--- license: mit base_model: cardiffnlp/twitter-roberta-base-2019-90m tags: - generated_from_trainer model-index: - name: 2020-Q1-75p-filtered-random results: [] --- # 2020-Q1-75p-filtered-random This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-2019-90m](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9103 ## 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 | 0.07 | 8000 | 2.2397 | | 2.4342 | 0.13 | 16000 | 2.1511 | | 2.4342 | 0.2 | 24000 | 2.1109 | | 2.2417 | 0.26 | 32000 | 2.0789 | | 2.2417 | 0.33 | 40000 | 2.0657 | | 2.1852 | 0.39 | 48000 | 2.0397 | | 2.1852 | 0.46 | 56000 | 2.0303 | | 2.1511 | 0.52 | 64000 | 2.0248 | | 2.1511 | 0.59 | 72000 | 2.0221 | | 2.1261 | 0.65 | 80000 | 2.0128 | | 2.1261 | 0.72 | 88000 | 2.0067 | | 2.1179 | 0.78 | 96000 | 2.0039 | | 2.1179 | 0.85 | 104000 | 1.9973 | | 2.1097 | 0.91 | 112000 | 1.9835 | | 2.1097 | 0.98 | 120000 | 1.9983 | | 2.1031 | 1.04 | 128000 | 1.9899 | | 2.1031 | 1.11 | 136000 | 1.9755 | | 2.0977 | 1.17 | 144000 | 1.9855 | | 2.0977 | 1.24 | 152000 | 1.9721 | | 2.0892 | 1.3 | 160000 | 1.9813 | | 2.0892 | 1.37 | 168000 | 1.9828 | | 2.0882 | 1.44 | 176000 | 1.9704 | | 2.0882 | 1.5 | 184000 | 1.9729 | | 2.0884 | 1.57 | 192000 | 1.9721 | | 2.0884 | 1.63 | 200000 | 1.9663 | | 2.0814 | 1.7 | 208000 | 1.9612 | | 2.0814 | 1.76 | 216000 | 1.9717 | | 2.0806 | 1.83 | 224000 | 1.9594 | | 2.0806 | 1.89 | 232000 | 1.9605 | | 2.0838 | 1.96 | 240000 | 1.9556 | | 2.0838 | 2.02 | 248000 | 1.9552 | | 2.0711 | 2.09 | 256000 | 1.9653 | | 2.0711 | 2.15 | 264000 | 1.9581 | | 2.065 | 2.22 | 272000 | 1.9559 | | 2.065 | 2.28 | 280000 | 1.9615 | | 2.0769 | 2.35 | 288000 | 1.9494 | | 2.0769 | 2.41 | 296000 | 1.9487 | | 2.0733 | 2.48 | 304000 | 1.9546 | | 2.0733 | 2.54 | 312000 | 1.9445 | | 2.0675 | 2.61 | 320000 | 1.9535 | | 2.0675 | 2.67 | 328000 | 1.9581 | | 2.0599 | 2.74 | 336000 | 1.9472 | | 2.0599 | 2.8 | 344000 | 1.9545 | | 2.0675 | 2.87 | 352000 | 1.9552 | | 2.0675 | 2.94 | 360000 | 1.9397 | | 2.0711 | 3.0 | 368000 | 1.9475 | | 2.0711 | 3.07 | 376000 | 1.9387 | | 2.0663 | 3.13 | 384000 | 1.9484 | | 2.0663 | 3.2 | 392000 | 1.9424 | | 2.0628 | 3.26 | 400000 | 1.9411 | | 2.0628 | 3.33 | 408000 | 1.9409 | | 2.0651 | 3.39 | 416000 | 1.9447 | | 2.0651 | 3.46 | 424000 | 1.9402 | | 2.0598 | 3.52 | 432000 | 1.9504 | | 2.0598 | 3.59 | 440000 | 1.9414 | | 2.0612 | 3.65 | 448000 | 1.9330 | | 2.0612 | 3.72 | 456000 | 1.9424 | | 2.0653 | 3.78 | 464000 | 1.9310 | | 2.0653 | 3.85 | 472000 | 1.9364 | | 2.0585 | 3.91 | 480000 | 1.9507 | | 2.0585 | 3.98 | 488000 | 1.9320 | | 2.0593 | 4.04 | 496000 | 1.9416 | | 2.0593 | 4.11 | 504000 | 1.9347 | | 2.0671 | 4.17 | 512000 | 1.9391 | | 2.0671 | 4.24 | 520000 | 1.9454 | | 2.0552 | 4.31 | 528000 | 1.9501 | | 2.0552 | 4.37 | 536000 | 1.9355 | | 2.0626 | 4.44 | 544000 | 1.9240 | | 2.0626 | 4.5 | 552000 | 1.9399 | | 2.0592 | 4.57 | 560000 | 1.9360 | | 2.0592 | 4.63 | 568000 | 1.9378 | | 2.0584 | 4.7 | 576000 | 1.9293 | | 2.0584 | 4.76 | 584000 | 1.9431 | | 2.0515 | 4.83 | 592000 | 1.9325 | | 2.0515 | 4.89 | 600000 | 1.9266 | | 2.0545 | 4.96 | 608000 | 1.9215 | | 2.0545 | 5.02 | 616000 | 1.9245 | | 2.0525 | 5.09 | 624000 | 1.9373 | | 2.0525 | 5.15 | 632000 | 1.9341 | | 2.0556 | 5.22 | 640000 | 1.9313 | | 2.0556 | 5.28 | 648000 | 1.9230 | | 2.0567 | 5.35 | 656000 | 1.9300 | | 2.0567 | 5.41 | 664000 | 1.9337 | | 2.0506 | 5.48 | 672000 | 1.9317 | | 2.0506 | 5.54 | 680000 | 1.9275 | | 2.0561 | 5.61 | 688000 | 1.9376 | | 2.0561 | 5.68 | 696000 | 1.9461 | | 2.0496 | 5.74 | 704000 | 1.9239 | | 2.0496 | 5.81 | 712000 | 1.9251 | | 2.045 | 5.87 | 720000 | 1.9309 | | 2.045 | 5.94 | 728000 | 1.9259 | | 2.0512 | 6.0 | 736000 | 1.9237 | | 2.0512 | 6.07 | 744000 | 1.9148 | | 2.0512 | 6.13 | 752000 | 1.9220 | | 2.0512 | 6.2 | 760000 | 1.9397 | | 2.0445 | 6.26 | 768000 | 1.9241 | | 2.0445 | 6.33 | 776000 | 1.9330 | | 2.0481 | 6.39 | 784000 | 1.9124 | | 2.0481 | 6.46 | 792000 | 1.9268 | | 2.048 | 6.52 | 800000 | 1.9211 | | 2.048 | 6.59 | 808000 | 1.9279 | | 2.0555 | 6.65 | 816000 | 1.9169 | | 2.0555 | 6.72 | 824000 | 1.9229 | | 2.052 | 6.78 | 832000 | 1.9253 | | 2.052 | 6.85 | 840000 | 1.9244 | | 2.0475 | 6.91 | 848000 | 1.9192 | | 2.0475 | 6.98 | 856000 | 1.9167 | | 2.0521 | 7.05 | 864000 | 1.9202 | | 2.0521 | 7.11 | 872000 | 1.9240 | | 2.0516 | 7.18 | 880000 | 1.9231 | | 2.0516 | 7.24 | 888000 | 1.9246 | | 2.0526 | 7.31 | 896000 | 1.9174 | | 2.0526 | 7.37 | 904000 | 1.9256 | | 2.044 | 7.44 | 912000 | 1.9234 | | 2.044 | 7.5 | 920000 | 1.9208 | | 2.0493 | 7.57 | 928000 | 1.9233 | | 2.0493 | 7.63 | 936000 | 1.9180 | | 2.0535 | 7.7 | 944000 | 1.9200 | | 2.0535 | 7.76 | 952000 | 1.9152 | | 2.0454 | 7.83 | 960000 | 1.9268 | | 2.0454 | 7.89 | 968000 | 1.9206 | | 2.0428 | 7.96 | 976000 | 1.9170 | | 2.0428 | 8.02 | 984000 | 1.9240 | | 2.052 | 8.09 | 992000 | 1.9306 | | 2.052 | 8.15 | 1000000 | 1.9192 | | 2.0472 | 8.22 | 1008000 | 1.9313 | | 2.0472 | 8.28 | 1016000 | 1.9238 | | 2.0454 | 8.35 | 1024000 | 1.9162 | | 2.0454 | 8.41 | 1032000 | 1.9130 | | 2.0503 | 8.48 | 1040000 | 1.9260 | | 2.0503 | 8.55 | 1048000 | 1.9212 | | 2.0511 | 8.61 | 1056000 | 1.9115 | | 2.0511 | 8.68 | 1064000 | 1.9123 | | 2.049 | 8.74 | 1072000 | 1.9259 | | 2.049 | 8.81 | 1080000 | 1.9321 | | 2.0463 | 8.87 | 1088000 | 1.9148 | | 2.0463 | 8.94 | 1096000 | 1.9145 | | 2.0494 | 9.0 | 1104000 | 1.9097 | | 2.0494 | 9.07 | 1112000 | 1.9135 | | 2.0467 | 9.13 | 1120000 | 1.9164 | | 2.0467 | 9.2 | 1128000 | 1.9224 | | 2.0483 | 9.26 | 1136000 | 1.9135 | | 2.0483 | 9.33 | 1144000 | 1.9199 | | 2.0437 | 9.39 | 1152000 | 1.9213 | | 2.0437 | 9.46 | 1160000 | 1.9161 | | 2.0526 | 9.52 | 1168000 | 1.9148 | | 2.0526 | 9.59 | 1176000 | 1.9183 | | 2.0408 | 9.65 | 1184000 | 1.9079 | | 2.0408 | 9.72 | 1192000 | 1.9186 | | 2.0488 | 9.78 | 1200000 | 1.9141 | | 2.0488 | 9.85 | 1208000 | 1.9079 | | 2.0441 | 9.92 | 1216000 | 1.9251 | | 2.0441 | 9.98 | 1224000 | 1.9255 | | 2.0483 | 10.05 | 1232000 | 1.9108 | | 2.0483 | 10.11 | 1240000 | 1.9045 | | 2.0503 | 10.18 | 1248000 | 1.9170 | | 2.0503 | 10.24 | 1256000 | 1.9025 | | 2.0334 | 10.31 | 1264000 | 1.9199 | | 2.0334 | 10.37 | 1272000 | 1.9187 | | 2.0388 | 10.44 | 1280000 | 1.9030 | | 2.0388 | 10.5 | 1288000 | 1.9231 | | 2.0489 | 10.57 | 1296000 | 1.9084 | | 2.0489 | 10.63 | 1304000 | 1.9184 | | 2.0476 | 10.7 | 1312000 | 1.9160 | | 2.0476 | 10.76 | 1320000 | 1.9276 | | 2.037 | 10.83 | 1328000 | 1.9041 | | 2.037 | 10.89 | 1336000 | 1.9228 | | 2.0447 | 10.96 | 1344000 | 1.9151 | | 2.0447 | 11.02 | 1352000 | 1.9069 | | 2.039 | 11.09 | 1360000 | 1.9275 | | 2.039 | 11.15 | 1368000 | 1.9067 | | 2.0434 | 11.22 | 1376000 | 1.9087 | | 2.0434 | 11.29 | 1384000 | 1.9041 | | 2.0501 | 11.35 | 1392000 | 1.9033 | | 2.0501 | 11.42 | 1400000 | 1.9153 | | 2.0455 | 11.48 | 1408000 | 1.9174 | | 2.0455 | 11.55 | 1416000 | 1.9174 | | 2.0466 | 11.61 | 1424000 | 1.9261 | | 2.0466 | 11.68 | 1432000 | 1.9181 | | 2.0424 | 11.74 | 1440000 | 1.9141 | | 2.0424 | 11.81 | 1448000 | 1.9004 | | 2.0441 | 11.87 | 1456000 | 1.9197 | | 2.0441 | 11.94 | 1464000 | 1.9075 | | 2.04 | 12.0 | 1472000 | 1.9121 | | 2.04 | 12.07 | 1480000 | 1.9211 | | 2.0375 | 12.13 | 1488000 | 1.9111 | | 2.0375 | 12.2 | 1496000 | 1.9188 | | 2.0482 | 12.26 | 1504000 | 1.9099 | | 2.0482 | 12.33 | 1512000 | 1.9161 | | 2.0432 | 12.39 | 1520000 | 1.9198 | | 2.0432 | 12.46 | 1528000 | 1.9154 | | 2.0514 | 12.52 | 1536000 | 1.9059 | | 2.0514 | 12.59 | 1544000 | 1.9204 | | 2.0397 | 12.65 | 1552000 | 1.9055 | | 2.0397 | 12.72 | 1560000 | 1.8962 | | 2.0454 | 12.79 | 1568000 | 1.9040 | | 2.0454 | 12.85 | 1576000 | 1.9168 | | 2.0391 | 12.92 | 1584000 | 1.9037 | | 2.0391 | 12.98 | 1592000 | 1.9186 | | 2.0414 | 13.05 | 1600000 | 1.9122 | | 2.0414 | 13.11 | 1608000 | 1.9116 | | 2.0431 | 13.18 | 1616000 | 1.9057 | | 2.0431 | 13.24 | 1624000 | 1.9115 | | 2.0368 | 13.31 | 1632000 | 1.9120 | | 2.0368 | 13.37 | 1640000 | 1.9114 | | 2.0427 | 13.44 | 1648000 | 1.9128 | | 2.0427 | 13.5 | 1656000 | 1.9201 | | 2.0366 | 13.57 | 1664000 | 1.9053 | | 2.0366 | 13.63 | 1672000 | 1.9077 | | 2.0423 | 13.7 | 1680000 | 1.9155 | | 2.0423 | 13.76 | 1688000 | 1.9025 | | 2.0345 | 13.83 | 1696000 | 1.9117 | | 2.0345 | 13.89 | 1704000 | 1.9146 | | 2.0523 | 13.96 | 1712000 | 1.9094 | | 2.0523 | 14.02 | 1720000 | 1.9028 | | 2.0405 | 14.09 | 1728000 | 1.9034 | | 2.0405 | 14.16 | 1736000 | 1.9033 | | 2.0416 | 14.22 | 1744000 | 1.8958 | | 2.0416 | 14.29 | 1752000 | 1.9072 | | 2.0453 | 14.35 | 1760000 | 1.9067 | | 2.0453 | 14.42 | 1768000 | 1.9113 | | 2.0425 | 14.48 | 1776000 | 1.9104 | | 2.0425 | 14.55 | 1784000 | 1.9110 | | 2.0404 | 14.61 | 1792000 | 1.9037 | | 2.0404 | 14.68 | 1800000 | 1.9003 | | 2.0427 | 14.74 | 1808000 | 1.9116 | | 2.0427 | 14.81 | 1816000 | 1.9106 | | 2.0368 | 14.87 | 1824000 | 1.9095 | | 2.0368 | 14.94 | 1832000 | 1.8980 | | 2.0441 | 15.0 | 1840000 | 1.9186 | | 2.0441 | 15.07 | 1848000 | 1.9040 | | 2.0313 | 15.13 | 1856000 | 1.9186 | | 2.0313 | 15.2 | 1864000 | 1.9016 | | 2.0488 | 15.26 | 1872000 | 1.9048 | | 2.0488 | 15.33 | 1880000 | 1.8995 | | 2.0361 | 15.39 | 1888000 | 1.9120 | | 2.0361 | 15.46 | 1896000 | 1.9079 | | 2.0449 | 15.53 | 1904000 | 1.9110 | | 2.0449 | 15.59 | 1912000 | 1.9091 | | 2.043 | 15.66 | 1920000 | 1.9062 | | 2.043 | 15.72 | 1928000 | 1.9070 | | 2.0414 | 15.79 | 1936000 | 1.9134 | | 2.0414 | 15.85 | 1944000 | 1.9079 | | 2.0419 | 15.92 | 1952000 | 1.9061 | | 2.0419 | 15.98 | 1960000 | 1.9058 | | 2.0384 | 16.05 | 1968000 | 1.9114 | | 2.0384 | 16.11 | 1976000 | 1.9040 | | 2.0391 | 16.18 | 1984000 | 1.9095 | | 2.0391 | 16.24 | 1992000 | 1.9182 | | 2.0405 | 16.31 | 2000000 | 1.9111 | | 2.0405 | 16.37 | 2008000 | 1.9056 | | 2.0404 | 16.44 | 2016000 | 1.9134 | | 2.0404 | 16.5 | 2024000 | 1.9070 | | 2.0414 | 16.57 | 2032000 | 1.9085 | | 2.0414 | 16.63 | 2040000 | 1.9063 | | 2.0483 | 16.7 | 2048000 | 1.9187 | | 2.0483 | 16.76 | 2056000 | 1.9105 | | 2.0452 | 16.83 | 2064000 | 1.9118 | | 2.0452 | 16.89 | 2072000 | 1.9092 | | 2.0401 | 16.96 | 2080000 | 1.9114 | | 2.0401 | 17.03 | 2088000 | 1.9098 | | 2.0353 | 17.09 | 2096000 | 1.9069 | | 2.0353 | 17.16 | 2104000 | 1.9027 | | 2.0468 | 17.22 | 2112000 | 1.9102 | | 2.0468 | 17.29 | 2120000 | 1.9046 | | 2.0448 | 17.35 | 2128000 | 1.9024 | | 2.0448 | 17.42 | 2136000 | 1.9108 | | 2.0435 | 17.48 | 2144000 | 1.9122 | | 2.0435 | 17.55 | 2152000 | 1.9044 | | 2.0421 | 17.61 | 2160000 | 1.9069 | | 2.0421 | 17.68 | 2168000 | 1.9020 | | 2.0366 | 17.74 | 2176000 | 1.9153 | | 2.0366 | 17.81 | 2184000 | 1.9072 | | 2.034 | 17.87 | 2192000 | 1.9182 | | 2.034 | 17.94 | 2200000 | 1.9086 | | 2.0397 | 18.0 | 2208000 | 1.9071 | | 2.0397 | 18.07 | 2216000 | 1.9147 | | 2.0374 | 18.13 | 2224000 | 1.9200 | | 2.0374 | 18.2 | 2232000 | 1.9178 | | 2.0413 | 18.26 | 2240000 | 1.9090 | | 2.0413 | 18.33 | 2248000 | 1.9037 | | 2.047 | 18.4 | 2256000 | 1.9126 | | 2.047 | 18.46 | 2264000 | 1.9117 | | 2.0395 | 18.53 | 2272000 | 1.9110 | | 2.0395 | 18.59 | 2280000 | 1.9158 | | 2.0447 | 18.66 | 2288000 | 1.9017 | | 2.0447 | 18.72 | 2296000 | 1.9072 | | 2.0377 | 18.79 | 2304000 | 1.9136 | | 2.0377 | 18.85 | 2312000 | 1.9084 | | 2.0312 | 18.92 | 2320000 | 1.9092 | | 2.0312 | 18.98 | 2328000 | 1.9103 | | 2.0387 | 19.05 | 2336000 | 1.9023 | | 2.0387 | 19.11 | 2344000 | 1.9035 | | 2.0358 | 19.18 | 2352000 | 1.9131 | | 2.0358 | 19.24 | 2360000 | 1.9066 | | 2.0402 | 19.31 | 2368000 | 1.9083 | | 2.0402 | 19.37 | 2376000 | 1.9068 | | 2.0319 | 19.44 | 2384000 | 1.9012 | | 2.0319 | 19.5 | 2392000 | 1.9273 | | 2.0436 | 19.57 | 2400000 | 1.9059 | ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.14.0