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2020-Q3-50p-filtered-random

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2020-Q3-50p-filtered-random / README.md
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metadata
license: mit
base_model: cardiffnlp/twitter-roberta-base-2019-90m
tags:
  - generated_from_trainer
model-index:
  - name: 2020-Q3-50p-filtered
    results: []

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