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2020-Q2-filtered_tweets_tok
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: 4.8842
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 | 0.17 | 8000 | 5.6509 |
| 5.9478 | 0.34 | 16000 | 5.6081 |
| 5.9478 | 0.51 | 24000 | 5.5775 |
| 5.7104 | 0.68 | 32000 | 5.5817 |
| 5.7104 | 0.85 | 40000 | 5.6254 |
| 5.6557 | 1.02 | 48000 | 5.4847 |
| 5.6557 | 1.19 | 56000 | 5.5621 |
| 5.6555 | 1.36 | 64000 | 5.4447 |
| 5.6555 | 1.54 | 72000 | 5.3280 |
| 5.5006 | 1.71 | 80000 | 5.3192 |
| 5.5006 | 1.88 | 88000 | 5.4208 |
| 5.4669 | 2.05 | 96000 | 5.2280 |
| 5.4669 | 2.22 | 104000 | 5.2483 |
| 5.3498 | 2.39 | 112000 | 5.2404 |
| 5.3498 | 2.56 | 120000 | 5.2381 |
| 5.3536 | 2.73 | 128000 | 5.1998 |
| 5.3536 | 2.9 | 136000 | 5.2001 |
| 5.3176 | 3.07 | 144000 | 5.2037 |
| 5.3176 | 3.24 | 152000 | 5.1937 |
| 5.2808 | 3.41 | 160000 | 5.1881 |
| 5.2808 | 3.58 | 168000 | 5.1804 |
| 5.271 | 3.75 | 176000 | 5.1523 |
| 5.271 | 3.92 | 184000 | 5.1187 |
| 5.253 | 4.09 | 192000 | 5.1478 |
| 5.253 | 4.26 | 200000 | 5.1414 |
| 5.2326 | 4.43 | 208000 | 5.1405 |
| 5.2326 | 4.61 | 216000 | 5.1351 |
| 5.2439 | 4.78 | 224000 | 5.1093 |
| 5.2439 | 4.95 | 232000 | 5.1358 |
| 5.2355 | 5.12 | 240000 | 5.1239 |
| 5.2355 | 5.29 | 248000 | 5.0874 |
| 5.2289 | 5.46 | 256000 | 5.1055 |
| 5.2289 | 5.63 | 264000 | 5.1027 |
| 5.2098 | 5.8 | 272000 | 5.0988 |
| 5.2098 | 5.97 | 280000 | 5.0715 |
| 5.2024 | 6.14 | 288000 | 5.0816 |
| 5.2024 | 6.31 | 296000 | 5.0613 |
| 5.1833 | 6.48 | 304000 | 5.0974 |
| 5.1833 | 6.65 | 312000 | 5.1084 |
| 5.1781 | 6.82 | 320000 | 5.0963 |
| 5.1781 | 6.99 | 328000 | 5.0823 |
| 5.1903 | 7.16 | 336000 | 5.0941 |
| 5.1903 | 7.33 | 344000 | 5.0810 |
| 5.1754 | 7.51 | 352000 | 5.0810 |
| 5.1754 | 7.68 | 360000 | 5.0585 |
| 5.1792 | 7.85 | 368000 | 5.0687 |
| 5.1792 | 8.02 | 376000 | 5.0723 |
| 5.1573 | 8.19 | 384000 | 5.0477 |
| 5.1573 | 8.36 | 392000 | 5.0620 |
| 5.1853 | 8.53 | 400000 | 5.0552 |
| 5.1853 | 8.7 | 408000 | 5.0672 |
| 5.1523 | 8.87 | 416000 | 5.0564 |
| 5.1523 | 9.04 | 424000 | 5.0335 |
| 5.149 | 9.21 | 432000 | 5.0277 |
| 5.149 | 9.38 | 440000 | 5.0309 |
| 5.13 | 9.55 | 448000 | 5.0347 |
| 5.13 | 9.72 | 456000 | 5.0324 |
| 5.1282 | 9.89 | 464000 | 4.9971 |
| 5.1282 | 10.06 | 472000 | 5.0343 |
| 5.1254 | 10.23 | 480000 | 5.0129 |
| 5.1254 | 10.4 | 488000 | 5.0218 |
| 5.1292 | 10.58 | 496000 | 5.0022 |
| 5.1292 | 10.75 | 504000 | 5.0418 |
| 5.1275 | 10.92 | 512000 | 5.0714 |
| 5.1275 | 11.09 | 520000 | 5.0253 |
| 5.1457 | 11.26 | 528000 | 5.0101 |
| 5.1457 | 11.43 | 536000 | 5.0224 |
| 5.1334 | 11.6 | 544000 | 5.0729 |
| 5.1334 | 11.77 | 552000 | 5.0300 |
| 5.1446 | 11.94 | 560000 | 5.0518 |
| 5.1446 | 12.11 | 568000 | 5.0379 |
| 5.1524 | 12.28 | 576000 | 5.0535 |
| 5.1524 | 12.45 | 584000 | 5.0351 |
| 5.1365 | 12.62 | 592000 | 5.0308 |
| 5.1365 | 12.79 | 600000 | 5.0137 |
| 5.1247 | 12.96 | 608000 | 5.0229 |
| 5.1247 | 13.13 | 616000 | 5.0306 |
| 5.1387 | 13.3 | 624000 | 5.0162 |
| 5.1387 | 13.47 | 632000 | 5.0320 |
| 5.1239 | 13.65 | 640000 | 5.0465 |
| 5.1239 | 13.82 | 648000 | 5.0459 |
| 5.1387 | 13.99 | 656000 | 5.0585 |
| 5.1387 | 14.16 | 664000 | 5.0233 |
| 5.1157 | 14.33 | 672000 | 5.0064 |
| 5.1157 | 14.5 | 680000 | 5.0242 |
| 5.133 | 14.67 | 688000 | 5.0171 |
| 5.133 | 14.84 | 696000 | 5.0239 |
| 5.1334 | 15.01 | 704000 | 5.0222 |
| 5.1334 | 15.18 | 712000 | 5.0351 |
| 5.1158 | 15.35 | 720000 | 5.0249 |
| 5.1158 | 15.52 | 728000 | 5.0106 |
| 5.1355 | 15.69 | 736000 | 5.0046 |
| 5.1355 | 15.86 | 744000 | 5.0288 |
| 5.1329 | 16.03 | 752000 | 5.0311 |
| 5.1329 | 16.2 | 760000 | 5.0253 |
| 5.1138 | 16.37 | 768000 | 5.0437 |
| 5.1138 | 16.55 | 776000 | 4.9991 |
| 5.1374 | 16.72 | 784000 | 4.9948 |
| 5.1374 | 16.89 | 792000 | 5.0045 |
| 5.126 | 17.06 | 800000 | 5.0276 |
| 5.126 | 17.23 | 808000 | 5.0016 |
| 5.1277 | 17.4 | 816000 | 5.0284 |
| 5.1277 | 17.57 | 824000 | 5.0190 |
| 5.1321 | 17.74 | 832000 | 5.0211 |
| 5.1321 | 17.91 | 840000 | 5.0467 |
| 5.1348 | 18.08 | 848000 | 5.0174 |
| 5.1348 | 18.25 | 856000 | 5.0046 |
| 5.1213 | 18.42 | 864000 | 5.0266 |
| 5.1213 | 18.59 | 872000 | 5.0151 |
| 5.1335 | 18.76 | 880000 | 5.0176 |
| 5.1335 | 18.93 | 888000 | 5.0264 |
| 5.1237 | 19.1 | 896000 | 5.0594 |
| 5.1237 | 19.27 | 904000 | 5.0464 |
| 5.1276 | 19.44 | 912000 | 5.0413 |
| 5.1276 | 19.62 | 920000 | 5.0071 |
| 5.1383 | 19.79 | 928000 | 4.9980 |
| 5.1383 | 19.96 | 936000 | 5.0451 |
| 5.1523 | 20.13 | 944000 | 5.0194 |
| 5.1523 | 20.3 | 952000 | 5.0519 |
| 5.1372 | 20.47 | 960000 | 5.0110 |
| 5.1372 | 20.64 | 968000 | 5.0041 |
| 5.1401 | 20.81 | 976000 | 5.0224 |
| 5.1401 | 20.98 | 984000 | 4.9984 |
| 5.1311 | 21.15 | 992000 | 5.0216 |
| 5.1311 | 21.32 | 1000000 | 4.9841 |
| 5.1366 | 21.49 | 1008000 | 5.0530 |
| 5.1366 | 21.66 | 1016000 | 5.0359 |
| 5.1312 | 21.83 | 1024000 | 5.0323 |
| 5.1312 | 22.0 | 1032000 | 5.0248 |
| 5.1318 | 22.17 | 1040000 | 5.0245 |
| 5.1318 | 22.34 | 1048000 | 5.0257 |
| 5.1339 | 22.52 | 1056000 | 5.0069 |
| 5.1339 | 22.69 | 1064000 | 5.0159 |
| 5.1222 | 22.86 | 1072000 | 5.0206 |
| 5.1222 | 23.03 | 1080000 | 5.0092 |
| 5.1321 | 23.2 | 1088000 | 4.9797 |
| 5.1321 | 23.37 | 1096000 | 4.9977 |
| 5.1189 | 23.54 | 1104000 | 4.9830 |
| 5.1189 | 23.71 | 1112000 | 4.9965 |
| 5.1141 | 23.88 | 1120000 | 5.0003 |
| 5.1141 | 24.05 | 1128000 | 4.9822 |
| 5.106 | 24.22 | 1136000 | 4.9971 |
| 5.106 | 24.39 | 1144000 | 4.9923 |
| 5.0859 | 24.56 | 1152000 | 4.9816 |
| 5.0859 | 24.73 | 1160000 | 4.9514 |
| 5.0885 | 24.9 | 1168000 | 4.9693 |
| 5.0885 | 25.07 | 1176000 | 5.0144 |
| 5.0716 | 25.24 | 1184000 | 4.9865 |
| 5.0716 | 25.41 | 1192000 | 4.9573 |
| 5.0794 | 25.59 | 1200000 | 4.9877 |
| 5.0794 | 25.76 | 1208000 | 5.0053 |
| 5.0817 | 25.93 | 1216000 | 4.9731 |
| 5.0817 | 26.1 | 1224000 | 4.9769 |
| 5.0861 | 26.27 | 1232000 | 4.9781 |
| 5.0861 | 26.44 | 1240000 | 4.9878 |
| 5.0799 | 26.61 | 1248000 | 5.0172 |
| 5.0799 | 26.78 | 1256000 | 4.9670 |
| 5.0822 | 26.95 | 1264000 | 5.0037 |
| 5.0822 | 27.12 | 1272000 | 4.9914 |
| 5.0786 | 27.29 | 1280000 | 4.9966 |
| 5.0786 | 27.46 | 1288000 | 4.9853 |
| 5.0672 | 27.63 | 1296000 | 4.9634 |
| 5.0672 | 27.8 | 1304000 | 4.9905 |
| 5.0861 | 27.97 | 1312000 | 4.9414 |
| 5.0861 | 28.14 | 1320000 | 4.9734 |
| 5.0675 | 28.31 | 1328000 | 4.9747 |
| 5.0675 | 28.48 | 1336000 | 4.9677 |
| 5.0642 | 28.66 | 1344000 | 4.9803 |
| 5.0642 | 28.83 | 1352000 | 4.9675 |
| 5.0699 | 29.0 | 1360000 | 4.9468 |
| 5.0699 | 29.17 | 1368000 | 4.9513 |
| 5.0592 | 29.34 | 1376000 | 4.9693 |
| 5.0592 | 29.51 | 1384000 | 4.9732 |
| 5.0681 | 29.68 | 1392000 | 4.9544 |
| 5.0681 | 29.85 | 1400000 | 4.9584 |
| 5.072 | 30.02 | 1408000 | 4.9320 |
| 5.072 | 30.19 | 1416000 | 4.9419 |
| 5.054 | 30.36 | 1424000 | 4.9438 |
| 5.054 | 30.53 | 1432000 | 4.9671 |
| 5.0611 | 30.7 | 1440000 | 4.9634 |
| 5.0611 | 30.87 | 1448000 | 4.9556 |
| 5.0547 | 31.04 | 1456000 | 4.9667 |
| 5.0547 | 31.21 | 1464000 | 4.9766 |
| 5.0563 | 31.38 | 1472000 | 4.9446 |
| 5.0563 | 31.56 | 1480000 | 4.9825 |
| 5.0492 | 31.73 | 1488000 | 4.9647 |
| 5.0492 | 31.9 | 1496000 | 4.9637 |
| 5.058 | 32.07 | 1504000 | 4.9656 |
| 5.058 | 32.24 | 1512000 | 4.9561 |
| 5.0389 | 32.41 | 1520000 | 4.9542 |
| 5.0389 | 32.58 | 1528000 | 4.9447 |
| 5.0638 | 32.75 | 1536000 | 4.9519 |
| 5.0638 | 32.92 | 1544000 | 4.9442 |
| 5.0622 | 33.09 | 1552000 | 4.9587 |
| 5.0622 | 33.26 | 1560000 | 4.9860 |
| 5.0476 | 33.43 | 1568000 | 4.9890 |
| 5.0476 | 33.6 | 1576000 | 4.9601 |
| 5.0549 | 33.77 | 1584000 | 4.9996 |
| 5.0549 | 33.94 | 1592000 | 4.9962 |
| 5.0577 | 34.11 | 1600000 | 4.9577 |
| 5.0577 | 34.28 | 1608000 | 4.9630 |
| 5.0626 | 34.45 | 1616000 | 4.9610 |
| 5.0626 | 34.63 | 1624000 | 4.9953 |
| 5.0669 | 34.8 | 1632000 | 5.0039 |
| 5.0669 | 34.97 | 1640000 | 4.9921 |
| 5.0664 | 35.14 | 1648000 | 4.9458 |
| 5.0664 | 35.31 | 1656000 | 4.9539 |
| 5.0655 | 35.48 | 1664000 | 5.0032 |
| 5.0655 | 35.65 | 1672000 | 4.9666 |
| 5.0654 | 35.82 | 1680000 | 4.9507 |
| 5.0654 | 35.99 | 1688000 | 4.9812 |
| 5.0618 | 36.16 | 1696000 | 4.9765 |
| 5.0618 | 36.33 | 1704000 | 4.9612 |
| 5.0576 | 36.5 | 1712000 | 4.9747 |
| 5.0576 | 36.67 | 1720000 | 4.9533 |
| 5.0618 | 36.84 | 1728000 | 4.9686 |
| 5.0618 | 37.01 | 1736000 | 4.9701 |
| 5.0654 | 37.18 | 1744000 | 4.9675 |
| 5.0654 | 37.35 | 1752000 | 4.9754 |
| 5.0575 | 37.53 | 1760000 | 4.9942 |
| 5.0575 | 37.7 | 1768000 | 4.9162 |
| 5.0692 | 37.87 | 1776000 | 4.9908 |
| 5.0692 | 38.04 | 1784000 | 4.9528 |
| 5.051 | 38.21 | 1792000 | 4.9697 |
| 5.051 | 38.38 | 1800000 | 4.9675 |
| 5.0594 | 38.55 | 1808000 | 4.9449 |
| 5.0594 | 38.72 | 1816000 | 4.9458 |
| 5.056 | 38.89 | 1824000 | 4.9347 |
| 5.056 | 39.06 | 1832000 | 4.9223 |
| 5.0576 | 39.23 | 1840000 | 4.8934 |
| 5.0576 | 39.4 | 1848000 | 4.9266 |
| 5.0377 | 39.57 | 1856000 | 4.9146 |
| 5.0377 | 39.74 | 1864000 | 4.9183 |
| 5.0531 | 39.91 | 1872000 | 4.9271 |
| 5.0531 | 40.08 | 1880000 | 4.8958 |
| 5.0499 | 40.25 | 1888000 | 4.9203 |
| 5.0499 | 40.42 | 1896000 | 4.9353 |
| 5.0251 | 40.6 | 1904000 | 4.9269 |
| 5.0251 | 40.77 | 1912000 | 4.9150 |
| 5.0313 | 40.94 | 1920000 | 4.9020 |
| 5.0313 | 41.11 | 1928000 | 4.9320 |
| 5.0305 | 41.28 | 1936000 | 4.9189 |
| 5.0305 | 41.45 | 1944000 | 4.9360 |
| 5.0327 | 41.62 | 1952000 | 4.9290 |
| 5.0327 | 41.79 | 1960000 | 4.8970 |
| 5.0202 | 41.96 | 1968000 | 4.8916 |
| 5.0202 | 42.13 | 1976000 | 4.9115 |
| 5.0226 | 42.3 | 1984000 | 4.9156 |
| 5.0226 | 42.47 | 1992000 | 4.9248 |
| 5.033 | 42.64 | 2000000 | 4.9289 |
| 5.033 | 42.81 | 2008000 | 4.9227 |
| 5.0218 | 42.98 | 2016000 | 4.9529 |
| 5.0218 | 43.15 | 2024000 | 4.9438 |
| 5.0156 | 43.32 | 2032000 | 4.9479 |
| 5.0156 | 43.49 | 2040000 | 4.9315 |
| 5.018 | 43.67 | 2048000 | 4.9434 |
| 5.018 | 43.84 | 2056000 | 4.9204 |
| 5.0087 | 44.01 | 2064000 | 4.9229 |
| 5.0087 | 44.18 | 2072000 | 4.9211 |
| 5.0035 | 44.35 | 2080000 | 4.9196 |
| 5.0035 | 44.52 | 2088000 | 4.9044 |
| 5.0213 | 44.69 | 2096000 | 4.9159 |
| 5.0213 | 44.86 | 2104000 | 4.9063 |
| 4.9959 | 45.03 | 2112000 | 4.9323 |
| 4.9959 | 45.2 | 2120000 | 4.9227 |
| 5.0006 | 45.37 | 2128000 | 4.9388 |
| 5.0006 | 45.54 | 2136000 | 4.9221 |
| 5.0011 | 45.71 | 2144000 | 4.9219 |
| 5.0011 | 45.88 | 2152000 | 4.9295 |
| 5.0125 | 46.05 | 2160000 | 4.8980 |
| 5.0125 | 46.22 | 2168000 | 4.9241 |
| 5.0106 | 46.39 | 2176000 | 4.9191 |
| 5.0106 | 46.57 | 2184000 | 4.9093 |
| 5.0004 | 46.74 | 2192000 | 4.9341 |
| 5.0004 | 46.91 | 2200000 | 4.9223 |
| 5.0044 | 47.08 | 2208000 | 4.9181 |
| 5.0044 | 47.25 | 2216000 | 4.8962 |
| 4.9803 | 47.42 | 2224000 | 4.9037 |
| 4.9803 | 47.59 | 2232000 | 4.9239 |
| 4.9937 | 47.76 | 2240000 | 4.9030 |
| 4.9937 | 47.93 | 2248000 | 4.9277 |
| 5.0041 | 48.1 | 2256000 | 4.9068 |
| 5.0041 | 48.27 | 2264000 | 4.9054 |
| 4.9817 | 48.44 | 2272000 | 4.9266 |
| 4.9817 | 48.61 | 2280000 | 4.9289 |
| 4.9818 | 48.78 | 2288000 | 4.9174 |
| 4.9818 | 48.95 | 2296000 | 4.8848 |
| 4.9812 | 49.12 | 2304000 | 4.8895 |
| 4.9812 | 49.29 | 2312000 | 4.9083 |
| 4.9839 | 49.46 | 2320000 | 4.9282 |
| 4.9839 | 49.64 | 2328000 | 4.8845 |
| 4.9855 | 49.81 | 2336000 | 4.9038 |
| 4.9855 | 49.98 | 2344000 | 4.9057 |
| 4.9861 | 50.15 | 2352000 | 4.9299 |
| 4.9861 | 50.32 | 2360000 | 4.8779 |
| 4.9697 | 50.49 | 2368000 | 4.8845 |
| 4.9697 | 50.66 | 2376000 | 4.9017 |
| 4.9887 | 50.83 | 2384000 | 4.8914 |
| 4.9887 | 51.0 | 2392000 | 4.8991 |
| 4.9676 | 51.17 | 2400000 | 4.8992 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
