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---
license: mit
base_model: cardiffnlp/twitter-roberta-base-2019-90m
tags:
- generated_from_trainer
model-index:
- name: 2020-Q4-25p-filtered
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 2020-Q4-25p-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: 2.2653
## 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 | 2.5926 |
| 2.7864 | 0.04 | 16000 | 2.5071 |
| 2.7864 | 0.07 | 24000 | 2.4690 |
| 2.5937 | 0.09 | 32000 | 2.4355 |
| 2.5937 | 0.11 | 40000 | 2.4125 |
| 2.55 | 0.13 | 48000 | 2.4009 |
| 2.55 | 0.16 | 56000 | 2.3892 |
| 2.5159 | 0.18 | 64000 | 2.3736 |
| 2.5159 | 0.2 | 72000 | 2.3713 |
| 2.495 | 0.22 | 80000 | 2.3641 |
| 2.495 | 0.25 | 88000 | 2.3574 |
| 2.4845 | 0.27 | 96000 | 2.3491 |
| 2.4845 | 0.29 | 104000 | 2.3485 |
| 2.4765 | 0.31 | 112000 | 2.3433 |
| 2.4765 | 0.34 | 120000 | 2.3376 |
| 2.472 | 0.36 | 128000 | 2.3396 |
| 2.472 | 0.38 | 136000 | 2.3326 |
| 2.467 | 0.4 | 144000 | 2.3384 |
| 2.467 | 0.43 | 152000 | 2.3350 |
| 2.46 | 0.45 | 160000 | 2.3263 |
| 2.46 | 0.47 | 168000 | 2.3231 |
| 2.4593 | 0.49 | 176000 | 2.3223 |
| 2.4593 | 0.52 | 184000 | 2.3250 |
| 2.4552 | 0.54 | 192000 | 2.3195 |
| 2.4552 | 0.56 | 200000 | 2.3236 |
| 2.4558 | 0.58 | 208000 | 2.3221 |
| 2.4558 | 0.61 | 216000 | 2.3194 |
| 2.4487 | 0.63 | 224000 | 2.3225 |
| 2.4487 | 0.65 | 232000 | 2.3221 |
| 2.4485 | 0.67 | 240000 | 2.3135 |
| 2.4485 | 0.7 | 248000 | 2.3109 |
| 2.4461 | 0.72 | 256000 | 2.3134 |
| 2.4461 | 0.74 | 264000 | 2.3177 |
| 2.4513 | 0.76 | 272000 | 2.3102 |
| 2.4513 | 0.79 | 280000 | 2.3052 |
| 2.4488 | 0.81 | 288000 | 2.3044 |
| 2.4488 | 0.83 | 296000 | 2.3117 |
| 2.4447 | 0.85 | 304000 | 2.3051 |
| 2.4447 | 0.88 | 312000 | 2.3112 |
| 2.4485 | 0.9 | 320000 | 2.3064 |
| 2.4485 | 0.92 | 328000 | 2.3099 |
| 2.4475 | 0.94 | 336000 | 2.3110 |
| 2.4475 | 0.97 | 344000 | 2.3014 |
| 2.4464 | 0.99 | 352000 | 2.3032 |
| 2.4464 | 1.01 | 360000 | 2.3036 |
| 2.4478 | 1.03 | 368000 | 2.3050 |
| 2.4478 | 1.06 | 376000 | 2.3078 |
| 2.4416 | 1.08 | 384000 | 2.3028 |
| 2.4416 | 1.1 | 392000 | 2.3017 |
| 2.4374 | 1.12 | 400000 | 2.3012 |
| 2.4374 | 1.15 | 408000 | 2.3017 |
| 2.4406 | 1.17 | 416000 | 2.3043 |
| 2.4406 | 1.19 | 424000 | 2.3058 |
| 2.4434 | 1.21 | 432000 | 2.2938 |
| 2.4434 | 1.24 | 440000 | 2.2971 |
| 2.4421 | 1.26 | 448000 | 2.3025 |
| 2.4421 | 1.28 | 456000 | 2.2950 |
| 2.443 | 1.3 | 464000 | 2.2987 |
| 2.443 | 1.32 | 472000 | 2.2949 |
| 2.4357 | 1.35 | 480000 | 2.3026 |
| 2.4357 | 1.37 | 488000 | 2.2961 |
| 2.4366 | 1.39 | 496000 | 2.3003 |
| 2.4366 | 1.41 | 504000 | 2.2954 |
| 2.4528 | 1.44 | 512000 | 2.2883 |
| 2.4528 | 1.46 | 520000 | 2.3000 |
| 2.4389 | 1.48 | 528000 | 2.2939 |
| 2.4389 | 1.5 | 536000 | 2.2990 |
| 2.441 | 1.53 | 544000 | 2.2916 |
| 2.441 | 1.55 | 552000 | 2.2906 |
| 2.4372 | 1.57 | 560000 | 2.2885 |
| 2.4372 | 1.59 | 568000 | 2.3003 |
| 2.4379 | 1.62 | 576000 | 2.2988 |
| 2.4379 | 1.64 | 584000 | 2.2923 |
| 2.4347 | 1.66 | 592000 | 2.2937 |
| 2.4347 | 1.68 | 600000 | 2.2958 |
| 2.4311 | 1.71 | 608000 | 2.2995 |
| 2.4311 | 1.73 | 616000 | 2.2941 |
| 2.4437 | 1.75 | 624000 | 2.2949 |
| 2.4437 | 1.77 | 632000 | 2.2878 |
| 2.4306 | 1.8 | 640000 | 2.2895 |
| 2.4306 | 1.82 | 648000 | 2.2930 |
| 2.4341 | 1.84 | 656000 | 2.2895 |
| 2.4341 | 1.86 | 664000 | 2.2908 |
| 2.4333 | 1.89 | 672000 | 2.2842 |
| 2.4333 | 1.91 | 680000 | 2.2912 |
| 2.4403 | 1.93 | 688000 | 2.2900 |
| 2.4403 | 1.95 | 696000 | 2.2862 |
| 2.4396 | 1.98 | 704000 | 2.2871 |
| 2.4396 | 2.0 | 712000 | 2.2948 |
| 2.441 | 2.02 | 720000 | 2.2942 |
| 2.441 | 2.04 | 728000 | 2.2828 |
| 2.434 | 2.07 | 736000 | 2.2808 |
| 2.434 | 2.09 | 744000 | 2.2883 |
| 2.4387 | 2.11 | 752000 | 2.2923 |
| 2.4387 | 2.13 | 760000 | 2.2848 |
| 2.4342 | 2.16 | 768000 | 2.2848 |
| 2.4342 | 2.18 | 776000 | 2.2865 |
| 2.4389 | 2.2 | 784000 | 2.2885 |
| 2.4389 | 2.22 | 792000 | 2.2794 |
| 2.4318 | 2.25 | 800000 | 2.2861 |
| 2.4318 | 2.27 | 808000 | 2.2876 |
| 2.4343 | 2.29 | 816000 | 2.2820 |
| 2.4343 | 2.31 | 824000 | 2.2835 |
| 2.4335 | 2.34 | 832000 | 2.2788 |
| 2.4335 | 2.36 | 840000 | 2.2813 |
| 2.4428 | 2.38 | 848000 | 2.2789 |
| 2.4428 | 2.4 | 856000 | 2.2858 |
| 2.4272 | 2.43 | 864000 | 2.2883 |
| 2.4272 | 2.45 | 872000 | 2.2809 |
| 2.4331 | 2.47 | 880000 | 2.2880 |
| 2.4331 | 2.49 | 888000 | 2.2838 |
| 2.4326 | 2.52 | 896000 | 2.2804 |
| 2.4326 | 2.54 | 904000 | 2.2831 |
| 2.436 | 2.56 | 912000 | 2.2867 |
| 2.436 | 2.58 | 920000 | 2.2848 |
| 2.435 | 2.6 | 928000 | 2.2871 |
| 2.435 | 2.63 | 936000 | 2.2828 |
| 2.44 | 2.65 | 944000 | 2.2808 |
| 2.44 | 2.67 | 952000 | 2.2853 |
| 2.4285 | 2.69 | 960000 | 2.2799 |
| 2.4285 | 2.72 | 968000 | 2.2829 |
| 2.423 | 2.74 | 976000 | 2.2761 |
| 2.423 | 2.76 | 984000 | 2.2768 |
| 2.4353 | 2.78 | 992000 | 2.2844 |
| 2.4353 | 2.81 | 1000000 | 2.2828 |
| 2.4301 | 2.83 | 1008000 | 2.2806 |
| 2.4301 | 2.85 | 1016000 | 2.2813 |
| 2.4284 | 2.87 | 1024000 | 2.2789 |
| 2.4284 | 2.9 | 1032000 | 2.2770 |
| 2.4252 | 2.92 | 1040000 | 2.2763 |
| 2.4252 | 2.94 | 1048000 | 2.2763 |
| 2.4289 | 2.96 | 1056000 | 2.2779 |
| 2.4289 | 2.99 | 1064000 | 2.2812 |
| 2.4349 | 3.01 | 1072000 | 2.2881 |
| 2.4349 | 3.03 | 1080000 | 2.2805 |
| 2.4365 | 3.05 | 1088000 | 2.2758 |
| 2.4365 | 3.08 | 1096000 | 2.2733 |
| 2.4274 | 3.1 | 1104000 | 2.2842 |
| 2.4274 | 3.12 | 1112000 | 2.2808 |
| 2.4326 | 3.14 | 1120000 | 2.2753 |
| 2.4326 | 3.17 | 1128000 | 2.2792 |
| 2.4244 | 3.19 | 1136000 | 2.2788 |
| 2.4244 | 3.21 | 1144000 | 2.2824 |
| 2.4285 | 3.23 | 1152000 | 2.2800 |
| 2.4285 | 3.26 | 1160000 | 2.2784 |
| 2.4371 | 3.28 | 1168000 | 2.2675 |
| 2.4371 | 3.3 | 1176000 | 2.2740 |
| 2.4273 | 3.32 | 1184000 | 2.2805 |
| 2.4273 | 3.35 | 1192000 | 2.2849 |
| 2.4359 | 3.37 | 1200000 | 2.2808 |
| 2.4359 | 3.39 | 1208000 | 2.2791 |
| 2.4303 | 3.41 | 1216000 | 2.2730 |
| 2.4303 | 3.44 | 1224000 | 2.2732 |
| 2.4306 | 3.46 | 1232000 | 2.2785 |
| 2.4306 | 3.48 | 1240000 | 2.2764 |
| 2.4267 | 3.5 | 1248000 | 2.2740 |
| 2.4267 | 3.53 | 1256000 | 2.2789 |
| 2.4271 | 3.55 | 1264000 | 2.2774 |
| 2.4271 | 3.57 | 1272000 | 2.2768 |
| 2.4263 | 3.59 | 1280000 | 2.2796 |
| 2.4263 | 3.62 | 1288000 | 2.2759 |
| 2.431 | 3.64 | 1296000 | 2.2741 |
| 2.431 | 3.66 | 1304000 | 2.2821 |
| 2.4273 | 3.68 | 1312000 | 2.2740 |
| 2.4273 | 3.71 | 1320000 | 2.2713 |
| 2.4371 | 3.73 | 1328000 | 2.2704 |
| 2.4371 | 3.75 | 1336000 | 2.2734 |
| 2.4273 | 3.77 | 1344000 | 2.2746 |
| 2.4273 | 3.8 | 1352000 | 2.2840 |
| 2.4246 | 3.82 | 1360000 | 2.2764 |
| 2.4246 | 3.84 | 1368000 | 2.2740 |
| 2.4308 | 3.86 | 1376000 | 2.2730 |
| 2.4308 | 3.88 | 1384000 | 2.2751 |
| 2.4341 | 3.91 | 1392000 | 2.2777 |
| 2.4341 | 3.93 | 1400000 | 2.2679 |
| 2.4266 | 3.95 | 1408000 | 2.2777 |
| 2.4266 | 3.97 | 1416000 | 2.2783 |
| 2.4344 | 4.0 | 1424000 | 2.2743 |
| 2.4344 | 4.02 | 1432000 | 2.2691 |
| 2.431 | 4.04 | 1440000 | 2.2714 |
| 2.431 | 4.06 | 1448000 | 2.2694 |
| 2.4296 | 4.09 | 1456000 | 2.2749 |
| 2.4296 | 4.11 | 1464000 | 2.2810 |
| 2.4265 | 4.13 | 1472000 | 2.2744 |
| 2.4265 | 4.15 | 1480000 | 2.2714 |
| 2.4266 | 4.18 | 1488000 | 2.2733 |
| 2.4266 | 4.2 | 1496000 | 2.2790 |
| 2.4253 | 4.22 | 1504000 | 2.2766 |
| 2.4253 | 4.24 | 1512000 | 2.2764 |
| 2.4303 | 4.27 | 1520000 | 2.2692 |
| 2.4303 | 4.29 | 1528000 | 2.2684 |
| 2.4373 | 4.31 | 1536000 | 2.2752 |
| 2.4373 | 4.33 | 1544000 | 2.2701 |
| 2.4346 | 4.36 | 1552000 | 2.2758 |
| 2.4346 | 4.38 | 1560000 | 2.2727 |
| 2.4294 | 4.4 | 1568000 | 2.2753 |
| 2.4294 | 4.42 | 1576000 | 2.2687 |
| 2.439 | 4.45 | 1584000 | 2.2776 |
| 2.439 | 4.47 | 1592000 | 2.2746 |
| 2.4337 | 4.49 | 1600000 | 2.2731 |
| 2.4337 | 4.51 | 1608000 | 2.2722 |
| 2.4273 | 4.54 | 1616000 | 2.2703 |
| 2.4273 | 4.56 | 1624000 | 2.2802 |
| 2.4275 | 4.58 | 1632000 | 2.2707 |
| 2.4275 | 4.6 | 1640000 | 2.2707 |
| 2.4201 | 4.63 | 1648000 | 2.2686 |
| 2.4201 | 4.65 | 1656000 | 2.2707 |
| 2.4319 | 4.67 | 1664000 | 2.2740 |
| 2.4319 | 4.69 | 1672000 | 2.2697 |
| 2.4314 | 4.72 | 1680000 | 2.2747 |
| 2.4314 | 4.74 | 1688000 | 2.2694 |
| 2.4242 | 4.76 | 1696000 | 2.2732 |
| 2.4242 | 4.78 | 1704000 | 2.2726 |
| 2.4302 | 4.81 | 1712000 | 2.2704 |
| 2.4302 | 4.83 | 1720000 | 2.2755 |
| 2.4375 | 4.85 | 1728000 | 2.2701 |
| 2.4375 | 4.87 | 1736000 | 2.2720 |
| 2.4305 | 4.9 | 1744000 | 2.2698 |
| 2.4305 | 4.92 | 1752000 | 2.2721 |
| 2.4353 | 4.94 | 1760000 | 2.2752 |
| 2.4353 | 4.96 | 1768000 | 2.2763 |
| 2.4274 | 4.99 | 1776000 | 2.2747 |
| 2.4274 | 5.01 | 1784000 | 2.2776 |
| 2.4234 | 5.03 | 1792000 | 2.2706 |
| 2.4234 | 5.05 | 1800000 | 2.2719 |
| 2.4304 | 5.08 | 1808000 | 2.2667 |
| 2.4304 | 5.1 | 1816000 | 2.2762 |
| 2.4308 | 5.12 | 1824000 | 2.2757 |
| 2.4308 | 5.14 | 1832000 | 2.2712 |
| 2.4342 | 5.16 | 1840000 | 2.2676 |
| 2.4342 | 5.19 | 1848000 | 2.2738 |
| 2.4342 | 5.21 | 1856000 | 2.2755 |
| 2.4342 | 5.23 | 1864000 | 2.2741 |
| 2.4329 | 5.25 | 1872000 | 2.2734 |
| 2.4329 | 5.28 | 1880000 | 2.2714 |
| 2.4306 | 5.3 | 1888000 | 2.2722 |
| 2.4306 | 5.32 | 1896000 | 2.2702 |
| 2.4302 | 5.34 | 1904000 | 2.2761 |
| 2.4302 | 5.37 | 1912000 | 2.2748 |
| 2.4303 | 5.39 | 1920000 | 2.2763 |
| 2.4303 | 5.41 | 1928000 | 2.2731 |
| 2.4234 | 5.43 | 1936000 | 2.2676 |
| 2.4234 | 5.46 | 1944000 | 2.2750 |
| 2.4349 | 5.48 | 1952000 | 2.2769 |
| 2.4349 | 5.5 | 1960000 | 2.2728 |
| 2.4295 | 5.52 | 1968000 | 2.2750 |
| 2.4295 | 5.55 | 1976000 | 2.2702 |
| 2.428 | 5.57 | 1984000 | 2.2729 |
| 2.428 | 5.59 | 1992000 | 2.2707 |
| 2.4336 | 5.61 | 2000000 | 2.2774 |
| 2.4336 | 5.64 | 2008000 | 2.2735 |
| 2.4332 | 5.66 | 2016000 | 2.2634 |
| 2.4332 | 5.68 | 2024000 | 2.2679 |
| 2.4342 | 5.7 | 2032000 | 2.2753 |
| 2.4342 | 5.73 | 2040000 | 2.2719 |
| 2.4279 | 5.75 | 2048000 | 2.2711 |
| 2.4279 | 5.77 | 2056000 | 2.2778 |
| 2.4281 | 5.79 | 2064000 | 2.2693 |
| 2.4281 | 5.82 | 2072000 | 2.2715 |
| 2.4246 | 5.84 | 2080000 | 2.2674 |
| 2.4246 | 5.86 | 2088000 | 2.2700 |
| 2.4235 | 5.88 | 2096000 | 2.2703 |
| 2.4235 | 5.91 | 2104000 | 2.2723 |
| 2.4388 | 5.93 | 2112000 | 2.2683 |
| 2.4388 | 5.95 | 2120000 | 2.2712 |
| 2.431 | 5.97 | 2128000 | 2.2739 |
| 2.431 | 6.0 | 2136000 | 2.2757 |
| 2.4329 | 6.02 | 2144000 | 2.2785 |
| 2.4329 | 6.04 | 2152000 | 2.2721 |
| 2.4266 | 6.06 | 2160000 | 2.2745 |
| 2.4266 | 6.09 | 2168000 | 2.2738 |
| 2.4255 | 6.11 | 2176000 | 2.2735 |
| 2.4255 | 6.13 | 2184000 | 2.2667 |
| 2.4263 | 6.15 | 2192000 | 2.2766 |
| 2.4263 | 6.18 | 2200000 | 2.2754 |
| 2.4388 | 6.2 | 2208000 | 2.2694 |
| 2.4388 | 6.22 | 2216000 | 2.2675 |
| 2.4293 | 6.24 | 2224000 | 2.2699 |
| 2.4293 | 6.27 | 2232000 | 2.2712 |
| 2.428 | 6.29 | 2240000 | 2.2707 |
| 2.428 | 6.31 | 2248000 | 2.2732 |
| 2.4247 | 6.33 | 2256000 | 2.2752 |
| 2.4247 | 6.36 | 2264000 | 2.2703 |
| 2.4272 | 6.38 | 2272000 | 2.2690 |
| 2.4272 | 6.4 | 2280000 | 2.2775 |
| 2.4297 | 6.42 | 2288000 | 2.2680 |
| 2.4297 | 6.45 | 2296000 | 2.2712 |
| 2.4268 | 6.47 | 2304000 | 2.2815 |
| 2.4268 | 6.49 | 2312000 | 2.2697 |
| 2.4248 | 6.51 | 2320000 | 2.2794 |
| 2.4248 | 6.53 | 2328000 | 2.2722 |
| 2.4285 | 6.56 | 2336000 | 2.2686 |
| 2.4285 | 6.58 | 2344000 | 2.2741 |
| 2.4318 | 6.6 | 2352000 | 2.2679 |
| 2.4318 | 6.62 | 2360000 | 2.2723 |
| 2.4269 | 6.65 | 2368000 | 2.2741 |
| 2.4269 | 6.67 | 2376000 | 2.2739 |
| 2.4275 | 6.69 | 2384000 | 2.2744 |
| 2.4275 | 6.71 | 2392000 | 2.2765 |
| 2.4259 | 6.74 | 2400000 | 2.2788 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.14.0
|