roberta-2020-Q2-filtered

DouglasPontes/roberta-2020-Q2-filtered

roberta-2020-Q2-filtered is a fill-mask model from DouglasPontes. Use it when you need the model to fill a missing word. It is set up for transformers. The card lists the license as mit.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Fill-Mask
Library: transformers
License: mit
Model type: roberta
Access: Public
Created: Nov 11, 2023
Updated: Nov 13, 2023
SHA: 55be0881

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Base models

Model card

From the Hugging Face model README

roberta-2020-Q2-filtered

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

Loss: 2.5868

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	0.03	8000	2.9727
3.1662	0.07	16000	2.8727
3.1662	0.1	24000	2.8134
2.914	0.14	32000	2.7663
2.914	0.17	40000	2.7392
2.8319	0.21	48000	2.7003
2.8319	0.24	56000	2.6796
2.7892	0.28	64000	2.6640
2.7892	0.31	72000	2.6422
2.7471	0.35	80000	2.6159
2.7471	0.38	88000	2.6056
2.727	0.42	96000	2.6007
2.727	0.45	104000	2.6046
2.7185	0.49	112000	2.6009
2.7185	0.52	120000	2.5992
2.7121	0.56	128000	2.5893
2.7121	0.59	136000	2.5993
2.722	0.63	144000	2.5935
2.722	0.66	152000	2.5808
2.724	0.7	160000	2.6009
2.724	0.73	168000	2.6015
2.7192	0.77	176000	2.5918
2.7192	0.8	184000	2.6031
2.7234	0.84	192000	2.5920
2.7234	0.87	200000	2.6065
2.7369	0.91	208000	2.6125
2.7369	0.94	216000	2.6040
2.7282	0.98	224000	2.6042
2.7282	1.01	232000	2.6186
2.7322	1.05	240000	2.6130
2.7322	1.08	248000	2.6214
2.7361	1.12	256000	2.6168
2.7361	1.15	264000	2.6266
2.7514	1.19	272000	2.6223
2.7514	1.22	280000	2.6240
2.7557	1.26	288000	2.6301
2.7557	1.29	296000	2.6284
2.7591	1.33	304000	2.6443
2.7591	1.36	312000	2.6485
2.7675	1.4	320000	2.6457
2.7675	1.43	328000	2.6444
2.7732	1.47	336000	2.6537
2.7732	1.5	344000	2.6632
2.7922	1.54	352000	2.6641
2.7922	1.57	360000	2.6715
2.7908	1.61	368000	2.6743
2.7908	1.64	376000	2.6686
2.8148	1.68	384000	2.6779
2.8148	1.71	392000	2.6765
2.8103	1.75	400000	2.6829
2.8103	1.78	408000	2.6941
2.8192	1.82	416000	2.6800
2.8192	1.85	424000	2.6954
2.8298	1.89	432000	2.7022
2.8298	1.92	440000	2.6993
2.8298	1.96	448000	2.7028
2.8298	1.99	456000	2.7213
2.8546	2.03	464000	2.7187
2.8546	2.06	472000	2.7107
2.8334	2.09	480000	2.7094
2.8334	2.13	488000	2.7309
2.854	2.16	496000	2.7340
2.854	2.2	504000	2.7264
2.8539	2.23	512000	2.7456
2.8539	2.27	520000	2.7412
2.8717	2.3	528000	2.7517
2.8717	2.34	536000	2.7474
2.8733	2.37	544000	2.7649
2.8733	2.41	552000	2.7536
2.8876	2.44	560000	2.7602
2.8876	2.48	568000	2.7617
2.905	2.51	576000	2.7663
2.905	2.55	584000	2.7840
2.8964	2.58	592000	2.7827
2.8964	2.62	600000	2.7769
2.9118	2.65	608000	2.7880
2.9118	2.69	616000	2.7923
2.9222	2.72	624000	2.7897
2.9222	2.76	632000	2.8131
2.9311	2.79	640000	2.8014
2.9311	2.83	648000	2.8287
2.9469	2.86	656000	2.8267
2.9469	2.9	664000	2.8234
2.9449	2.93	672000	2.8258
2.9449	2.97	680000	2.8252
2.9608	3.0	688000	2.8328
2.9608	3.04	696000	2.8387
2.9499	3.07	704000	2.8425
2.9499	3.11	712000	2.8431
2.9662	3.14	720000	2.8575
2.9662	3.18	728000	2.8588
2.9779	3.21	736000	2.8636
2.9779	3.25	744000	2.8631
2.9787	3.28	752000	2.8736
2.9787	3.32	760000	2.8701
3.0025	3.35	768000	2.8815
3.0025	3.39	776000	2.8750
2.999	3.42	784000	2.8860
2.999	3.46	792000	2.8876
3.0012	3.49	800000	2.9017
3.0012	3.53	808000	2.8898
3.0076	3.56	816000	2.9074
3.0076	3.6	824000	2.8906
3.0122	3.63	832000	2.9073
3.0122	3.67	840000	2.9154
3.0209	3.7	848000	2.9111
3.0209	3.74	856000	2.9094
3.0383	3.77	864000	2.9132
3.0383	3.81	872000	2.9201
3.043	3.84	880000	2.9280
3.043	3.88	888000	2.9231
3.0469	3.91	896000	2.9240
3.0469	3.95	904000	2.9272
3.0386	3.98	912000	2.9258
3.0386	4.02	920000	2.9494
3.0479	4.05	928000	2.9389
3.0479	4.08	936000	2.9377
3.0473	4.12	944000	2.9467
3.0473	4.15	952000	2.9495
3.0509	4.19	960000	2.9501
3.0509	4.22	968000	2.9470
3.0414	4.26	976000	2.9405
3.0414	4.29	984000	2.9444
3.0529	4.33	992000	2.9393
3.0529	4.36	1000000	2.9435
3.0594	4.4	1008000	2.9583
3.0594	4.43	1016000	2.9457
3.0479	4.47	1024000	2.9435
3.0479	4.5	1032000	2.9527
3.0564	4.54	1040000	2.9500
3.0564	4.57	1048000	2.9550
3.0554	4.61	1056000	2.9578
3.0554	4.64	1064000	2.9628
3.0626	4.68	1072000	2.9580
3.0626	4.71	1080000	2.9667
3.0722	4.75	1088000	2.9734
3.0722	4.78	1096000	2.9653
3.0731	4.82	1104000	2.9689
3.0731	4.85	1112000	2.9739
3.0724	4.89	1120000	2.9875
3.0724	4.92	1128000	2.9849
3.0656	4.96	1136000	2.9752
3.0656	4.99	1144000	2.9751
3.0829	5.03	1152000	2.9768
3.0829	5.06	1160000	2.9835
3.0785	5.1	1168000	2.9843
3.0785	5.13	1176000	3.0001
3.0704	5.17	1184000	2.9906
3.0704	5.2	1192000	2.9850
3.075	5.24	1200000	2.9931
3.075	5.27	1208000	2.9986
3.083	5.31	1216000	3.0008
3.083	5.34	1224000	3.0009
3.0708	5.38	1232000	3.0017
3.0708	5.41	1240000	2.9932
3.0896	5.45	1248000	2.9970
3.0896	5.48	1256000	3.0027
3.092	5.52	1264000	3.0002
3.092	5.55	1272000	2.9967
3.0916	5.59	1280000	2.9987
3.0916	5.62	1288000	2.9990
3.0938	5.66	1296000	3.0035
3.0938	5.69	1304000	2.9999
3.1039	5.73	1312000	3.0097
3.1039	5.76	1320000	3.0022
3.1059	5.8	1328000	3.0161
3.1059	5.83	1336000	3.0071
3.1014	5.87	1344000	3.0150
3.1014	5.9	1352000	2.9986
3.1048	5.94	1360000	3.0096
3.1048	5.97	1368000	3.0063
3.1099	6.01	1376000	3.0095
3.1099	6.04	1384000	3.0152
3.0891	6.08	1392000	3.0179
3.0891	6.11	1400000	3.0299
3.0979	6.14	1408000	3.0127
3.0979	6.18	1416000	3.0260
3.099	6.21	1424000	3.0187
3.099	6.25	1432000	3.0114
3.103	6.28	1440000	3.0191
3.103	6.32	1448000	3.0168
3.1066	6.35	1456000	3.0174
3.1066	6.39	1464000	3.0256
3.1164	6.42	1472000	3.0192
3.1164	6.46	1480000	3.0066
3.1066	6.49	1488000	3.0160
3.1066	6.53	1496000	3.0187
3.1014	6.56	1504000	3.0213
3.1014	6.6	1512000	3.0170
3.1043	6.63	1520000	3.0251
3.1043	6.67	1528000	3.0157
3.1073	6.7	1536000	3.0193
3.1073	6.74	1544000	3.0174
3.1131	6.77	1552000	3.0244
3.1131	6.81	1560000	3.0210
3.1033	6.84	1568000	3.0235
3.1033	6.88	1576000	3.0189
3.1087	6.91	1584000	3.0213
3.1087	6.95	1592000	3.0196
3.1065	6.98	1600000	3.0123
3.1065	7.02	1608000	3.0229
3.1019	7.05	1616000	3.0206
3.1019	7.09	1624000	3.0216
3.1023	7.12	1632000	3.0147
3.1023	7.16	1640000	3.0227
3.0969	7.19	1648000	3.0306
3.0969	7.23	1656000	3.0179
3.1034	7.26	1664000	3.0259
3.1034	7.3	1672000	3.0237
3.1077	7.33	1680000	3.0165
3.1077	7.37	1688000	3.0213
3.0983	7.4	1696000	3.0233
3.0983	7.44	1704000	3.0224
3.1014	7.47	1712000	3.0187
3.1014	7.51	1720000	3.0207
3.1052	7.54	1728000	3.0070
3.1052	7.58	1736000	3.0236
3.1062	7.61	1744000	3.0230
3.1062	7.65	1752000	3.0190
3.0941	7.68	1760000	3.0235
3.0941	7.72	1768000	3.0134
3.0942	7.75	1776000	3.0254
3.0942	7.79	1784000	3.0154
3.1089	7.82	1792000	3.0075
3.1089	7.86	1800000	3.0065
3.1117	7.89	1808000	3.0241
3.1117	7.93	1816000	3.0098
3.0958	7.96	1824000	3.0017
3.0958	8.0	1832000	3.0100
3.1177	8.03	1840000	3.0163
3.1177	8.07	1848000	3.0100
3.097	8.1	1856000	3.0099
3.097	8.13	1864000	3.0287
3.1039	8.17	1872000	3.0107
3.1039	8.2	1880000	3.0103
3.0987	8.24	1888000	3.0200
3.0987	8.27	1896000	3.0197
3.1029	8.31	1904000	3.0141
3.1029	8.34	1912000	3.0254
3.1053	8.38	1920000	3.0128
3.1053	8.41	1928000	3.0140
3.1042	8.45	1936000	3.0233
3.1042	8.48	1944000	3.0156
3.1039	8.52	1952000	3.0125
3.1039	8.55	1960000	3.0144
3.1044	8.59	1968000	3.0247
3.1044	8.62	1976000	3.0140
3.1172	8.66	1984000	3.0106
3.1172	8.69	1992000	3.0161
3.1106	8.73	2000000	3.0168
3.1106	8.76	2008000	3.0230
3.107	8.8	2016000	3.0207
3.107	8.83	2024000	3.0218
3.1153	8.87	2032000	3.0157
3.1153	8.9	2040000	3.0326
3.1104	8.94	2048000	3.0194
3.1104	8.97	2056000	3.0211
3.1206	9.01	2064000	3.0197
3.1206	9.04	2072000	3.0311
3.1101	9.08	2080000	3.0218
3.1101	9.11	2088000	3.0224
3.1166	9.15	2096000	3.0326
3.1166	9.18	2104000	3.0252
3.106	9.22	2112000	3.0259
3.106	9.25	2120000	3.0116
3.1067	9.29	2128000	3.0312
3.1067	9.32	2136000	3.0125
3.1152	9.36	2144000	3.0147
3.1152	9.39	2152000	3.0210
3.1122	9.43	2160000	3.0388
3.1122	9.46	2168000	3.0409
3.1092	9.5	2176000	3.0364
3.1092	9.53	2184000	3.0270
3.1117	9.57	2192000	3.0326
3.1117	9.6	2200000	3.0381
3.1089	9.64	2208000	3.0258
3.1089	9.67	2216000	3.0287
3.1195	9.71	2224000	3.0326
3.1195	9.74	2232000	3.0374
3.1172	9.78	2240000	3.0227
3.1172	9.81	2248000	3.0281
3.1271	9.85	2256000	3.0274
3.1271	9.88	2264000	3.0225
3.1112	9.92	2272000	3.0248
3.1112	9.95	2280000	3.0188
3.1179	9.99	2288000	3.0227
3.1179	10.02	2296000	3.0337
3.1178	10.06	2304000	3.0241
3.1178	10.09	2312000	3.0247
3.1148	10.13	2320000	3.0342
3.1148	10.16	2328000	3.0202
3.1153	10.19	2336000	3.0294
3.1153	10.23	2344000	3.0282
3.1097	10.26	2352000	3.0198
3.1097	10.3	2360000	3.0188
3.1041	10.33	2368000	3.0225
3.1041	10.37	2376000	3.0212
3.0992	10.4	2384000	3.0208
3.0992	10.44	2392000	3.0191
3.1135	10.47	2400000	3.0245

Framework versions

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

Task: Fill-Mask
Library: transformers
Type: roberta
License: mit
Created: Nov 11, 2023
Updated: Nov 13, 2023