300
2.0856	4.6	2184000	1.9329
2.0744	4.61	2192000	1.9319
2.0744	4.63	2200000	1.9352
2.0839	4.65	2208000	1.9368
2.0839	4.67	2216000	1.9343
2.0706	4.68	2224000	1.9290
2.0706	4.7	2232000	1.9347
2.0745	4.72	2240000	1.9294
2.0745	4.73	2248000	1.9255
2.0767	4.75	2256000	1.9271
2.0767	4.77	2264000	1.9296
2.0753	4.78	2272000	1.9268
2.0753	4.8	2280000	1.9292
2.0716	4.82	2288000	1.9310
2.0716	4.83	2296000	1.9267
2.0778	4.85	2304000	1.9301
2.0778	4.87	2312000	1.9280
2.0724	4.88	2320000	1.9283
2.0724	4.9	2328000	1.9289
2.0811	4.92	2336000	1.9315
2.0811	4.93	2344000	1.9268
2.0816	4.95	2352000	1.9304
2.0816	4.97	2360000	1.9302
2.0775	4.99	2368000	1.9292
2.0775	5.0	2376000	1.9274
2.0807	5.02	2384000	1.9317
2.0807	5.04	2392000	1.9298
2.0668	5.05	2400000	1.9349

Framework versions

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

DouglasPontes/2020-Q2-full_tweets_combined90

2020-Q2-full_tweets_combined90

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

DouglasPontes/
2020-Q2-full_tweets_combined90