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2020-Q3-25p-filtered

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metadata
license: mit
base_model: cardiffnlp/twitter-roberta-base-2019-90m
tags:
  - generated_from_trainer
model-index:
  - name: 2020-Q3-25p-filtered
    results: []

2020-Q3-25p-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: 2.2870

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 0.02 8000 2.5705
2.7559 0.05 16000 2.4932
2.7559 0.07 24000 2.4516
2.5786 0.09 32000 2.4174
2.5786 0.11 40000 2.4071
2.5316 0.14 48000 2.3903
2.5316 0.16 56000 2.3744
2.5006 0.18 64000 2.3650
2.5006 0.2 72000 2.3600
2.483 0.23 80000 2.3548
2.483 0.25 88000 2.3485
2.4703 0.27 96000 2.3475
2.4703 0.29 104000 2.3384
2.47 0.32 112000 2.3330
2.47 0.34 120000 2.3354
2.4601 0.36 128000 2.3343
2.4601 0.38 136000 2.3282
2.4486 0.41 144000 2.3316
2.4486 0.43 152000 2.3180
2.4536 0.45 160000 2.3257
2.4536 0.47 168000 2.3222
2.4523 0.5 176000 2.3208
2.4523 0.52 184000 2.3218
2.4489 0.54 192000 2.3184
2.4489 0.56 200000 2.3225
2.4448 0.59 208000 2.3185
2.4448 0.61 216000 2.3139
2.4412 0.63 224000 2.3235
2.4412 0.65 232000 2.3148
2.442 0.68 240000 2.3146
2.442 0.7 248000 2.3145
2.4408 0.72 256000 2.3083
2.4408 0.74 264000 2.3068
2.4336 0.77 272000 2.3104
2.4336 0.79 280000 2.3147
2.4394 0.81 288000 2.3105
2.4394 0.83 296000 2.3135
2.4363 0.86 304000 2.3057
2.4363 0.88 312000 2.3050
2.4403 0.9 320000 2.3066
2.4403 0.92 328000 2.3076
2.4409 0.95 336000 2.3026
2.4409 0.97 344000 2.3045
2.4434 0.99 352000 2.3047
2.4434 1.01 360000 2.3080
2.4372 1.04 368000 2.3143
2.4372 1.06 376000 2.3049
2.4329 1.08 384000 2.3066
2.4329 1.1 392000 2.3050
2.437 1.13 400000 2.3012
2.437 1.15 408000 2.3033
2.4378 1.17 416000 2.3064
2.4378 1.19 424000 2.2984
2.4386 1.22 432000 2.3057
2.4386 1.24 440000 2.3035
2.4411 1.26 448000 2.2969
2.4411 1.28 456000 2.2930
2.4466 1.31 464000 2.3005
2.4466 1.33 472000 2.2975
2.4451 1.35 480000 2.3042
2.4451 1.37 488000 2.3061
2.4399 1.4 496000 2.2987
2.4399 1.42 504000 2.2967
2.4397 1.44 512000 2.3010
2.4397 1.47 520000 2.3019
2.4483 1.49 528000 2.3009
2.4483 1.51 536000 2.3048
2.4436 1.53 544000 2.3029
2.4436 1.56 552000 2.3026
2.4407 1.58 560000 2.3027
2.4407 1.6 568000 2.3061
2.4364 1.62 576000 2.2972
2.4364 1.65 584000 2.2967
2.4406 1.67 592000 2.2965
2.4406 1.69 600000 2.2966
2.4393 1.71 608000 2.2982
2.4393 1.74 616000 2.2993
2.4352 1.76 624000 2.2916
2.4352 1.78 632000 2.2931
2.4366 1.8 640000 2.3016
2.4366 1.83 648000 2.2984
2.4361 1.85 656000 2.2877
2.4361 1.87 664000 2.2983
2.437 1.89 672000 2.3033
2.437 1.92 680000 2.2928
2.4488 1.94 688000 2.2953
2.4488 1.96 696000 2.2945
2.4459 1.98 704000 2.2961
2.4459 2.01 712000 2.2899
2.4334 2.03 720000 2.2964
2.4334 2.05 728000 2.2896
2.4343 2.07 736000 2.2954
2.4343 2.1 744000 2.3004
2.4345 2.12 752000 2.2892
2.4345 2.14 760000 2.2996
2.4386 2.16 768000 2.2886
2.4386 2.19 776000 2.2974
2.434 2.21 784000 2.2882
2.434 2.23 792000 2.2965
2.4379 2.25 800000 2.2899
2.4379 2.28 808000 2.2938
2.4356 2.3 816000 2.2997
2.4356 2.32 824000 2.2942
2.4399 2.34 832000 2.2916
2.4399 2.37 840000 2.2934
2.437 2.39 848000 2.2978
2.437 2.41 856000 2.2834
2.4311 2.43 864000 2.2872
2.4311 2.46 872000 2.2928
2.4453 2.48 880000 2.2888
2.4453 2.5 888000 2.2933
2.4434 2.52 896000 2.2911
2.4434 2.55 904000 2.2929
2.443 2.57 912000 2.2926
2.443 2.59 920000 2.2908
2.4361 2.61 928000 2.2914
2.4361 2.64 936000 2.2878
2.44 2.66 944000 2.2872
2.44 2.68 952000 2.2857
2.4447 2.7 960000 2.2932
2.4447 2.73 968000 2.2918
2.4362 2.75 976000 2.2875
2.4362 2.77 984000 2.2900
2.4457 2.8 992000 2.2913
2.4457 2.82 1000000 2.2871
2.4474 2.84 1008000 2.2875
2.4474 2.86 1016000 2.2902
2.444 2.89 1024000 2.2878
2.444 2.91 1032000 2.2856
2.4316 2.93 1040000 2.2908
2.4316 2.95 1048000 2.2889
2.4388 2.98 1056000 2.2922
2.4388 3.0 1064000 2.2867
2.442 3.02 1072000 2.2912
2.442 3.04 1080000 2.2891
2.4388 3.07 1088000 2.2855
2.4388 3.09 1096000 2.2949
2.4296 3.11 1104000 2.2853
2.4296 3.13 1112000 2.2854
2.4411 3.16 1120000 2.2902
2.4411 3.18 1128000 2.2902
2.4354 3.2 1136000 2.2873
2.4354 3.22 1144000 2.2931
2.4436 3.25 1152000 2.2906
2.4436 3.27 1160000 2.2945
2.4372 3.29 1168000 2.2899
2.4372 3.31 1176000 2.2869
2.4327 3.34 1184000 2.2891
2.4327 3.36 1192000 2.2933
2.4387 3.38 1200000 2.2849
2.4387 3.4 1208000 2.2934
2.4433 3.43 1216000 2.2876
2.4433 3.45 1224000 2.2860
2.4396 3.47 1232000 2.2898
2.4396 3.49 1240000 2.2830
2.4332 3.52 1248000 2.2855
2.4332 3.54 1256000 2.2925
2.4332 3.56 1264000 2.2832
2.4332 3.58 1272000 2.2851
2.4307 3.61 1280000 2.2912
2.4307 3.63 1288000 2.2924
2.4432 3.65 1296000 2.2916
2.4432 3.67 1304000 2.2892
2.4319 3.7 1312000 2.2908
2.4319 3.72 1320000 2.2898
2.4394 3.74 1328000 2.2860
2.4394 3.76 1336000 2.2879
2.4462 3.79 1344000 2.2865
2.4462 3.81 1352000 2.2844
2.4373 3.83 1360000 2.2933
2.4373 3.85 1368000 2.2877
2.4436 3.88 1376000 2.2937
2.4436 3.9 1384000 2.2902
2.4387 3.92 1392000 2.2870
2.4387 3.94 1400000 2.2823
2.4384 3.97 1408000 2.2899
2.4384 3.99 1416000 2.2865
2.4389 4.01 1424000 2.2856
2.4389 4.03 1432000 2.2911
2.4408 4.06 1440000 2.2906
2.4408 4.08 1448000 2.2860
2.4424 4.1 1456000 2.2816
2.4424 4.12 1464000 2.2850
2.4446 4.15 1472000 2.2936
2.4446 4.17 1480000 2.2829
2.4419 4.19 1488000 2.2871
2.4419 4.22 1496000 2.2892
2.4327 4.24 1504000 2.2822
2.4327 4.26 1512000 2.2900
2.4346 4.28 1520000 2.2906
2.4346 4.31 1528000 2.2837
2.4342 4.33 1536000 2.2846
2.4342 4.35 1544000 2.2863
2.4381 4.37 1552000 2.2940
2.4381 4.4 1560000 2.2900
2.4445 4.42 1568000 2.2887
2.4445 4.44 1576000 2.2901
2.4306 4.46 1584000 2.2832
2.4306 4.49 1592000 2.2862
2.4348 4.51 1600000 2.2877
2.4348 4.53 1608000 2.2834
2.4446 4.55 1616000 2.2892
2.4446 4.58 1624000 2.2800
2.444 4.6 1632000 2.2891
2.444 4.62 1640000 2.2839
2.4335 4.64 1648000 2.2787
2.4335 4.67 1656000 2.2856
2.4369 4.69 1664000 2.2889
2.4369 4.71 1672000 2.2900
2.4446 4.73 1680000 2.2891
2.4446 4.76 1688000 2.2835
2.4334 4.78 1696000 2.2841
2.4334 4.8 1704000 2.2895
2.4426 4.82 1712000 2.2832
2.4426 4.85 1720000 2.2870
2.4434 4.87 1728000 2.2819
2.4434 4.89 1736000 2.2896
2.4382 4.91 1744000 2.2869
2.4382 4.94 1752000 2.2844
2.4405 4.96 1760000 2.2820
2.4405 4.98 1768000 2.2922
2.4507 5.0 1776000 2.2808
2.4507 5.03 1784000 2.2868
2.4437 5.05 1792000 2.2815
2.4437 5.07 1800000 2.2889
2.4373 5.09 1808000 2.2797
2.4373 5.12 1816000 2.2882
2.4368 5.14 1824000 2.2879
2.4368 5.16 1832000 2.2829
2.4398 5.18 1840000 2.2867
2.4398 5.21 1848000 2.2829
2.4469 5.23 1856000 2.2846
2.4469 5.25 1864000 2.2839
2.4457 5.27 1872000 2.2880
2.4457 5.3 1880000 2.2849
2.4444 5.32 1888000 2.2838
2.4444 5.34 1896000 2.2800
2.437 5.36 1904000 2.2915
2.437 5.39 1912000 2.2813
2.4415 5.41 1920000 2.2893
2.4415 5.43 1928000 2.2848
2.4472 5.45 1936000 2.2920
2.4472 5.48 1944000 2.2759
2.4418 5.5 1952000 2.2837
2.4418 5.52 1960000 2.2860
2.4406 5.54 1968000 2.2825
2.4406 5.57 1976000 2.2794
2.4359 5.59 1984000 2.2773
2.4359 5.61 1992000 2.2876
2.4416 5.64 2000000 2.2793
2.4416 5.66 2008000 2.2814
2.4327 5.68 2016000 2.2865
2.4327 5.7 2024000 2.2903
2.4395 5.73 2032000 2.2850
2.4395 5.75 2040000 2.2835
2.4379 5.77 2048000 2.2837
2.4379 5.79 2056000 2.2833
2.4471 5.82 2064000 2.2857
2.4471 5.84 2072000 2.2863
2.4443 5.86 2080000 2.2882
2.4443 5.88 2088000 2.2849
2.4406 5.91 2096000 2.2885
2.4406 5.93 2104000 2.2852
2.4502 5.95 2112000 2.2898
2.4502 5.97 2120000 2.2924
2.4356 6.0 2128000 2.2886
2.4356 6.02 2136000 2.2883
2.4431 6.04 2144000 2.2935
2.4431 6.06 2152000 2.2918
2.4379 6.09 2160000 2.2824
2.4379 6.11 2168000 2.2850
2.4504 6.13 2176000 2.2842
2.4504 6.15 2184000 2.2891
2.4352 6.18 2192000 2.2834
2.4352 6.2 2200000 2.2877
2.4385 6.22 2208000 2.2836
2.4385 6.24 2216000 2.2923
2.4401 6.27 2224000 2.2884
2.4401 6.29 2232000 2.2876
2.4396 6.31 2240000 2.2955
2.4396 6.33 2248000 2.2843
2.4384 6.36 2256000 2.2884
2.4384 6.38 2264000 2.2903
2.4365 6.4 2272000 2.2850
2.4365 6.42 2280000 2.2877
2.4361 6.45 2288000 2.2887
2.4361 6.47 2296000 2.2872
2.4409 6.49 2304000 2.2851
2.4409 6.51 2312000 2.2847
2.4423 6.54 2320000 2.2845
2.4423 6.56 2328000 2.2849
2.4409 6.58 2336000 2.2865
2.4409 6.6 2344000 2.2856
2.4468 6.63 2352000 2.2842
2.4468 6.65 2360000 2.2870
2.4461 6.67 2368000 2.2858
2.4461 6.69 2376000 2.2852
2.4469 6.72 2384000 2.2871
2.4469 6.74 2392000 2.2895
2.4413 6.76 2400000 2.2823

Framework versions

  • Transformers 4.35.0.dev0
  • Pytorch 2.0.1+cu117
  • Datasets 2.14.5
  • Tokenizers 0.14.0