2020-Q3-50p-filtered-prog_from_Q2

DouglasPontes/2020-Q3-50p-filtered-prog_from_Q2

2020-Q3-50p-filtered-prog_from_Q2 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: Feb 26, 2024
Updated: Mar 4, 2024
SHA: 05d3d010

Try a prompt

Base models

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

Model card

From the Hugging Face model README

2020-Q3-50p-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: 2.6316

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	2.9380
3.1308	0.07	16000	2.8593
3.1308	0.1	24000	2.8063
2.9519	0.14	32000	2.7832
2.9519	0.17	40000	2.7521
2.889	0.2	48000	2.7322
2.889	0.24	56000	2.7259
2.8592	0.27	64000	2.7218
2.8592	0.3	72000	2.7106
2.8345	0.34	80000	2.7071
2.8345	0.37	88000	2.6903
2.8303	0.41	96000	2.7001
2.8303	0.44	104000	2.6929
2.824	0.47	112000	2.6907
2.824	0.51	120000	2.6852
2.8223	0.54	128000	2.6804
2.8223	0.57	136000	2.6727
2.8141	0.61	144000	2.6784
2.8141	0.64	152000	2.6775
2.8124	0.68	160000	2.6723
2.8124	0.71	168000	2.6683
2.8042	0.74	176000	2.6712
2.8042	0.78	184000	2.6661
2.8051	0.81	192000	2.6783
2.8051	0.85	200000	2.6683
2.798	0.88	208000	2.6656
2.798	0.91	216000	2.6659
2.8043	0.95	224000	2.6700
2.8043	0.98	232000	2.6680
2.8055	1.01	240000	2.6597
2.8055	1.05	248000	2.6597
2.8048	1.08	256000	2.6569
2.8048	1.12	264000	2.6502
2.806	1.15	272000	2.6593
2.806	1.18	280000	2.6597
2.8012	1.22	288000	2.6604
2.8012	1.25	296000	2.6545
2.8029	1.28	304000	2.6571
2.8029	1.32	312000	2.6534
2.7991	1.35	320000	2.6650
2.7991	1.39	328000	2.6680
2.7949	1.42	336000	2.6544
2.7949	1.45	344000	2.6460
2.7972	1.49	352000	2.6553
2.7972	1.52	360000	2.6428
2.7924	1.56	368000	2.6536
2.7924	1.59	376000	2.6550
2.805	1.62	384000	2.6524
2.805	1.66	392000	2.6524
2.7972	1.69	400000	2.6579
2.7972	1.72	408000	2.6500
2.8003	1.76	416000	2.6526
2.8003	1.79	424000	2.6444
2.8005	1.83	432000	2.6463
2.8005	1.86	440000	2.6549
2.7957	1.89	448000	2.6530
2.7957	1.93	456000	2.6504
2.7949	1.96	464000	2.6480
2.7949	1.99	472000	2.6497
2.7978	2.03	480000	2.6490
2.7978	2.06	488000	2.6505
2.8041	2.1	496000	2.6388
2.8041	2.13	504000	2.6460
2.7935	2.16	512000	2.6519
2.7935	2.2	520000	2.6494
2.7982	2.23	528000	2.6550
2.7982	2.27	536000	2.6460
2.7949	2.3	544000	2.6497
2.7949	2.33	552000	2.6478
2.7953	2.37	560000	2.6487
2.7953	2.4	568000	2.6400
2.7942	2.43	576000	2.6440
2.7942	2.47	584000	nan
2.803	2.5	592000	2.6455
2.803	2.54	600000	2.6401
2.7961	2.57	608000	2.6511
2.7961	2.6	616000	2.6401
2.7975	2.64	624000	2.6437
2.7975	2.67	632000	2.6432
2.7946	2.7	640000	2.6461
2.7946	2.74	648000	2.6491
2.7963	2.77	656000	2.6442
2.7963	2.81	664000	2.6416
2.7924	2.84	672000	2.6403
2.7924	2.87	680000	2.6466
2.8004	2.91	688000	2.6436
2.8004	2.94	696000	2.6447
2.8039	2.98	704000	2.6412
2.8039	3.01	712000	2.6400
2.7958	3.04	720000	2.6419
2.7958	3.08	728000	2.6413
2.7967	3.11	736000	nan
2.7967	3.14	744000	2.6399
2.7934	3.18	752000	2.6405
2.7934	3.21	760000	2.6387
2.7988	3.25	768000	2.6463
2.7988	3.28	776000	2.6308
2.793	3.31	784000	2.6343
2.793	3.35	792000	2.6358
2.797	3.38	800000	2.6397
2.797	3.41	808000	2.6341
2.7832	3.45	816000	2.6394
2.7832	3.48	824000	2.6341
2.792	3.52	832000	2.6424
2.792	3.55	840000	2.6380
2.7945	3.58	848000	2.6373
2.7945	3.62	856000	2.6366
2.7876	3.65	864000	2.6409
2.7876	3.69	872000	2.6382
2.7975	3.72	880000	2.6259
2.7975	3.75	888000	2.6443
2.7965	3.79	896000	2.6248
2.7965	3.82	904000	2.6395
2.7991	3.85	912000	2.6325
2.7991	3.89	920000	2.6354
2.7947	3.92	928000	2.6342
2.7947	3.96	936000	2.6290
2.7977	3.99	944000	2.6315
2.7977	4.02	952000	2.6347
2.8	4.06	960000	2.6318
2.8	4.09	968000	2.6328
2.7945	4.12	976000	2.6315
2.7945	4.16	984000	2.6297
2.7946	4.19	992000	2.6378
2.7946	4.23	1000000	2.6328
2.7962	4.26	1008000	2.6296
2.7962	4.29	1016000	2.6347
2.7932	4.33	1024000	2.6355
2.7932	4.36	1032000	2.6364
2.7992	4.4	1040000	2.6327
2.7992	4.43	1048000	2.6273
2.7922	4.46	1056000	2.6301
2.7922	4.5	1064000	2.6350
2.7939	4.53	1072000	2.6358
2.7939	4.56	1080000	nan
2.789	4.6	1088000	2.6288
2.789	4.63	1096000	2.6267
2.7965	4.67	1104000	2.6229
2.7965	4.7	1112000	2.6331
2.7963	4.73	1120000	2.6368
2.7963	4.77	1128000	2.6436
2.7993	4.8	1136000	2.6363
2.7993	4.83	1144000	2.6288
2.7952	4.87	1152000	2.6294
2.7952	4.9	1160000	2.6337
2.7972	4.94	1168000	2.6235
2.7972	4.97	1176000	2.6405
2.7988	5.0	1184000	2.6266
2.7988	5.04	1192000	2.6328
2.7901	5.07	1200000	2.6335
2.7901	5.11	1208000	2.6405
2.7975	5.14	1216000	2.6246
2.7975	5.17	1224000	2.6315
2.7974	5.21	1232000	2.6390
2.7974	5.24	1240000	2.6318
2.7909	5.27	1248000	2.6237
2.7909	5.31	1256000	2.6343
2.7899	5.34	1264000	2.6288
2.7899	5.38	1272000	2.6297
2.7937	5.41	1280000	2.6343
2.7937	5.44	1288000	2.6306
2.7916	5.48	1296000	2.6268
2.7916	5.51	1304000	2.6317
2.7874	5.54	1312000	2.6380
2.7874	5.58	1320000	2.6281
2.7967	5.61	1328000	2.6334
2.7967	5.65	1336000	2.6273
2.791	5.68	1344000	2.6339
2.791	5.71	1352000	2.6276
2.791	5.75	1360000	2.6247
2.791	5.78	1368000	2.6303
2.7909	5.82	1376000	2.6355
2.7909	5.85	1384000	2.6352
2.7833	5.88	1392000	2.6321
2.7833	5.92	1400000	2.6336
2.7944	5.95	1408000	2.6312
2.7944	5.98	1416000	2.6223
2.8001	6.02	1424000	2.6369
2.8001	6.05	1432000	2.6299
2.7954	6.09	1440000	2.6373
2.7954	6.12	1448000	2.6223
2.7914	6.15	1456000	2.6225
2.7914	6.19	1464000	2.6277
2.7896	6.22	1472000	2.6334
2.7896	6.26	1480000	2.6260
2.7925	6.29	1488000	2.6312
2.7925	6.32	1496000	2.6336
2.7976	6.36	1504000	2.6270
2.7976	6.39	1512000	2.6286
2.8025	6.42	1520000	2.6320
2.8025	6.46	1528000	2.6252
2.7953	6.49	1536000	2.6319
2.7953	6.53	1544000	2.6223
2.7994	6.56	1552000	2.6358
2.7994	6.59	1560000	2.6296
2.7966	6.63	1568000	2.6360
2.7966	6.66	1576000	2.6327
2.7883	6.69	1584000	2.6365
2.7883	6.73	1592000	2.6258
2.7963	6.76	1600000	2.6401
2.7963	6.8	1608000	2.6318
2.7923	6.83	1616000	2.6330
2.7923	6.86	1624000	2.6372
2.789	6.9	1632000	2.6363
2.789	6.93	1640000	2.6346
2.7883	6.97	1648000	2.6292
2.7883	7.0	1656000	2.6284
2.7965	7.03	1664000	2.6408
2.7965	7.07	1672000	2.6296
2.7963	7.1	1680000	2.6331
2.7963	7.13	1688000	2.6339
2.7911	7.17	1696000	2.6206
2.7911	7.2	1704000	2.6268
2.794	7.24	1712000	2.6278
2.794	7.27	1720000	2.6242
2.7893	7.3	1728000	2.6329
2.7893	7.34	1736000	2.6342
2.7935	7.37	1744000	2.6329
2.7935	7.4	1752000	2.6294
2.7936	7.44	1760000	2.6301
2.7936	7.47	1768000	2.6295
2.7922	7.51	1776000	2.6261
2.7922	7.54	1784000	2.6370
2.7911	7.57	1792000	2.6364
2.7911	7.61	1800000	2.6232
2.795	7.64	1808000	2.6201
2.795	7.68	1816000	2.6329
2.7898	7.71	1824000	2.6249
2.7898	7.74	1832000	2.6249
2.7931	7.78	1840000	2.6361
2.7931	7.81	1848000	nan
2.7919	7.84	1856000	2.6270
2.7919	7.88	1864000	2.6362
2.7833	7.91	1872000	2.6278
2.7833	7.95	1880000	2.6232
2.8067	7.98	1888000	2.6260
2.8067	8.01	1896000	2.6262
2.7953	8.05	1904000	2.6271
2.7953	8.08	1912000	2.6270
2.7953	8.11	1920000	2.6305
2.7953	8.15	1928000	2.6254
2.7881	8.18	1936000	2.6297
2.7881	8.22	1944000	2.6271
2.7928	8.25	1952000	2.6254
2.7928	8.28	1960000	2.6286
2.8003	8.32	1968000	2.6330
2.8003	8.35	1976000	2.6286
2.7935	8.39	1984000	2.6408
2.7935	8.42	1992000	2.6275
2.7925	8.45	2000000	2.6259
2.7925	8.49	2008000	2.6302
2.7924	8.52	2016000	2.6320
2.7924	8.55	2024000	2.6295
2.799	8.59	2032000	2.6259
2.799	8.62	2040000	2.6246
2.7983	8.66	2048000	2.6295
2.7983	8.69	2056000	2.6194
2.7901	8.72	2064000	2.6258
2.7901	8.76	2072000	2.6334
2.7956	8.79	2080000	2.6361
2.7956	8.82	2088000	2.6177
2.8008	8.86	2096000	2.6322
2.8008	8.89	2104000	2.6281
2.791	8.93	2112000	2.6249
2.791	8.96	2120000	2.6284
2.7933	8.99	2128000	2.6270
2.7933	9.03	2136000	2.6241
2.7825	9.06	2144000	2.6254
2.7825	9.1	2152000	2.6283
2.7854	9.13	2160000	2.6343
2.7854	9.16	2168000	2.6208
2.7949	9.2	2176000	2.6293
2.7949	9.23	2184000	2.6266
2.7938	9.26	2192000	2.6270
2.7938	9.3	2200000	2.6238
2.7905	9.33	2208000	2.6282
2.7905	9.37	2216000	2.6246
2.8004	9.4	2224000	2.6274
2.8004	9.43	2232000	2.6252
2.7921	9.47	2240000	2.6343
2.7921	9.5	2248000	2.6328
2.7964	9.53	2256000	2.6206
2.7964	9.57	2264000	2.6235
2.7954	9.6	2272000	2.6288
2.7954	9.64	2280000	2.6204
2.7902	9.67	2288000	2.6232
2.7902	9.7	2296000	2.6239
2.8046	9.74	2304000	2.6241
2.8046	9.77	2312000	2.6259
2.793	9.81	2320000	2.6275
2.793	9.84	2328000	2.6264
2.7893	9.87	2336000	2.6332
2.7893	9.91	2344000	2.6214
2.7898	9.94	2352000	2.6318
2.7898	9.97	2360000	2.6239
2.7906	10.01	2368000	2.6215
2.7906	10.04	2376000	2.6336
2.7942	10.08	2384000	2.6218
2.7942	10.11	2392000	2.6299
2.7997	10.14	2400000	2.6303

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: Feb 26, 2024
Updated: Mar 4, 2024