You can deploy this model and start earning money today!

DouglasPontes/
2020-Q4-50p-filtered

Name: 2020-Q4-50p-filtered
Availability: InStock

generated_from_trainer

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

Views

No views yet

2020-Q4-50p-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: 2.6101

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	0.03	8000	2.9660
3.1627	0.07	16000	2.8754
3.1627	0.1	24000	2.8263
2.9611	0.13	32000	2.7973
2.9611	0.17	40000	2.7741
2.8986	0.2	48000	2.7574
2.8986	0.24	56000	2.7413
2.8726	0.27	64000	2.7240
2.8726	0.3	72000	2.7239
2.8558	0.34	80000	2.7132
2.8558	0.37	88000	2.7030
2.8459	0.4	96000	2.7112
2.8459	0.44	104000	2.6918
2.8379	0.47	112000	2.7017
2.8379	0.51	120000	2.6920
2.8265	0.54	128000	2.6971
2.8265	0.57	136000	2.6924
2.8227	0.61	144000	2.6952
2.8227	0.64	152000	2.6811
2.8209	0.67	160000	2.6829
2.8209	0.71	168000	2.6883
2.8147	0.74	176000	2.6675
2.8147	0.77	184000	2.6674
2.8077	0.81	192000	2.6661
2.8077	0.84	200000	2.6773
2.8058	0.88	208000	2.6734
2.8058	0.91	216000	2.6742
2.812	0.94	224000	2.6666
2.812	0.98	232000	2.6642
2.8025	1.01	240000	2.6681
2.8025	1.04	248000	2.6663
2.809	1.08	256000	2.6645
2.809	1.11	264000	2.6529
2.8073	1.15	272000	2.6623
2.8073	1.18	280000	2.6551
2.8005	1.21	288000	2.6643
2.8005	1.25	296000	2.6628
2.7988	1.28	304000	2.6583
2.7988	1.31	312000	2.6594
2.7887	1.35	320000	2.6544
2.7887	1.38	328000	2.6516
2.7964	1.41	336000	2.6555
2.7964	1.45	344000	2.6551
2.7919	1.48	352000	2.6508
2.7919	1.52	360000	2.6486
2.8058	1.55	368000	2.6484
2.8058	1.58	376000	2.6532
2.796	1.62	384000	2.6473
2.796	1.65	392000	2.6489
2.799	1.68	400000	2.6476
2.799	1.72	408000	2.6417
2.7991	1.75	416000	2.6545
2.7991	1.79	424000	2.6466
2.792	1.82	432000	2.6397
2.792	1.85	440000	2.6428
2.7972	1.89	448000	2.6446
2.7972	1.92	456000	2.6434
2.798	1.95	464000	2.6490
2.798	1.99	472000	2.6502
2.7914	2.02	480000	2.6407
2.7914	2.05	488000	2.6284
2.7932	2.09	496000	2.6426
2.7932	2.12	504000	2.6423
2.787	2.16	512000	2.6385
2.787	2.19	520000	2.6388
2.7893	2.22	528000	2.6422
2.7893	2.26	536000	2.6410
2.7889	2.29	544000	2.6337
2.7889	2.32	552000	2.6280
2.791	2.36	560000	2.6364
2.791	2.39	568000	2.6341
2.7883	2.43	576000	2.6317
2.7883	2.46	584000	2.6278
2.7889	2.49	592000	2.6357
2.7889	2.53	600000	2.6341
2.7838	2.56	608000	2.6333
2.7838	2.59	616000	2.6382
2.7873	2.63	624000	2.6275
2.7873	2.66	632000	2.6260
2.7813	2.69	640000	2.6373
2.7813	2.73	648000	2.6349
2.7858	2.76	656000	2.6223
2.7858	2.8	664000	2.6276
2.7895	2.83	672000	2.6355
2.7895	2.86	680000	2.6270
2.7873	2.9	688000	2.6244
2.7873	2.93	696000	2.6397
2.7866	2.96	704000	2.6303
2.7866	3.0	712000	2.6167
2.7865	3.03	720000	2.6265
2.7865	3.07	728000	2.6403
2.7716	3.1	736000	2.6247
2.7716	3.13	744000	2.6255
2.779	3.17	752000	2.6316
2.779	3.2	760000	2.6270
2.7811	3.23	768000	2.6268
2.7811	3.27	776000	2.6147
2.7797	3.3	784000	2.6271
2.7797	3.33	792000	2.6243
2.7798	3.37	800000	2.6240
2.7798	3.4	808000	2.6225
2.7774	3.44	816000	2.6232
2.7774	3.47	824000	2.6247
2.7744	3.5	832000	2.6270
2.7744	3.54	840000	2.6175
2.7786	3.57	848000	2.6264
2.7786	3.6	856000	2.6192
2.7829	3.64	864000	2.6278
2.7829	3.67	872000	2.6237
2.776	3.71	880000	2.6202
2.776	3.74	888000	2.6216
2.7797	3.77	896000	2.6174
2.7797	3.81	904000	2.6239
2.7744	3.84	912000	2.6163
2.7744	3.87	920000	2.6198
2.7713	3.91	928000	2.6236
2.7713	3.94	936000	2.6226
2.7853	3.97	944000	2.6175
2.7853	4.01	952000	2.6189
2.7766	4.04	960000	2.6192
2.7766	4.08	968000	2.6318
2.7851	4.11	976000	2.6210
2.7851	4.14	984000	2.6172
2.7804	4.18	992000	2.6200
2.7804	4.21	1000000	2.6157
2.773	4.24	1008000	2.6098
2.773	4.28	1016000	2.6156
2.7818	4.31	1024000	2.6149
2.7818	4.35	1032000	2.6121
2.7736	4.38	1040000	2.6150
2.7736	4.41	1048000	2.6156
2.7761	4.45	1056000	2.6171
2.7761	4.48	1064000	2.6124
2.7789	4.51	1072000	2.6277
2.7789	4.55	1080000	2.6138
2.7744	4.58	1088000	2.6081
2.7744	4.61	1096000	2.6201
2.77	4.65	1104000	2.6171
2.77	4.68	1112000	2.6099
2.772	4.72	1120000	2.6141
2.772	4.75	1128000	2.6174
2.7709	4.78	1136000	2.6200
2.7709	4.82	1144000	2.6150
2.7724	4.85	1152000	2.6042
2.7724	4.88	1160000	2.6158
2.7763	4.92	1168000	2.6167
2.7763	4.95	1176000	2.6174
2.7736	4.99	1184000	2.6099
2.7736	5.02	1192000	2.6076
2.7692	5.05	1200000	2.6088
2.7692	5.09	1208000	2.6174
2.7794	5.12	1216000	2.6041
2.7794	5.15	1224000	2.6051
2.7709	5.19	1232000	2.6093
2.7709	5.22	1240000	2.6062
2.7727	5.25	1248000	2.6052
2.7727	5.29	1256000	2.6126
2.7686	5.32	1264000	2.6099
2.7686	5.36	1272000	2.6192
2.7668	5.39	1280000	2.6166
2.7668	5.42	1288000	2.6042
2.7777	5.46	1296000	2.6038
2.7777	5.49	1304000	2.6119
2.7737	5.52	1312000	2.6155
2.7737	5.56	1320000	2.6236
2.7757	5.59	1328000	2.6124
2.7757	5.63	1336000	2.5993
2.7757	5.66	1344000	2.6132
2.7757	5.69	1352000	2.6063
2.7748	5.73	1360000	2.6130
2.7748	5.76	1368000	2.6100
2.769	5.79	1376000	2.6024
2.769	5.83	1384000	2.6062
2.7713	5.86	1392000	2.6138
2.7713	5.89	1400000	2.6025
2.7766	5.93	1408000	2.6088
2.7766	5.96	1416000	2.6138
2.7727	6.0	1424000	2.6048
2.7727	6.03	1432000	2.6068
2.7737	6.06	1440000	2.6144
2.7737	6.1	1448000	2.6051
2.778	6.13	1456000	2.6158
2.778	6.16	1464000	2.6152
2.7767	6.2	1472000	2.6019
2.7767	6.23	1480000	2.6117
2.7706	6.27	1488000	2.6065
2.7706	6.3	1496000	2.6122
2.7775	6.33	1504000	2.6100
2.7775	6.37	1512000	2.6100
2.7753	6.4	1520000	2.6051
2.7753	6.43	1528000	2.6037
2.7691	6.47	1536000	2.6037
2.7691	6.5	1544000	2.5992
2.758	6.53	1552000	2.6080
2.758	6.57	1560000	2.6139
2.7722	6.6	1568000	2.6000
2.7722	6.64	1576000	2.6107
2.7737	6.67	1584000	2.6057
2.7737	6.7	1592000	2.6063
2.7722	6.74	1600000	2.6028
2.7722	6.77	1608000	2.5995
2.7659	6.8	1616000	2.6042
2.7659	6.84	1624000	2.6013
2.7769	6.87	1632000	2.6028
2.7769	6.91	1640000	2.6080
2.7732	6.94	1648000	2.5994
2.7732	6.97	1656000	2.6063
2.7708	7.01	1664000	2.6120
2.7708	7.04	1672000	2.6023
2.7614	7.07	1680000	2.6091
2.7614	7.11	1688000	2.6003
2.7655	7.14	1696000	2.6016
2.7655	7.17	1704000	2.6058
2.7747	7.21	1712000	2.6045
2.7747	7.24	1720000	2.6097
2.7685	7.28	1728000	2.6068
2.7685	7.31	1736000	2.6037
2.7736	7.34	1744000	2.6125
2.7736	7.38	1752000	2.6113
2.7666	7.41	1760000	2.5972
2.7666	7.44	1768000	2.6081
2.7658	7.48	1776000	2.6090
2.7658	7.51	1784000	2.6126
2.7802	7.55	1792000	2.6021
2.7802	7.58	1800000	2.6087
2.7749	7.61	1808000	2.5986
2.7749	7.65	1816000	2.6002
2.7689	7.68	1824000	2.6023
2.7689	7.71	1832000	2.5969
2.7699	7.75	1840000	2.5975
2.7699	7.78	1848000	2.6070
2.7715	7.81	1856000	2.6035
2.7715	7.85	1864000	2.6049
2.7653	7.88	1872000	2.6129
2.7653	7.92	1880000	2.6027
2.7729	7.95	1888000	2.6000
2.7729	7.98	1896000	2.6138
2.7693	8.02	1904000	2.6052
2.7693	8.05	1912000	2.6060
2.7585	8.08	1920000	2.6065
2.7585	8.12	1928000	2.6105
2.7652	8.15	1936000	2.6075
2.7652	8.19	1944000	2.6076
2.7508	8.22	1952000	2.6083
2.7508	8.25	1960000	2.6112
2.7678	8.29	1968000	2.6019
2.7678	8.32	1976000	2.6029
2.7653	8.35	1984000	2.6087
2.7653	8.39	1992000	2.6064
2.7661	8.42	2000000	2.6031
2.7661	8.45	2008000	2.6051
2.7742	8.49	2016000	2.6091
2.7742	8.52	2024000	2.5978
2.7748	8.56	2032000	2.6131
2.7748	8.59	2040000	2.6030
2.7706	8.62	2048000	2.6036
2.7706	8.66	2056000	2.5998
2.769	8.69	2064000	2.6013
2.769	8.72	2072000	2.6000
2.7733	8.76	2080000	2.6062
2.7733	8.79	2088000	2.6057
2.7714	8.83	2096000	2.6021
2.7714	8.86	2104000	2.6028
2.7754	8.89	2112000	2.5964
2.7754	8.93	2120000	2.6015
2.7683	8.96	2128000	2.6060
2.7683	8.99	2136000	2.6082
2.7758	9.03	2144000	2.6130
2.7758	9.06	2152000	2.6071
2.768	9.09	2160000	2.6141
2.768	9.13	2168000	2.6003
2.7653	9.16	2176000	2.5987
2.7653	9.2	2184000	2.6066
2.7621	9.23	2192000	2.6041
2.7621	9.26	2200000	2.6060
2.7712	9.3	2208000	2.6144
2.7712	9.33	2216000	2.5990
2.7718	9.36	2224000	2.6039
2.7718	9.4	2232000	2.5931
2.774	9.43	2240000	2.6129
2.774	9.47	2248000	2.6095
2.765	9.5	2256000	2.5932
2.765	9.53	2264000	2.6010
2.7754	9.57	2272000	2.6078
2.7754	9.6	2280000	2.5981
2.771	9.63	2288000	2.6052
2.771	9.67	2296000	2.5944
2.7757	9.7	2304000	2.6045
2.7757	9.73	2312000	2.5971
2.7685	9.77	2320000	2.6101
2.7685	9.8	2328000	2.5964
2.7708	9.84	2336000	2.5974
2.7708	9.87	2344000	2.5953
2.7695	9.9	2352000	2.5981
2.7695	9.94	2360000	2.6095
2.7702	9.97	2368000	2.6042
2.7702	10.0	2376000	2.6095
2.7614	10.04	2384000	2.6007
2.7614	10.07	2392000	2.6017
2.7708	10.11	2400000	2.6114

Framework versions

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

DouglasPontes/2020-Q4-50p-filtered

2020-Q4-50p-filtered

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

DouglasPontes/
2020-Q4-50p-filtered