--- license: mit base_model: cardiffnlp/twitter-roberta-base-2019-90m tags: - generated_from_trainer model-index: - name: 2020-Q2-90p-filtered results: [] --- # 2020-Q2-90p-filtered 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: 3.5439 ## 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 | 0.17 | 8000 | 4.0640 | | 4.2654 | 0.34 | 16000 | 3.9414 | | 4.2654 | 0.51 | 24000 | 3.8956 | | 4.0459 | 0.67 | 32000 | 3.8527 | | 4.0459 | 0.84 | 40000 | 3.8232 | | 3.9781 | 1.01 | 48000 | 3.7806 | | 3.9781 | 1.18 | 56000 | 3.7861 | | 3.9323 | 1.35 | 64000 | 3.7930 | | 3.9323 | 1.52 | 72000 | 3.7814 | | 3.9224 | 1.68 | 80000 | 3.7815 | | 3.9224 | 1.85 | 88000 | 3.7403 | | 3.8924 | 2.02 | 96000 | 3.7468 | | 3.8924 | 2.19 | 104000 | 3.7400 | | 3.879 | 2.36 | 112000 | 3.7283 | | 3.879 | 2.53 | 120000 | 3.7381 | | 3.8806 | 2.69 | 128000 | 3.7073 | | 3.8806 | 2.86 | 136000 | 3.7083 | | 3.8659 | 3.03 | 144000 | 3.6992 | | 3.8659 | 3.2 | 152000 | 3.6956 | | 3.8634 | 3.37 | 160000 | 3.6745 | | 3.8634 | 3.54 | 168000 | 3.7017 | | 3.8632 | 3.71 | 176000 | 3.6960 | | 3.8632 | 3.87 | 184000 | 3.7202 | | 3.8416 | 4.04 | 192000 | 3.7109 | | 3.8416 | 4.21 | 200000 | 3.6942 | | 3.8368 | 4.38 | 208000 | 3.6944 | | 3.8368 | 4.55 | 216000 | 3.6751 | | 3.8359 | 4.72 | 224000 | 3.6815 | | 3.8359 | 4.88 | 232000 | 3.6915 | | 3.8411 | 5.05 | 240000 | 3.6796 | | 3.8411 | 5.22 | 248000 | 3.6847 | | 3.8359 | 5.39 | 256000 | 3.6988 | | 3.8359 | 5.56 | 264000 | 3.6799 | | 3.8268 | 5.73 | 272000 | 3.6810 | | 3.8268 | 5.89 | 280000 | 3.6639 | | 3.8172 | 6.06 | 288000 | 3.6663 | | 3.8172 | 6.23 | 296000 | 3.6838 | | 3.8263 | 6.4 | 304000 | 3.6756 | | 3.8263 | 6.57 | 312000 | 3.6507 | | 3.8215 | 6.74 | 320000 | 3.6409 | | 3.8215 | 6.91 | 328000 | 3.6790 | | 3.8189 | 7.07 | 336000 | 3.6679 | | 3.8189 | 7.24 | 344000 | 3.6443 | | 3.8155 | 7.41 | 352000 | 3.6588 | | 3.8155 | 7.58 | 360000 | 3.6448 | | 3.8075 | 7.75 | 368000 | 3.6520 | | 3.8075 | 7.92 | 376000 | 3.6541 | | 3.8064 | 8.08 | 384000 | 3.6569 | | 3.8064 | 8.25 | 392000 | 3.6586 | | 3.8092 | 8.42 | 400000 | 3.6701 | | 3.8092 | 8.59 | 408000 | 3.6544 | | 3.8032 | 8.76 | 416000 | 3.6668 | | 3.8032 | 8.93 | 424000 | 3.6631 | | 3.8062 | 9.09 | 432000 | 3.6481 | | 3.8062 | 9.26 | 440000 | 3.6392 | | 3.7987 | 9.43 | 448000 | 3.6482 | | 3.7987 | 9.6 | 456000 | 3.6357 | | 3.7954 | 9.77 | 464000 | 3.6333 | | 3.7954 | 9.94 | 472000 | 3.6653 | | 3.7938 | 10.11 | 480000 | 3.6267 | | 3.7938 | 10.27 | 488000 | 3.6490 | | 3.7901 | 10.44 | 496000 | 3.6417 | | 3.7901 | 10.61 | 504000 | 3.6263 | | 3.7935 | 10.78 | 512000 | 3.6523 | | 3.7935 | 10.95 | 520000 | 3.6444 | | 3.7951 | 11.12 | 528000 | 3.6226 | | 3.7951 | 11.28 | 536000 | 3.6347 | | 3.7861 | 11.45 | 544000 | 3.6372 | | 3.7861 | 11.62 | 552000 | 3.6163 | | 3.7846 | 11.79 | 560000 | 3.6299 | | 3.7846 | 11.96 | 568000 | 3.6330 | | 3.7778 | 12.13 | 576000 | 3.6371 | | 3.7778 | 12.29 | 584000 | 3.6343 | | 3.777 | 12.46 | 592000 | 3.6242 | | 3.777 | 12.63 | 600000 | 3.6119 | | 3.778 | 12.8 | 608000 | 3.6167 | | 3.778 | 12.97 | 616000 | 3.6191 | | 3.7795 | 13.14 | 624000 | 3.6225 | | 3.7795 | 13.3 | 632000 | 3.6056 | | 3.7766 | 13.47 | 640000 | 3.6135 | | 3.7766 | 13.64 | 648000 | 3.6169 | | 3.7729 | 13.81 | 656000 | 3.6035 | | 3.7729 | 13.98 | 664000 | 3.6109 | | 3.7846 | 14.15 | 672000 | 3.6180 | | 3.7846 | 14.32 | 680000 | 3.6171 | | 3.7726 | 14.48 | 688000 | 3.6182 | | 3.7726 | 14.65 | 696000 | 3.6086 | | 3.7717 | 14.82 | 704000 | 3.5852 | | 3.7717 | 14.99 | 712000 | 3.5883 | | 3.7713 | 15.16 | 720000 | 3.6056 | | 3.7713 | 15.33 | 728000 | 3.6004 | | 3.7745 | 15.49 | 736000 | 3.6059 | | 3.7745 | 15.66 | 744000 | 3.6156 | | 3.7557 | 15.83 | 752000 | 3.6029 | | 3.7557 | 16.0 | 760000 | 3.6099 | | 3.7628 | 16.17 | 768000 | 3.6016 | | 3.7628 | 16.34 | 776000 | 3.6008 | | 3.7717 | 16.5 | 784000 | 3.5972 | | 3.7717 | 16.67 | 792000 | 3.5838 | | 3.7616 | 16.84 | 800000 | 3.5868 | | 3.7616 | 17.01 | 808000 | 3.5834 | | 3.7608 | 17.18 | 816000 | 3.6066 | | 3.7608 | 17.35 | 824000 | 3.5911 | | 3.7625 | 17.52 | 832000 | 3.5997 | | 3.7625 | 17.68 | 840000 | 3.5855 | | 3.7634 | 17.85 | 848000 | 3.5861 | | 3.7634 | 18.02 | 856000 | 3.6021 | | 3.75 | 18.19 | 864000 | 3.5966 | | 3.75 | 18.36 | 872000 | 3.5761 | | 3.7492 | 18.53 | 880000 | 3.5757 | | 3.7492 | 18.69 | 888000 | 3.6123 | | 3.7522 | 18.86 | 896000 | 3.5841 | | 3.7522 | 19.03 | 904000 | 3.5831 | | 3.7482 | 19.2 | 912000 | 3.5860 | | 3.7482 | 19.37 | 920000 | 3.5804 | | 3.75 | 19.54 | 928000 | 3.5730 | | 3.75 | 19.7 | 936000 | 3.5955 | | 3.755 | 19.87 | 944000 | 3.5868 | | 3.755 | 20.04 | 952000 | 3.5992 | | 3.7549 | 20.21 | 960000 | 3.5657 | | 3.7549 | 20.38 | 968000 | 3.5780 | | 3.743 | 20.55 | 976000 | 3.5828 | | 3.743 | 20.72 | 984000 | 3.5676 | | 3.75 | 20.88 | 992000 | 3.5724 | | 3.75 | 21.05 | 1000000 | 3.5850 | | 3.7483 | 21.22 | 1008000 | 3.5873 | | 3.7483 | 21.39 | 1016000 | 3.5799 | | 3.7523 | 21.56 | 1024000 | 3.5974 | | 3.7523 | 21.73 | 1032000 | 3.5790 | | 3.7458 | 21.89 | 1040000 | 3.5884 | | 3.7458 | 22.06 | 1048000 | 3.5904 | | 3.7498 | 22.23 | 1056000 | 3.5851 | | 3.7498 | 22.4 | 1064000 | 3.5776 | | 3.7496 | 22.57 | 1072000 | 3.5685 | | 3.7496 | 22.74 | 1080000 | 3.5731 | | 3.7395 | 22.9 | 1088000 | 3.5858 | | 3.7395 | 23.07 | 1096000 | 3.5931 | | 3.7466 | 23.24 | 1104000 | 3.5614 | | 3.7466 | 23.41 | 1112000 | 3.5456 | | 3.7503 | 23.58 | 1120000 | 3.5895 | | 3.7503 | 23.75 | 1128000 | 3.5608 | | 3.7484 | 23.92 | 1136000 | 3.5696 | | 3.7484 | 24.08 | 1144000 | 3.5653 | | 3.7435 | 24.25 | 1152000 | 3.5721 | | 3.7435 | 24.42 | 1160000 | 3.5510 | | 3.7348 | 24.59 | 1168000 | 3.5631 | | 3.7348 | 24.76 | 1176000 | 3.5727 | | 3.7341 | 24.93 | 1184000 | 3.5835 | | 3.7341 | 25.09 | 1192000 | 3.5766 | | 3.7435 | 25.26 | 1200000 | 3.5606 | | 3.7435 | 25.43 | 1208000 | 3.5497 | | 3.732 | 25.6 | 1216000 | 3.5433 | | 3.732 | 25.77 | 1224000 | 3.5420 | | 3.7343 | 25.94 | 1232000 | 3.5987 | | 3.7343 | 26.1 | 1240000 | 3.5956 | | 3.7336 | 26.27 | 1248000 | 3.5673 | | 3.7336 | 26.44 | 1256000 | 3.5643 | | 3.7444 | 26.61 | 1264000 | 3.5848 | | 3.7444 | 26.78 | 1272000 | 3.5693 | | 3.7395 | 26.95 | 1280000 | 3.5745 | | 3.7395 | 27.12 | 1288000 | 3.5758 | | 3.7389 | 27.28 | 1296000 | 3.5685 | | 3.7389 | 27.45 | 1304000 | 3.5712 | | 3.7416 | 27.62 | 1312000 | 3.5693 | | 3.7416 | 27.79 | 1320000 | 3.5740 | | 3.7305 | 27.96 | 1328000 | 3.5803 | | 3.7305 | 28.13 | 1336000 | 3.5682 | | 3.7268 | 28.29 | 1344000 | 3.5928 | | 3.7268 | 28.46 | 1352000 | 3.5608 | | 3.7363 | 28.63 | 1360000 | 3.5587 | | 3.7363 | 28.8 | 1368000 | 3.5603 | | 3.7325 | 28.97 | 1376000 | 3.5711 | | 3.7325 | 29.14 | 1384000 | 3.5828 | | 3.7337 | 29.3 | 1392000 | 3.5790 | | 3.7337 | 29.47 | 1400000 | 3.5795 | | 3.7367 | 29.64 | 1408000 | 3.5528 | | 3.7367 | 29.81 | 1416000 | 3.5766 | | 3.7313 | 29.98 | 1424000 | 3.5610 | | 3.7313 | 30.15 | 1432000 | 3.5834 | | 3.7277 | 30.32 | 1440000 | 3.5546 | | 3.7277 | 30.48 | 1448000 | 3.5534 | | 3.7296 | 30.65 | 1456000 | 3.5646 | | 3.7296 | 30.82 | 1464000 | 3.5436 | | 3.7411 | 30.99 | 1472000 | 3.5778 | | 3.7411 | 31.16 | 1480000 | 3.5541 | | 3.7233 | 31.33 | 1488000 | 3.5720 | | 3.7233 | 31.49 | 1496000 | 3.5567 | | 3.7291 | 31.66 | 1504000 | 3.5477 | | 3.7291 | 31.83 | 1512000 | 3.5557 | | 3.7265 | 32.0 | 1520000 | 3.5643 | | 3.7265 | 32.17 | 1528000 | 3.5739 | | 3.7352 | 32.34 | 1536000 | 3.5628 | | 3.7352 | 32.5 | 1544000 | 3.5542 | | 3.7353 | 32.67 | 1552000 | 3.5496 | | 3.7353 | 32.84 | 1560000 | 3.5737 | | 3.7243 | 33.01 | 1568000 | 3.5788 | | 3.7243 | 33.18 | 1576000 | 3.5631 | | 3.7192 | 33.35 | 1584000 | 3.5438 | | 3.7192 | 33.52 | 1592000 | 3.5554 | | 3.7266 | 33.68 | 1600000 | 3.5748 | | 3.7266 | 33.85 | 1608000 | 3.5620 | | 3.73 | 34.02 | 1616000 | 3.5464 | | 3.73 | 34.19 | 1624000 | 3.5670 | | 3.7264 | 34.36 | 1632000 | 3.5626 | | 3.7264 | 34.53 | 1640000 | 3.5640 | | 3.7317 | 34.69 | 1648000 | 3.5650 | | 3.7317 | 34.86 | 1656000 | 3.5458 | | 3.7332 | 35.03 | 1664000 | 3.5567 | | 3.7332 | 35.2 | 1672000 | 3.5610 | | 3.7248 | 35.37 | 1680000 | 3.5650 | | 3.7248 | 35.54 | 1688000 | 3.5580 | | 3.7232 | 35.7 | 1696000 | 3.5829 | | 3.7232 | 35.87 | 1704000 | 3.5532 | | 3.729 | 36.04 | 1712000 | 3.5723 | | 3.729 | 36.21 | 1720000 | 3.5454 | | 3.7273 | 36.38 | 1728000 | 3.5623 | | 3.7273 | 36.55 | 1736000 | 3.5462 | | 3.7261 | 36.72 | 1744000 | 3.5743 | | 3.7261 | 36.88 | 1752000 | 3.5638 | | 3.7208 | 37.05 | 1760000 | 3.5519 | | 3.7208 | 37.22 | 1768000 | 3.5584 | | 3.7183 | 37.39 | 1776000 | 3.5308 | | 3.7183 | 37.56 | 1784000 | 3.5549 | | 3.7193 | 37.73 | 1792000 | 3.5409 | | 3.7193 | 37.89 | 1800000 | 3.5396 | | 3.7271 | 38.06 | 1808000 | 3.5536 | | 3.7271 | 38.23 | 1816000 | 3.5452 | | 3.7284 | 38.4 | 1824000 | 3.5582 | | 3.7284 | 38.57 | 1832000 | 3.5668 | | 3.714 | 38.74 | 1840000 | 3.5673 | | 3.714 | 38.9 | 1848000 | 3.5477 | | 3.7105 | 39.07 | 1856000 | 3.5662 | | 3.7105 | 39.24 | 1864000 | 3.5498 | | 3.7189 | 39.41 | 1872000 | 3.5493 | | 3.7189 | 39.58 | 1880000 | 3.5676 | | 3.7203 | 39.75 | 1888000 | 3.5640 | | 3.7203 | 39.91 | 1896000 | 3.5747 | | 3.7271 | 40.08 | 1904000 | 3.5592 | | 3.7271 | 40.25 | 1912000 | 3.5515 | | 3.7237 | 40.42 | 1920000 | 3.5704 | | 3.7237 | 40.59 | 1928000 | 3.5642 | | 3.723 | 40.76 | 1936000 | 3.5300 | | 3.723 | 40.93 | 1944000 | 3.5482 | | 3.7224 | 41.09 | 1952000 | 3.5586 | | 3.7224 | 41.26 | 1960000 | 3.5463 | | 3.715 | 41.43 | 1968000 | 3.5323 | | 3.715 | 41.6 | 1976000 | 3.5426 | | 3.7209 | 41.77 | 1984000 | 3.5513 | | 3.7209 | 41.94 | 1992000 | 3.5614 | | 3.7183 | 42.1 | 2000000 | 3.5678 | | 3.7183 | 42.27 | 2008000 | 3.5304 | | 3.7161 | 42.44 | 2016000 | 3.5631 | | 3.7161 | 42.61 | 2024000 | 3.5589 | | 3.7215 | 42.78 | 2032000 | 3.5639 | | 3.7215 | 42.95 | 2040000 | 3.5376 | | 3.7205 | 43.11 | 2048000 | 3.5478 | | 3.7205 | 43.28 | 2056000 | 3.5511 | | 3.7178 | 43.45 | 2064000 | 3.5285 | | 3.7178 | 43.62 | 2072000 | 3.5428 | | 3.7232 | 43.79 | 2080000 | 3.5347 | | 3.7232 | 43.96 | 2088000 | 3.5501 | | 3.7167 | 44.13 | 2096000 | 3.5422 | | 3.7167 | 44.29 | 2104000 | 3.5487 | | 3.7253 | 44.46 | 2112000 | 3.5540 | | 3.7253 | 44.63 | 2120000 | 3.5432 | | 3.7139 | 44.8 | 2128000 | 3.5502 | | 3.7139 | 44.97 | 2136000 | 3.5450 | | 3.7194 | 45.14 | 2144000 | 3.5564 | | 3.7194 | 45.3 | 2152000 | 3.5441 | | 3.7167 | 45.47 | 2160000 | 3.5549 | | 3.7167 | 45.64 | 2168000 | 3.5429 | | 3.7202 | 45.81 | 2176000 | 3.5613 | | 3.7202 | 45.98 | 2184000 | 3.5469 | | 3.7193 | 46.15 | 2192000 | 3.5467 | | 3.7193 | 46.31 | 2200000 | 3.5493 | | 3.717 | 46.48 | 2208000 | 3.5652 | | 3.717 | 46.65 | 2216000 | 3.5669 | | 3.7164 | 46.82 | 2224000 | 3.5755 | | 3.7164 | 46.99 | 2232000 | 3.5580 | | 3.715 | 47.16 | 2240000 | 3.5403 | | 3.715 | 47.33 | 2248000 | 3.5521 | | 3.7091 | 47.49 | 2256000 | 3.5604 | | 3.7091 | 47.66 | 2264000 | 3.5401 | | 3.7199 | 47.83 | 2272000 | 3.5408 | | 3.7199 | 48.0 | 2280000 | 3.5509 | | 3.7238 | 48.17 | 2288000 | 3.5348 | | 3.7238 | 48.34 | 2296000 | 3.5530 | | 3.7193 | 48.5 | 2304000 | 3.5447 | | 3.7193 | 48.67 | 2312000 | 3.5453 | | 3.7195 | 48.84 | 2320000 | 3.5487 | | 3.7195 | 49.01 | 2328000 | 3.5357 | | 3.7187 | 49.18 | 2336000 | 3.5404 | | 3.7187 | 49.35 | 2344000 | 3.5247 | | 3.7157 | 49.51 | 2352000 | 3.5557 | | 3.7157 | 49.68 | 2360000 | 3.5532 | | 3.7144 | 49.85 | 2368000 | 3.5453 | | 3.7144 | 50.02 | 2376000 | 3.5421 | | 3.715 | 50.19 | 2384000 | 3.5183 | | 3.715 | 50.36 | 2392000 | 3.5473 | | 3.7208 | 50.53 | 2400000 | 3.5386 | ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.14.0