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@@ -4,18 +4,18 @@ base_model: cardiffnlp/twitter-roberta-base-2019-90m
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  tags:
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  - generated_from_trainer
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  model-index:
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- - name: 2020-Q3-25p-filtered
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  results: []
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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  should probably proofread and complete it, then remove this comment. -->
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- # 2020-Q3-25p-filtered
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  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.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.2870
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  ## Model description
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@@ -46,306 +46,306 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss |
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  |:-------------:|:-----:|:-------:|:---------------:|
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- | No log | 0.02 | 8000 | 2.5705 |
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- | 2.7559 | 0.05 | 16000 | 2.4932 |
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- | 2.7559 | 0.07 | 24000 | 2.4516 |
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- | 2.5786 | 0.09 | 32000 | 2.4174 |
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- | 2.5786 | 0.11 | 40000 | 2.4071 |
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- | 2.5316 | 0.14 | 48000 | 2.3903 |
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- | 2.5316 | 0.16 | 56000 | 2.3744 |
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- | 2.5006 | 0.18 | 64000 | 2.3650 |
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- | 2.5006 | 0.2 | 72000 | 2.3600 |
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- | 2.483 | 0.23 | 80000 | 2.3548 |
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- | 2.483 | 0.25 | 88000 | 2.3485 |
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- | 2.4703 | 0.27 | 96000 | 2.3475 |
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- | 2.4703 | 0.29 | 104000 | 2.3384 |
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- | 2.47 | 0.32 | 112000 | 2.3330 |
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- | 2.47 | 0.34 | 120000 | 2.3354 |
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- | 2.4601 | 0.36 | 128000 | 2.3343 |
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- | 2.4601 | 0.38 | 136000 | 2.3282 |
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- | 2.4486 | 0.41 | 144000 | 2.3316 |
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- | 2.4486 | 0.43 | 152000 | 2.3180 |
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- | 2.4536 | 0.45 | 160000 | 2.3257 |
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- | 2.4536 | 0.47 | 168000 | 2.3222 |
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- | 2.4523 | 0.5 | 176000 | 2.3208 |
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- | 2.4523 | 0.52 | 184000 | 2.3218 |
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- | 2.4489 | 0.54 | 192000 | 2.3184 |
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- | 2.4489 | 0.56 | 200000 | 2.3225 |
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- | 2.4448 | 0.59 | 208000 | 2.3185 |
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- | 2.4448 | 0.61 | 216000 | 2.3139 |
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- | 2.4412 | 0.63 | 224000 | 2.3235 |
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- | 2.4412 | 0.65 | 232000 | 2.3148 |
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- | 2.442 | 0.68 | 240000 | 2.3146 |
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- | 2.442 | 0.7 | 248000 | 2.3145 |
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- | 2.4408 | 0.72 | 256000 | 2.3083 |
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- | 2.4408 | 0.74 | 264000 | 2.3068 |
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- | 2.4336 | 0.77 | 272000 | 2.3104 |
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- | 2.4336 | 0.79 | 280000 | 2.3147 |
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- | 2.4394 | 0.81 | 288000 | 2.3105 |
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- | 2.4394 | 0.83 | 296000 | 2.3135 |
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- | 2.4363 | 0.86 | 304000 | 2.3057 |
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- | 2.4363 | 0.88 | 312000 | 2.3050 |
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- | 2.4403 | 0.9 | 320000 | 2.3066 |
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- | 2.4403 | 0.92 | 328000 | 2.3076 |
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- | 2.4409 | 0.95 | 336000 | 2.3026 |
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- | 2.4409 | 0.97 | 344000 | 2.3045 |
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- | 2.4434 | 0.99 | 352000 | 2.3047 |
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- | 2.4434 | 1.01 | 360000 | 2.3080 |
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- | 2.4372 | 1.04 | 368000 | 2.3143 |
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- | 2.4372 | 1.06 | 376000 | 2.3049 |
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- | 2.4329 | 1.08 | 384000 | 2.3066 |
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- | 2.4329 | 1.1 | 392000 | 2.3050 |
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- | 2.437 | 1.13 | 400000 | 2.3012 |
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- | 2.437 | 1.15 | 408000 | 2.3033 |
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- | 2.4378 | 1.17 | 416000 | 2.3064 |
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- | 2.4378 | 1.19 | 424000 | 2.2984 |
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- | 2.4386 | 1.22 | 432000 | 2.3057 |
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- | 2.4386 | 1.24 | 440000 | 2.3035 |
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- | 2.4411 | 1.26 | 448000 | 2.2969 |
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- | 2.4411 | 1.28 | 456000 | 2.2930 |
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- | 2.4466 | 1.31 | 464000 | 2.3005 |
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- | 2.4466 | 1.33 | 472000 | 2.2975 |
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- | 2.4451 | 1.35 | 480000 | 2.3042 |
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- | 2.4451 | 1.37 | 488000 | 2.3061 |
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- | 2.4399 | 1.4 | 496000 | 2.2987 |
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- | 2.4399 | 1.42 | 504000 | 2.2967 |
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- | 2.4397 | 1.44 | 512000 | 2.3010 |
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- | 2.4397 | 1.47 | 520000 | 2.3019 |
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- | 2.4483 | 1.49 | 528000 | 2.3009 |
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- | 2.4483 | 1.51 | 536000 | 2.3048 |
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- | 2.4436 | 1.53 | 544000 | 2.3029 |
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- | 2.4436 | 1.56 | 552000 | 2.3026 |
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- | 2.4407 | 1.58 | 560000 | 2.3027 |
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- | 2.4407 | 1.6 | 568000 | 2.3061 |
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- | 2.4364 | 1.62 | 576000 | 2.2972 |
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- | 2.4364 | 1.65 | 584000 | 2.2967 |
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- | 2.4406 | 1.67 | 592000 | 2.2965 |
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- | 2.4406 | 1.69 | 600000 | 2.2966 |
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- | 2.4393 | 1.71 | 608000 | 2.2982 |
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- | 2.4393 | 1.74 | 616000 | 2.2993 |
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- | 2.4352 | 1.76 | 624000 | 2.2916 |
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- | 2.4352 | 1.78 | 632000 | 2.2931 |
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- | 2.4366 | 1.8 | 640000 | 2.3016 |
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- | 2.4366 | 1.83 | 648000 | 2.2984 |
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- | 2.4361 | 1.85 | 656000 | 2.2877 |
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- | 2.4361 | 1.87 | 664000 | 2.2983 |
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- | 2.437 | 1.89 | 672000 | 2.3033 |
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- | 2.437 | 1.92 | 680000 | 2.2928 |
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- | 2.4488 | 1.94 | 688000 | 2.2953 |
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- | 2.4488 | 1.96 | 696000 | 2.2945 |
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- | 2.4459 | 1.98 | 704000 | 2.2961 |
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- | 2.4459 | 2.01 | 712000 | 2.2899 |
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- | 2.4334 | 2.03 | 720000 | 2.2964 |
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- | 2.4334 | 2.05 | 728000 | 2.2896 |
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- | 2.4343 | 2.07 | 736000 | 2.2954 |
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- | 2.4343 | 2.1 | 744000 | 2.3004 |
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- | 2.4345 | 2.12 | 752000 | 2.2892 |
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- | 2.4345 | 2.14 | 760000 | 2.2996 |
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- | 2.4386 | 2.16 | 768000 | 2.2886 |
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- | 2.4386 | 2.19 | 776000 | 2.2974 |
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- | 2.434 | 2.21 | 784000 | 2.2882 |
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- | 2.434 | 2.23 | 792000 | 2.2965 |
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- | 2.4379 | 2.25 | 800000 | 2.2899 |
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- | 2.4379 | 2.28 | 808000 | 2.2938 |
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- | 2.4356 | 2.3 | 816000 | 2.2997 |
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- | 2.4356 | 2.32 | 824000 | 2.2942 |
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- | 2.4399 | 2.34 | 832000 | 2.2916 |
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- | 2.4399 | 2.37 | 840000 | 2.2934 |
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- | 2.437 | 2.39 | 848000 | 2.2978 |
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- | 2.437 | 2.41 | 856000 | 2.2834 |
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- | 2.4311 | 2.43 | 864000 | 2.2872 |
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- | 2.4311 | 2.46 | 872000 | 2.2928 |
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- | 2.4453 | 2.48 | 880000 | 2.2888 |
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- | 2.4453 | 2.5 | 888000 | 2.2933 |
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- | 2.4434 | 2.52 | 896000 | 2.2911 |
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- | 2.4434 | 2.55 | 904000 | 2.2929 |
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- | 2.443 | 2.57 | 912000 | 2.2926 |
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- | 2.443 | 2.59 | 920000 | 2.2908 |
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- | 2.4361 | 2.61 | 928000 | 2.2914 |
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- | 2.4361 | 2.64 | 936000 | 2.2878 |
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- | 2.44 | 2.66 | 944000 | 2.2872 |
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- | 2.44 | 2.68 | 952000 | 2.2857 |
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- | 2.4447 | 2.7 | 960000 | 2.2932 |
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- | 2.4447 | 2.73 | 968000 | 2.2918 |
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- | 2.4362 | 2.75 | 976000 | 2.2875 |
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- | 2.4362 | 2.77 | 984000 | 2.2900 |
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- | 2.4457 | 2.8 | 992000 | 2.2913 |
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- | 2.4457 | 2.82 | 1000000 | 2.2871 |
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- | 2.4474 | 2.84 | 1008000 | 2.2875 |
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- | 2.4474 | 2.86 | 1016000 | 2.2902 |
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- | 2.444 | 2.89 | 1024000 | 2.2878 |
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- | 2.444 | 2.91 | 1032000 | 2.2856 |
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- | 2.4316 | 2.93 | 1040000 | 2.2908 |
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- | 2.4316 | 2.95 | 1048000 | 2.2889 |
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- | 2.4388 | 2.98 | 1056000 | 2.2922 |
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- | 2.4388 | 3.0 | 1064000 | 2.2867 |
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- | 2.442 | 3.02 | 1072000 | 2.2912 |
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- | 2.442 | 3.04 | 1080000 | 2.2891 |
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- | 2.4388 | 3.07 | 1088000 | 2.2855 |
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- | 2.4388 | 3.09 | 1096000 | 2.2949 |
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- | 2.4296 | 3.11 | 1104000 | 2.2853 |
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- | 2.4296 | 3.13 | 1112000 | 2.2854 |
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- | 2.4411 | 3.16 | 1120000 | 2.2902 |
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- | 2.4411 | 3.18 | 1128000 | 2.2902 |
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- | 2.4354 | 3.2 | 1136000 | 2.2873 |
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- | 2.4354 | 3.22 | 1144000 | 2.2931 |
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- | 2.4436 | 3.25 | 1152000 | 2.2906 |
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- | 2.4436 | 3.27 | 1160000 | 2.2945 |
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- | 2.4372 | 3.29 | 1168000 | 2.2899 |
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- | 2.4372 | 3.31 | 1176000 | 2.2869 |
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- | 2.4327 | 3.34 | 1184000 | 2.2891 |
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- | 2.4327 | 3.36 | 1192000 | 2.2933 |
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- | 2.4387 | 3.38 | 1200000 | 2.2849 |
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- | 2.4387 | 3.4 | 1208000 | 2.2934 |
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- | 2.4433 | 3.43 | 1216000 | 2.2876 |
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- | 2.4433 | 3.45 | 1224000 | 2.2860 |
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- | 2.4396 | 3.47 | 1232000 | 2.2898 |
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- | 2.4396 | 3.49 | 1240000 | 2.2830 |
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- | 2.4332 | 3.52 | 1248000 | 2.2855 |
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- | 2.4332 | 3.54 | 1256000 | 2.2925 |
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- | 2.4332 | 3.56 | 1264000 | 2.2832 |
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- | 2.4332 | 3.58 | 1272000 | 2.2851 |
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- | 2.4307 | 3.61 | 1280000 | 2.2912 |
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- | 2.4307 | 3.63 | 1288000 | 2.2924 |
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- | 2.4432 | 3.65 | 1296000 | 2.2916 |
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- | 2.4432 | 3.67 | 1304000 | 2.2892 |
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- | 2.4319 | 3.7 | 1312000 | 2.2908 |
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- | 2.4319 | 3.72 | 1320000 | 2.2898 |
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- | 2.4394 | 3.74 | 1328000 | 2.2860 |
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- | 2.4394 | 3.76 | 1336000 | 2.2879 |
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- | 2.4462 | 3.79 | 1344000 | 2.2865 |
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- | 2.4462 | 3.81 | 1352000 | 2.2844 |
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- | 2.4373 | 3.83 | 1360000 | 2.2933 |
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- | 2.4373 | 3.85 | 1368000 | 2.2877 |
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- | 2.4436 | 3.88 | 1376000 | 2.2937 |
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- | 2.4436 | 3.9 | 1384000 | 2.2902 |
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- | 2.4387 | 3.92 | 1392000 | 2.2870 |
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- | 2.4387 | 3.94 | 1400000 | 2.2823 |
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- | 2.4384 | 3.97 | 1408000 | 2.2899 |
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- | 2.4384 | 3.99 | 1416000 | 2.2865 |
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- | 2.4389 | 4.01 | 1424000 | 2.2856 |
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- | 2.4389 | 4.03 | 1432000 | 2.2911 |
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- | 2.4408 | 4.06 | 1440000 | 2.2906 |
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- | 2.4408 | 4.08 | 1448000 | 2.2860 |
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- | 2.4424 | 4.1 | 1456000 | 2.2816 |
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- | 2.4424 | 4.12 | 1464000 | 2.2850 |
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- | 2.4446 | 4.15 | 1472000 | 2.2936 |
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- | 2.4446 | 4.17 | 1480000 | 2.2829 |
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- | 2.4419 | 4.19 | 1488000 | 2.2871 |
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- | 2.4419 | 4.22 | 1496000 | 2.2892 |
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- | 2.4327 | 4.24 | 1504000 | 2.2822 |
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- | 2.4327 | 4.26 | 1512000 | 2.2900 |
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- | 2.4346 | 4.28 | 1520000 | 2.2906 |
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- | 2.4346 | 4.31 | 1528000 | 2.2837 |
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- | 2.4342 | 4.33 | 1536000 | 2.2846 |
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- | 2.4342 | 4.35 | 1544000 | 2.2863 |
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- | 2.4381 | 4.37 | 1552000 | 2.2940 |
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- | 2.4381 | 4.4 | 1560000 | 2.2900 |
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- | 2.4445 | 4.42 | 1568000 | 2.2887 |
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- | 2.4445 | 4.44 | 1576000 | 2.2901 |
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- | 2.4306 | 4.46 | 1584000 | 2.2832 |
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- | 2.4306 | 4.49 | 1592000 | 2.2862 |
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- | 2.4348 | 4.51 | 1600000 | 2.2877 |
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- | 2.4348 | 4.53 | 1608000 | 2.2834 |
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- | 2.4446 | 4.55 | 1616000 | 2.2892 |
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- | 2.4446 | 4.58 | 1624000 | 2.2800 |
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- | 2.444 | 4.6 | 1632000 | 2.2891 |
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- | 2.444 | 4.62 | 1640000 | 2.2839 |
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- | 2.4335 | 4.64 | 1648000 | 2.2787 |
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- | 2.4335 | 4.67 | 1656000 | 2.2856 |
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- | 2.4369 | 4.69 | 1664000 | 2.2889 |
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- | 2.4369 | 4.71 | 1672000 | 2.2900 |
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- | 2.4446 | 4.73 | 1680000 | 2.2891 |
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- | 2.4446 | 4.76 | 1688000 | 2.2835 |
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- | 2.4334 | 4.78 | 1696000 | 2.2841 |
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- | 2.4334 | 4.8 | 1704000 | 2.2895 |
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- | 2.4426 | 4.82 | 1712000 | 2.2832 |
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- | 2.4426 | 4.85 | 1720000 | 2.2870 |
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- | 2.4434 | 4.87 | 1728000 | 2.2819 |
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- | 2.4434 | 4.89 | 1736000 | 2.2896 |
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- | 2.4382 | 4.91 | 1744000 | 2.2869 |
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- | 2.4382 | 4.94 | 1752000 | 2.2844 |
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- | 2.4405 | 4.96 | 1760000 | 2.2820 |
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- | 2.4405 | 4.98 | 1768000 | 2.2922 |
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- | 2.4507 | 5.0 | 1776000 | 2.2808 |
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- | 2.4507 | 5.03 | 1784000 | 2.2868 |
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- | 2.4437 | 5.05 | 1792000 | 2.2815 |
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- | 2.4437 | 5.07 | 1800000 | 2.2889 |
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- | 2.4373 | 5.09 | 1808000 | 2.2797 |
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- | 2.4373 | 5.12 | 1816000 | 2.2882 |
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- | 2.4368 | 5.14 | 1824000 | 2.2879 |
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- | 2.4368 | 5.16 | 1832000 | 2.2829 |
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- | 2.4398 | 5.18 | 1840000 | 2.2867 |
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- | 2.4398 | 5.21 | 1848000 | 2.2829 |
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- | 2.4469 | 5.23 | 1856000 | 2.2846 |
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- | 2.4469 | 5.25 | 1864000 | 2.2839 |
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- | 2.4457 | 5.27 | 1872000 | 2.2880 |
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- | 2.4457 | 5.3 | 1880000 | 2.2849 |
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- | 2.4444 | 5.32 | 1888000 | 2.2838 |
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- | 2.4444 | 5.34 | 1896000 | 2.2800 |
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- | 2.437 | 5.36 | 1904000 | 2.2915 |
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- | 2.437 | 5.39 | 1912000 | 2.2813 |
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- | 2.4415 | 5.41 | 1920000 | 2.2893 |
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- | 2.4415 | 5.43 | 1928000 | 2.2848 |
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- | 2.4472 | 5.45 | 1936000 | 2.2920 |
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- | 2.4472 | 5.48 | 1944000 | 2.2759 |
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- | 2.4418 | 5.5 | 1952000 | 2.2837 |
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- | 2.4418 | 5.52 | 1960000 | 2.2860 |
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- | 2.4406 | 5.54 | 1968000 | 2.2825 |
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- | 2.4406 | 5.57 | 1976000 | 2.2794 |
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- | 2.4359 | 5.59 | 1984000 | 2.2773 |
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- | 2.4359 | 5.61 | 1992000 | 2.2876 |
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- | 2.4416 | 5.64 | 2000000 | 2.2793 |
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- | 2.4416 | 5.66 | 2008000 | 2.2814 |
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- | 2.4327 | 5.68 | 2016000 | 2.2865 |
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- | 2.4327 | 5.7 | 2024000 | 2.2903 |
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- | 2.4395 | 5.73 | 2032000 | 2.2850 |
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- | 2.4395 | 5.75 | 2040000 | 2.2835 |
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- | 2.4379 | 5.77 | 2048000 | 2.2837 |
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- | 2.4379 | 5.79 | 2056000 | 2.2833 |
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- | 2.4471 | 5.82 | 2064000 | 2.2857 |
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- | 2.4471 | 5.84 | 2072000 | 2.2863 |
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- | 2.4443 | 5.86 | 2080000 | 2.2882 |
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- | 2.4443 | 5.88 | 2088000 | 2.2849 |
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- | 2.4406 | 5.91 | 2096000 | 2.2885 |
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- | 2.4406 | 5.93 | 2104000 | 2.2852 |
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- | 2.4502 | 5.95 | 2112000 | 2.2898 |
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- | 2.4502 | 5.97 | 2120000 | 2.2924 |
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- | 2.4356 | 6.0 | 2128000 | 2.2886 |
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- | 2.4356 | 6.02 | 2136000 | 2.2883 |
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- | 2.4431 | 6.04 | 2144000 | 2.2935 |
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- | 2.4431 | 6.06 | 2152000 | 2.2918 |
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- | 2.4379 | 6.09 | 2160000 | 2.2824 |
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- | 2.4379 | 6.11 | 2168000 | 2.2850 |
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- | 2.4504 | 6.13 | 2176000 | 2.2842 |
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- | 2.4504 | 6.15 | 2184000 | 2.2891 |
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- | 2.4352 | 6.18 | 2192000 | 2.2834 |
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- | 2.4352 | 6.2 | 2200000 | 2.2877 |
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- | 2.4385 | 6.22 | 2208000 | 2.2836 |
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- | 2.4385 | 6.24 | 2216000 | 2.2923 |
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- | 2.4401 | 6.27 | 2224000 | 2.2884 |
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- | 2.4401 | 6.29 | 2232000 | 2.2876 |
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- | 2.4396 | 6.31 | 2240000 | 2.2955 |
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- | 2.4396 | 6.33 | 2248000 | 2.2843 |
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- | 2.4384 | 6.36 | 2256000 | 2.2884 |
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- | 2.4384 | 6.38 | 2264000 | 2.2903 |
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- | 2.4365 | 6.4 | 2272000 | 2.2850 |
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- | 2.4365 | 6.42 | 2280000 | 2.2877 |
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- | 2.4361 | 6.45 | 2288000 | 2.2887 |
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- | 2.4361 | 6.47 | 2296000 | 2.2872 |
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- | 2.4409 | 6.49 | 2304000 | 2.2851 |
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- | 2.4409 | 6.51 | 2312000 | 2.2847 |
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- | 2.4423 | 6.54 | 2320000 | 2.2845 |
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- | 2.4423 | 6.56 | 2328000 | 2.2849 |
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- | 2.4409 | 6.58 | 2336000 | 2.2865 |
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- | 2.4409 | 6.6 | 2344000 | 2.2856 |
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- | 2.4468 | 6.63 | 2352000 | 2.2842 |
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- | 2.4468 | 6.65 | 2360000 | 2.2870 |
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- | 2.4461 | 6.67 | 2368000 | 2.2858 |
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- | 2.4461 | 6.69 | 2376000 | 2.2852 |
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- | 2.4469 | 6.72 | 2384000 | 2.2871 |
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- | 2.4469 | 6.74 | 2392000 | 2.2895 |
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- | 2.4413 | 6.76 | 2400000 | 2.2823 |
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350
 
351
  ### Framework versions
 
4
  tags:
5
  - generated_from_trainer
6
  model-index:
7
+ - name: 2020-Q3-25p-filtered-random
8
  results: []
9
  ---
10
 
11
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
12
  should probably proofread and complete it, then remove this comment. -->
13
 
14
+ # 2020-Q3-25p-filtered-random
15
 
16
  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.
17
  It achieves the following results on the evaluation set:
18
+ - Loss: 2.2660
19
 
20
  ## Model description
21
 
 
46
 
47
  | Training Loss | Epoch | Step | Validation Loss |
48
  |:-------------:|:-----:|:-------:|:---------------:|
49
+ | No log | 0.02 | 8000 | 2.5582 |
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+ | 2.8015 | 0.04 | 16000 | 2.4569 |
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+ | 2.8015 | 0.07 | 24000 | 2.4054 |
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+ | 2.5403 | 0.09 | 32000 | 2.3788 |
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+ | 2.5403 | 0.11 | 40000 | 2.3619 |
54
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55
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56
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59
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60
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66
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68
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69
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70
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71
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80
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81
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82
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102
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103
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104
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105
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107
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108
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112
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113
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114
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118
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119
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120
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121
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122
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125
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126
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127
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128
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129
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135
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136
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138
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139
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140
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141
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143
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144
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145
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146
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147
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148
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149
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150
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151
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153
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155
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156
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157
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158
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159
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160
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162
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163
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165
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166
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168
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169
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170
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172
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174
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177
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179
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180
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183
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184
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185
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186
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187
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188
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189
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190
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191
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192
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193
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194
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195
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196
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197
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198
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199
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200
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201
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202
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203
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204
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205
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210
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211
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212
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213
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214
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215
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216
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217
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218
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219
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220
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221
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222
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223
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224
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225
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226
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227
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229
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230
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232
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233
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234
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235
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236
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239
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240
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241
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244
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245
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248
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249
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250
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254
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255
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256
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257
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258
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259
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260
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261
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262
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263
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264
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265
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266
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267
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268
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269
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270
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271
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272
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273
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274
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275
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276
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277
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280
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282
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283
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284
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285
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286
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287
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288
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289
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290
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291
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292
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293
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294
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295
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296
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297
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298
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299
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300
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303
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304
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305
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306
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307
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308
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309
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310
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311
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312
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313
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314
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315
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316
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317
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318
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319
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320
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321
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322
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323
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324
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325
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326
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327
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328
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329
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330
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331
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332
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333
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334
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335
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336
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337
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338
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339
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340
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341
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342
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343
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344
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345
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346
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347
+ | 2.4044 | 6.71 | 2392000 | 2.2654 |
348
+ | 2.4003 | 6.74 | 2400000 | 2.2660 |
349
 
350
 
351
  ### Framework versions
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