--- license: mit base_model: cardiffnlp/twitter-roberta-base-2019-90m tags: - generated_from_trainer model-index: - name: 2020-Q4-50p-filtered-random results: [] --- # 2020-Q4-50p-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: 2.2570 ## 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 | 0.03 | 8000 | 2.5888 | | 2.8176 | 0.07 | 16000 | 2.4814 | | 2.8176 | 0.1 | 24000 | 2.4264 | | 2.5609 | 0.13 | 32000 | 2.3993 | | 2.5609 | 0.17 | 40000 | 2.3761 | | 2.4969 | 0.2 | 48000 | 2.3624 | | 2.4969 | 0.24 | 56000 | 2.3481 | | 2.48 | 0.27 | 64000 | 2.3399 | | 2.48 | 0.3 | 72000 | 2.3289 | | 2.451 | 0.34 | 80000 | 2.3221 | | 2.451 | 0.37 | 88000 | 2.3183 | | 2.4367 | 0.4 | 96000 | 2.3221 | | 2.4367 | 0.44 | 104000 | 2.3142 | | 2.4388 | 0.47 | 112000 | 2.3028 | | 2.4388 | 0.51 | 120000 | 2.3066 | | 2.4215 | 0.54 | 128000 | 2.3013 | | 2.4215 | 0.57 | 136000 | 2.3039 | | 2.4178 | 0.61 | 144000 | 2.2907 | | 2.4178 | 0.64 | 152000 | 2.2996 | | 2.4103 | 0.67 | 160000 | 2.2943 | | 2.4103 | 0.71 | 168000 | 2.2900 | | 2.4122 | 0.74 | 176000 | 2.2902 | | 2.4122 | 0.77 | 184000 | 2.2961 | | 2.4173 | 0.81 | 192000 | 2.2906 | | 2.4173 | 0.84 | 200000 | 2.2925 | | 2.4067 | 0.88 | 208000 | 2.2911 | | 2.4067 | 0.91 | 216000 | 2.2844 | | 2.4059 | 0.94 | 224000 | 2.2855 | | 2.4059 | 0.98 | 232000 | 2.2811 | | 2.4089 | 1.01 | 240000 | 2.2788 | | 2.4089 | 1.04 | 248000 | 2.2796 | | 2.4034 | 1.08 | 256000 | 2.2827 | | 2.4034 | 1.11 | 264000 | 2.2803 | | 2.408 | 1.15 | 272000 | 2.2746 | | 2.408 | 1.18 | 280000 | 2.2851 | | 2.3985 | 1.21 | 288000 | 2.2781 | | 2.3985 | 1.25 | 296000 | 2.2795 | | 2.4009 | 1.28 | 304000 | 2.2777 | | 2.4009 | 1.31 | 312000 | 2.2770 | | 2.4017 | 1.35 | 320000 | 2.2763 | | 2.4017 | 1.38 | 328000 | 2.2734 | | 2.4056 | 1.41 | 336000 | 2.2758 | | 2.4056 | 1.45 | 344000 | 2.2763 | | 2.4017 | 1.48 | 352000 | 2.2700 | | 2.4017 | 1.52 | 360000 | 2.2736 | | 2.3993 | 1.55 | 368000 | 2.2763 | | 2.3993 | 1.58 | 376000 | 2.2792 | | 2.3994 | 1.62 | 384000 | 2.2666 | | 2.3994 | 1.65 | 392000 | 2.2699 | | 2.3969 | 1.68 | 400000 | 2.2753 | | 2.3969 | 1.72 | 408000 | 2.2707 | | 2.4094 | 1.75 | 416000 | 2.2731 | | 2.4094 | 1.79 | 424000 | 2.2709 | | 2.4102 | 1.82 | 432000 | 2.2623 | | 2.4102 | 1.85 | 440000 | 2.2751 | | 2.4042 | 1.89 | 448000 | 2.2728 | | 2.4042 | 1.92 | 456000 | 2.2714 | | 2.3991 | 1.95 | 464000 | 2.2634 | | 2.3991 | 1.99 | 472000 | 2.2695 | | 2.3976 | 2.02 | 480000 | 2.2731 | | 2.3976 | 2.05 | 488000 | 2.2736 | | 2.4019 | 2.09 | 496000 | 2.2803 | | 2.4019 | 2.12 | 504000 | 2.2699 | | 2.4044 | 2.16 | 512000 | 2.2731 | | 2.4044 | 2.19 | 520000 | 2.2709 | | 2.3989 | 2.22 | 528000 | 2.2716 | | 2.3989 | 2.26 | 536000 | 2.2668 | | 2.4068 | 2.29 | 544000 | 2.2728 | | 2.4068 | 2.32 | 552000 | 2.2709 | | 2.4047 | 2.36 | 560000 | 2.2683 | | 2.4047 | 2.39 | 568000 | 2.2731 | | 2.3976 | 2.43 | 576000 | 2.2676 | | 2.3976 | 2.46 | 584000 | 2.2736 | | 2.3994 | 2.49 | 592000 | 2.2624 | | 2.3994 | 2.53 | 600000 | 2.2773 | | 2.3997 | 2.56 | 608000 | 2.2719 | | 2.3997 | 2.59 | 616000 | 2.2701 | | 2.4042 | 2.63 | 624000 | 2.2695 | | 2.4042 | 2.66 | 632000 | 2.2666 | | 2.3994 | 2.69 | 640000 | 2.2719 | | 2.3994 | 2.73 | 648000 | 2.2686 | | 2.3953 | 2.76 | 656000 | 2.2623 | | 2.3953 | 2.8 | 664000 | 2.2662 | | 2.402 | 2.83 | 672000 | 2.2707 | | 2.402 | 2.86 | 680000 | 2.2662 | | 2.3929 | 2.9 | 688000 | 2.2726 | | 2.3929 | 2.93 | 696000 | 2.2682 | | 2.3977 | 2.96 | 704000 | 2.2634 | | 2.3977 | 3.0 | 712000 | 2.2685 | | 2.4022 | 3.03 | 720000 | 2.2693 | | 2.4022 | 3.07 | 728000 | 2.2666 | | 2.4046 | 3.1 | 736000 | 2.2690 | | 2.4046 | 3.13 | 744000 | 2.2641 | | 2.3977 | 3.17 | 752000 | 2.2658 | | 2.3977 | 3.2 | 760000 | 2.2645 | | 2.4015 | 3.23 | 768000 | 2.2619 | | 2.4015 | 3.27 | 776000 | 2.2671 | | 2.393 | 3.3 | 784000 | 2.2694 | | 2.393 | 3.33 | 792000 | 2.2662 | | 2.3907 | 3.37 | 800000 | 2.2691 | | 2.3907 | 3.4 | 808000 | 2.2679 | | 2.3987 | 3.44 | 816000 | 2.2688 | | 2.3987 | 3.47 | 824000 | 2.2655 | | 2.4116 | 3.5 | 832000 | 2.2668 | | 2.4116 | 3.54 | 840000 | 2.2675 | | 2.3913 | 3.57 | 848000 | 2.2689 | | 2.3913 | 3.6 | 856000 | 2.2642 | | 2.3974 | 3.64 | 864000 | 2.2667 | | 2.3974 | 3.67 | 872000 | 2.2717 | | 2.4046 | 3.71 | 880000 | 2.2661 | | 2.4046 | 3.74 | 888000 | 2.2705 | | 2.4006 | 3.77 | 896000 | 2.2637 | | 2.4006 | 3.81 | 904000 | 2.2635 | | 2.3987 | 3.84 | 912000 | 2.2642 | | 2.3987 | 3.87 | 920000 | 2.2691 | | 2.4068 | 3.91 | 928000 | 2.2689 | | 2.4068 | 3.94 | 936000 | 2.2730 | | 2.4092 | 3.97 | 944000 | 2.2644 | | 2.4092 | 4.01 | 952000 | 2.2706 | | 2.4035 | 4.04 | 960000 | 2.2671 | | 2.4035 | 4.08 | 968000 | 2.2562 | | 2.4005 | 4.11 | 976000 | 2.2622 | | 2.4005 | 4.14 | 984000 | 2.2642 | | 2.406 | 4.18 | 992000 | 2.2625 | | 2.406 | 4.21 | 1000000 | 2.2662 | | 2.3972 | 4.24 | 1008000 | 2.2658 | | 2.3972 | 4.28 | 1016000 | 2.2668 | | 2.3937 | 4.31 | 1024000 | 2.2593 | | 2.3937 | 4.35 | 1032000 | 2.2712 | | 2.3982 | 4.38 | 1040000 | 2.2695 | | 2.3982 | 4.41 | 1048000 | 2.2684 | | 2.4034 | 4.45 | 1056000 | 2.2643 | | 2.4034 | 4.48 | 1064000 | 2.2665 | | 2.3996 | 4.51 | 1072000 | 2.2692 | | 2.3996 | 4.55 | 1080000 | 2.2628 | | 2.4054 | 4.58 | 1088000 | 2.2673 | | 2.4054 | 4.61 | 1096000 | 2.2577 | | 2.4039 | 4.65 | 1104000 | 2.2671 | | 2.4039 | 4.68 | 1112000 | 2.2586 | | 2.4033 | 4.72 | 1120000 | 2.2730 | | 2.4033 | 4.75 | 1128000 | 2.2655 | | 2.4036 | 4.78 | 1136000 | 2.2694 | | 2.4036 | 4.82 | 1144000 | 2.2630 | | 2.4036 | 4.85 | 1152000 | 2.2618 | | 2.4036 | 4.88 | 1160000 | 2.2665 | | 2.4005 | 4.92 | 1168000 | 2.2609 | | 2.4005 | 4.95 | 1176000 | 2.2617 | | 2.4065 | 4.99 | 1184000 | 2.2646 | | 2.4065 | 5.02 | 1192000 | 2.2606 | | 2.4044 | 5.05 | 1200000 | 2.2656 | | 2.4044 | 5.09 | 1208000 | 2.2630 | | 2.3997 | 5.12 | 1216000 | 2.2737 | | 2.3997 | 5.15 | 1224000 | 2.2762 | | 2.407 | 5.19 | 1232000 | 2.2669 | | 2.407 | 5.22 | 1240000 | 2.2695 | | 2.4013 | 5.25 | 1248000 | 2.2704 | | 2.4013 | 5.29 | 1256000 | 2.2612 | | 2.4118 | 5.32 | 1264000 | 2.2654 | | 2.4118 | 5.36 | 1272000 | 2.2683 | | 2.3953 | 5.39 | 1280000 | 2.2628 | | 2.3953 | 5.42 | 1288000 | 2.2605 | | 2.3973 | 5.46 | 1296000 | 2.2667 | | 2.3973 | 5.49 | 1304000 | 2.2640 | | 2.4027 | 5.52 | 1312000 | 2.2619 | | 2.4027 | 5.56 | 1320000 | 2.2687 | | 2.3967 | 5.59 | 1328000 | 2.2598 | | 2.3967 | 5.63 | 1336000 | 2.2621 | | 2.4028 | 5.66 | 1344000 | 2.2602 | | 2.4028 | 5.69 | 1352000 | 2.2713 | | 2.4053 | 5.73 | 1360000 | 2.2623 | | 2.4053 | 5.76 | 1368000 | 2.2697 | | 2.3987 | 5.79 | 1376000 | 2.2638 | | 2.3987 | 5.83 | 1384000 | 2.2601 | | 2.3987 | 5.86 | 1392000 | 2.2642 | | 2.3987 | 5.89 | 1400000 | 2.2656 | | 2.401 | 5.93 | 1408000 | 2.2712 | | 2.401 | 5.96 | 1416000 | 2.2639 | | 2.4011 | 6.0 | 1424000 | 2.2646 | | 2.4011 | 6.03 | 1432000 | 2.2669 | | 2.4022 | 6.06 | 1440000 | 2.2619 | | 2.4022 | 6.1 | 1448000 | 2.2580 | | 2.3998 | 6.13 | 1456000 | 2.2612 | | 2.3998 | 6.16 | 1464000 | 2.2652 | | 2.3999 | 6.2 | 1472000 | 2.2610 | | 2.3999 | 6.23 | 1480000 | 2.2567 | | 2.3984 | 6.27 | 1488000 | 2.2590 | | 2.3984 | 6.3 | 1496000 | 2.2565 | | 2.4017 | 6.33 | 1504000 | 2.2658 | | 2.4017 | 6.37 | 1512000 | 2.2626 | | 2.4055 | 6.4 | 1520000 | 2.2656 | | 2.4055 | 6.43 | 1528000 | 2.2622 | | 2.3959 | 6.47 | 1536000 | 2.2691 | | 2.3959 | 6.5 | 1544000 | 2.2604 | | 2.4016 | 6.53 | 1552000 | 2.2599 | | 2.4016 | 6.57 | 1560000 | 2.2655 | | 2.3986 | 6.6 | 1568000 | 2.2684 | | 2.3986 | 6.64 | 1576000 | 2.2716 | | 2.4051 | 6.67 | 1584000 | 2.2605 | | 2.4051 | 6.7 | 1592000 | 2.2569 | | 2.4057 | 6.74 | 1600000 | 2.2687 | | 2.4057 | 6.77 | 1608000 | 2.2571 | | 2.3956 | 6.8 | 1616000 | 2.2664 | | 2.3956 | 6.84 | 1624000 | 2.2612 | | 2.4048 | 6.87 | 1632000 | 2.2643 | | 2.4048 | 6.91 | 1640000 | 2.2633 | | 2.4042 | 6.94 | 1648000 | 2.2634 | | 2.4042 | 6.97 | 1656000 | 2.2637 | | 2.4008 | 7.01 | 1664000 | 2.2619 | | 2.4008 | 7.04 | 1672000 | 2.2579 | | 2.397 | 7.07 | 1680000 | 2.2628 | | 2.397 | 7.11 | 1688000 | 2.2593 | | 2.4044 | 7.14 | 1696000 | 2.2593 | | 2.4044 | 7.17 | 1704000 | 2.2613 | | 2.3979 | 7.21 | 1712000 | 2.2685 | | 2.3979 | 7.24 | 1720000 | 2.2683 | | 2.4017 | 7.28 | 1728000 | 2.2611 | | 2.4017 | 7.31 | 1736000 | 2.2672 | | 2.4017 | 7.34 | 1744000 | 2.2577 | | 2.4017 | 7.38 | 1752000 | 2.2609 | | 2.4018 | 7.41 | 1760000 | 2.2567 | | 2.4018 | 7.44 | 1768000 | 2.2661 | | 2.3905 | 7.48 | 1776000 | 2.2671 | | 2.3905 | 7.51 | 1784000 | 2.2663 | | 2.4063 | 7.55 | 1792000 | 2.2619 | | 2.4063 | 7.58 | 1800000 | 2.2587 | | 2.4015 | 7.61 | 1808000 | 2.2584 | | 2.4015 | 7.65 | 1816000 | 2.2580 | | 2.3984 | 7.68 | 1824000 | 2.2586 | | 2.3984 | 7.71 | 1832000 | 2.2620 | | 2.3962 | 7.75 | 1840000 | 2.2584 | | 2.3962 | 7.78 | 1848000 | 2.2607 | | 2.3998 | 7.81 | 1856000 | 2.2638 | | 2.3998 | 7.85 | 1864000 | 2.2629 | | 2.4005 | 7.88 | 1872000 | 2.2716 | | 2.4005 | 7.92 | 1880000 | 2.2623 | | 2.4006 | 7.95 | 1888000 | 2.2555 | | 2.4006 | 7.98 | 1896000 | 2.2653 | | 2.3946 | 8.02 | 1904000 | 2.2629 | | 2.3946 | 8.05 | 1912000 | 2.2654 | | 2.3983 | 8.08 | 1920000 | 2.2623 | | 2.3983 | 8.12 | 1928000 | 2.2544 | | 2.4038 | 8.15 | 1936000 | 2.2605 | | 2.4038 | 8.19 | 1944000 | 2.2622 | | 2.399 | 8.22 | 1952000 | 2.2600 | | 2.399 | 8.25 | 1960000 | 2.2629 | | 2.3983 | 8.29 | 1968000 | 2.2621 | | 2.3983 | 8.32 | 1976000 | 2.2609 | | 2.4059 | 8.35 | 1984000 | 2.2705 | | 2.4059 | 8.39 | 1992000 | 2.2572 | | 2.4058 | 8.42 | 2000000 | 2.2602 | | 2.4058 | 8.45 | 2008000 | 2.2626 | | 2.3954 | 8.49 | 2016000 | 2.2668 | | 2.3954 | 8.52 | 2024000 | 2.2599 | | 2.3932 | 8.56 | 2032000 | 2.2643 | | 2.3932 | 8.59 | 2040000 | 2.2559 | | 2.4001 | 8.62 | 2048000 | 2.2614 | | 2.4001 | 8.66 | 2056000 | 2.2577 | | 2.3912 | 8.69 | 2064000 | 2.2665 | | 2.3912 | 8.72 | 2072000 | 2.2576 | | 2.4015 | 8.76 | 2080000 | 2.2672 | | 2.4015 | 8.79 | 2088000 | 2.2598 | | 2.4015 | 8.83 | 2096000 | 2.2599 | | 2.4015 | 8.86 | 2104000 | 2.2641 | | 2.399 | 8.89 | 2112000 | 2.2612 | | 2.399 | 8.93 | 2120000 | 2.2607 | | 2.3963 | 8.96 | 2128000 | 2.2633 | | 2.3963 | 8.99 | 2136000 | 2.2567 | | 2.3957 | 9.03 | 2144000 | 2.2630 | | 2.3957 | 9.06 | 2152000 | 2.2597 | | 2.3943 | 9.09 | 2160000 | 2.2624 | | 2.3943 | 9.13 | 2168000 | 2.2599 | | 2.4025 | 9.16 | 2176000 | 2.2578 | | 2.4025 | 9.2 | 2184000 | 2.2640 | | 2.3944 | 9.23 | 2192000 | 2.2562 | | 2.3944 | 9.26 | 2200000 | 2.2660 | | 2.3964 | 9.3 | 2208000 | 2.2556 | | 2.3964 | 9.33 | 2216000 | 2.2697 | | 2.4026 | 9.36 | 2224000 | 2.2652 | | 2.4026 | 9.4 | 2232000 | 2.2571 | | 2.398 | 9.43 | 2240000 | 2.2555 | | 2.398 | 9.47 | 2248000 | 2.2607 | | 2.4038 | 9.5 | 2256000 | 2.2558 | | 2.4038 | 9.53 | 2264000 | 2.2660 | | 2.4027 | 9.57 | 2272000 | 2.2587 | | 2.4027 | 9.6 | 2280000 | 2.2605 | | 2.4025 | 9.63 | 2288000 | 2.2578 | | 2.4025 | 9.67 | 2296000 | 2.2609 | | 2.3969 | 9.7 | 2304000 | 2.2597 | | 2.3969 | 9.73 | 2312000 | 2.2619 | | 2.3886 | 9.77 | 2320000 | 2.2645 | | 2.3886 | 9.8 | 2328000 | 2.2717 | | 2.3942 | 9.84 | 2336000 | 2.2627 | | 2.3942 | 9.87 | 2344000 | 2.2582 | | 2.396 | 9.9 | 2352000 | 2.2634 | | 2.396 | 9.94 | 2360000 | 2.2582 | | 2.3998 | 9.97 | 2368000 | 2.2643 | | 2.3998 | 10.0 | 2376000 | 2.2690 | | 2.4014 | 10.04 | 2384000 | 2.2655 | | 2.4014 | 10.07 | 2392000 | 2.2660 | | 2.4004 | 10.11 | 2400000 | 2.2650 | ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.14.0