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--- |
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license: mit |
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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-Q4-25p-filtered-random |
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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-Q4-25p-filtered-random |
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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: 2.2681 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 4.1e-07 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- training_steps: 2400000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-------:|:---------------:| |
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| No log | 0.02 | 8000 | 2.5802 | |
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| 2.8151 | 0.04 | 16000 | 2.4882 | |
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| 2.8151 | 0.07 | 24000 | 2.4292 | |
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| 2.5636 | 0.09 | 32000 | 2.3980 | |
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| 2.5636 | 0.11 | 40000 | 2.3799 | |
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| 2.4947 | 0.13 | 48000 | 2.3665 | |
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| 2.4947 | 0.16 | 56000 | 2.3455 | |
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| 2.473 | 0.18 | 64000 | 2.3419 | |
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| 2.473 | 0.2 | 72000 | 2.3307 | |
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| 2.4512 | 0.22 | 80000 | 2.3289 | |
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| 2.4512 | 0.25 | 88000 | 2.3250 | |
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| 2.4421 | 0.27 | 96000 | 2.3189 | |
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| 2.4421 | 0.29 | 104000 | 2.3200 | |
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| 2.4354 | 0.31 | 112000 | 2.3155 | |
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| 2.4354 | 0.34 | 120000 | 2.3138 | |
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| 2.4324 | 0.36 | 128000 | 2.3054 | |
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| 2.4324 | 0.38 | 136000 | 2.3028 | |
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| 2.4253 | 0.4 | 144000 | 2.3029 | |
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| 2.4253 | 0.43 | 152000 | 2.3006 | |
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| 2.4156 | 0.45 | 160000 | 2.3001 | |
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| 2.4156 | 0.47 | 168000 | 2.2980 | |
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| 2.4165 | 0.49 | 176000 | 2.2913 | |
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| 2.4165 | 0.52 | 184000 | 2.2974 | |
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| 2.4131 | 0.54 | 192000 | 2.2906 | |
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| 2.4131 | 0.56 | 200000 | 2.2908 | |
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| 2.407 | 0.58 | 208000 | 2.2895 | |
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| 2.407 | 0.61 | 216000 | 2.2865 | |
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| 2.4153 | 0.63 | 224000 | 2.2914 | |
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| 2.4153 | 0.65 | 232000 | 2.2806 | |
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| 2.4011 | 0.67 | 240000 | 2.2819 | |
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| 2.4011 | 0.7 | 248000 | 2.2854 | |
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| 2.4087 | 0.72 | 256000 | 2.2837 | |
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| 2.4087 | 0.74 | 264000 | 2.2866 | |
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| 2.4059 | 0.76 | 272000 | 2.2855 | |
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| 2.4059 | 0.79 | 280000 | 2.2868 | |
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| 2.4086 | 0.81 | 288000 | 2.2770 | |
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| 2.4086 | 0.83 | 296000 | 2.2789 | |
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| 2.4093 | 0.85 | 304000 | 2.2792 | |
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| 2.4093 | 0.88 | 312000 | 2.2797 | |
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| 2.4036 | 0.9 | 320000 | 2.2794 | |
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| 2.4036 | 0.92 | 328000 | 2.2768 | |
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| 2.4063 | 0.94 | 336000 | 2.2836 | |
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| 2.4063 | 0.97 | 344000 | 2.2809 | |
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| 2.4047 | 0.99 | 352000 | 2.2808 | |
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| 2.4047 | 1.01 | 360000 | 2.2840 | |
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| 2.4084 | 1.03 | 368000 | 2.2799 | |
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| 2.4084 | 1.06 | 376000 | 2.2726 | |
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| 2.4041 | 1.08 | 384000 | 2.2824 | |
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| 2.4041 | 1.1 | 392000 | 2.2781 | |
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| 2.4034 | 1.12 | 400000 | 2.2751 | |
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| 2.4034 | 1.15 | 408000 | 2.2761 | |
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| 2.3951 | 1.17 | 416000 | 2.2732 | |
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| 2.3951 | 1.19 | 424000 | 2.2710 | |
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| 2.409 | 1.21 | 432000 | 2.2780 | |
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| 2.409 | 1.24 | 440000 | 2.2715 | |
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| 2.3985 | 1.26 | 448000 | 2.2790 | |
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| 2.3985 | 1.28 | 456000 | 2.2766 | |
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| 2.4016 | 1.3 | 464000 | 2.2745 | |
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| 2.4016 | 1.32 | 472000 | 2.2719 | |
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| 2.3978 | 1.35 | 480000 | 2.2755 | |
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| 2.3978 | 1.37 | 488000 | 2.2699 | |
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| 2.406 | 1.39 | 496000 | 2.2823 | |
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| 2.406 | 1.41 | 504000 | 2.2736 | |
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| 2.3958 | 1.44 | 512000 | 2.2728 | |
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| 2.3958 | 1.46 | 520000 | 2.2763 | |
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| 2.406 | 1.48 | 528000 | 2.2781 | |
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| 2.406 | 1.5 | 536000 | 2.2723 | |
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| 2.4 | 1.53 | 544000 | 2.2733 | |
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| 2.4 | 1.55 | 552000 | 2.2715 | |
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| 2.3998 | 1.57 | 560000 | 2.2716 | |
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| 2.3998 | 1.59 | 568000 | 2.2751 | |
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| 2.4017 | 1.62 | 576000 | 2.2743 | |
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| 2.4017 | 1.64 | 584000 | 2.2739 | |
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| 2.4019 | 1.66 | 592000 | 2.2755 | |
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| 2.4019 | 1.68 | 600000 | 2.2691 | |
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| 2.398 | 1.71 | 608000 | 2.2706 | |
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| 2.398 | 1.73 | 616000 | 2.2703 | |
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| 2.4027 | 1.75 | 624000 | 2.2657 | |
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| 2.4027 | 1.77 | 632000 | 2.2674 | |
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| 2.4 | 1.8 | 640000 | 2.2749 | |
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| 2.4 | 1.82 | 648000 | 2.2714 | |
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| 2.4046 | 1.84 | 656000 | 2.2695 | |
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| 2.4046 | 1.86 | 664000 | 2.2724 | |
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| 2.4033 | 1.89 | 672000 | 2.2697 | |
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| 2.4033 | 1.91 | 680000 | 2.2697 | |
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| 2.3981 | 1.93 | 688000 | 2.2674 | |
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| 2.3981 | 1.95 | 696000 | 2.2669 | |
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| 2.4029 | 1.98 | 704000 | 2.2755 | |
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| 2.4029 | 2.0 | 712000 | 2.2664 | |
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| 2.4046 | 2.02 | 720000 | 2.2759 | |
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| 2.4046 | 2.04 | 728000 | 2.2689 | |
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| 2.4056 | 2.07 | 736000 | 2.2710 | |
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| 2.4056 | 2.09 | 744000 | 2.2744 | |
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| 2.4036 | 2.11 | 752000 | 2.2653 | |
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| 2.4036 | 2.13 | 760000 | 2.2642 | |
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| 2.3961 | 2.16 | 768000 | 2.2703 | |
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| 2.3961 | 2.18 | 776000 | 2.2683 | |
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| 2.3939 | 2.2 | 784000 | 2.2746 | |
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| 2.3939 | 2.22 | 792000 | 2.2667 | |
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| 2.3998 | 2.25 | 800000 | 2.2690 | |
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| 2.3998 | 2.27 | 808000 | 2.2697 | |
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| 2.3921 | 2.29 | 816000 | 2.2681 | |
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| 2.3921 | 2.31 | 824000 | 2.2740 | |
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| 2.4011 | 2.34 | 832000 | 2.2704 | |
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| 2.4011 | 2.36 | 840000 | 2.2666 | |
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| 2.3948 | 2.38 | 848000 | 2.2689 | |
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| 2.3948 | 2.4 | 856000 | 2.2742 | |
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| 2.3957 | 2.43 | 864000 | 2.2755 | |
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| 2.3957 | 2.45 | 872000 | 2.2689 | |
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| 2.3971 | 2.47 | 880000 | 2.2717 | |
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| 2.3971 | 2.49 | 888000 | 2.2690 | |
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| 2.3982 | 2.52 | 896000 | 2.2645 | |
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| 2.3982 | 2.54 | 904000 | 2.2726 | |
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| 2.4005 | 2.56 | 912000 | 2.2628 | |
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| 2.4005 | 2.58 | 920000 | 2.2726 | |
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| 2.4037 | 2.6 | 928000 | 2.2760 | |
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| 2.4037 | 2.63 | 936000 | 2.2662 | |
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| 2.4031 | 2.65 | 944000 | 2.2729 | |
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| 2.4031 | 2.67 | 952000 | 2.2706 | |
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| 2.4025 | 2.69 | 960000 | 2.2684 | |
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| 2.4025 | 2.72 | 968000 | 2.2635 | |
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| 2.409 | 2.74 | 976000 | 2.2606 | |
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| 2.409 | 2.76 | 984000 | 2.2664 | |
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| 2.4085 | 2.78 | 992000 | 2.2647 | |
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| 2.4085 | 2.81 | 1000000 | 2.2656 | |
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| 2.3971 | 2.83 | 1008000 | 2.2655 | |
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| 2.3971 | 2.85 | 1016000 | 2.2681 | |
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| 2.3946 | 2.87 | 1024000 | 2.2671 | |
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| 2.3946 | 2.9 | 1032000 | 2.2660 | |
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| 2.4063 | 2.92 | 1040000 | 2.2697 | |
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| 2.4063 | 2.94 | 1048000 | 2.2706 | |
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| 2.399 | 2.96 | 1056000 | 2.2625 | |
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| 2.399 | 2.99 | 1064000 | 2.2699 | |
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| 2.4024 | 3.01 | 1072000 | 2.2622 | |
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| 2.4024 | 3.03 | 1080000 | 2.2695 | |
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| 2.4035 | 3.05 | 1088000 | 2.2700 | |
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| 2.4035 | 3.08 | 1096000 | 2.2624 | |
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| 2.4061 | 3.1 | 1104000 | 2.2690 | |
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| 2.4061 | 3.12 | 1112000 | 2.2653 | |
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| 2.4044 | 3.14 | 1120000 | 2.2679 | |
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| 2.4044 | 3.17 | 1128000 | 2.2658 | |
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| 2.3996 | 3.19 | 1136000 | 2.2680 | |
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| 2.3996 | 3.21 | 1144000 | 2.2668 | |
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| 2.3943 | 3.23 | 1152000 | 2.2689 | |
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| 2.3943 | 3.26 | 1160000 | 2.2702 | |
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| 2.3948 | 3.28 | 1168000 | 2.2653 | |
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| 2.3948 | 3.3 | 1176000 | 2.2621 | |
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| 2.4047 | 3.32 | 1184000 | 2.2723 | |
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| 2.4047 | 3.35 | 1192000 | 2.2718 | |
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| 2.4057 | 3.37 | 1200000 | 2.2668 | |
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| 2.4057 | 3.39 | 1208000 | 2.2649 | |
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| 2.3901 | 3.41 | 1216000 | 2.2699 | |
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| 2.3901 | 3.44 | 1224000 | 2.2683 | |
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| 2.3942 | 3.46 | 1232000 | 2.2679 | |
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| 2.3942 | 3.48 | 1240000 | 2.2647 | |
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| 2.4052 | 3.5 | 1248000 | 2.2656 | |
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| 2.4052 | 3.53 | 1256000 | 2.2679 | |
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| 2.401 | 3.55 | 1264000 | 2.2685 | |
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| 2.401 | 3.57 | 1272000 | 2.2654 | |
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| 2.4012 | 3.59 | 1280000 | 2.2607 | |
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| 2.4012 | 3.62 | 1288000 | 2.2668 | |
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| 2.4015 | 3.64 | 1296000 | 2.2672 | |
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| 2.4015 | 3.66 | 1304000 | 2.2685 | |
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| 2.4039 | 3.68 | 1312000 | 2.2675 | |
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| 2.4039 | 3.71 | 1320000 | 2.2702 | |
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| 2.3927 | 3.73 | 1328000 | 2.2689 | |
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| 2.3927 | 3.75 | 1336000 | 2.2674 | |
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| 2.3998 | 3.77 | 1344000 | 2.2694 | |
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| 2.3998 | 3.8 | 1352000 | 2.2649 | |
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| 2.404 | 3.82 | 1360000 | 2.2635 | |
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| 2.404 | 3.84 | 1368000 | 2.2681 | |
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| 2.4023 | 3.86 | 1376000 | 2.2601 | |
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| 2.4023 | 3.88 | 1384000 | 2.2661 | |
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| 2.393 | 3.91 | 1392000 | 2.2613 | |
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| 2.393 | 3.93 | 1400000 | 2.2717 | |
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| 2.402 | 3.95 | 1408000 | 2.2672 | |
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| 2.402 | 3.97 | 1416000 | 2.2637 | |
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| 2.4047 | 4.0 | 1424000 | 2.2705 | |
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| 2.4047 | 4.02 | 1432000 | 2.2682 | |
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| 2.4045 | 4.04 | 1440000 | 2.2630 | |
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| 2.4045 | 4.06 | 1448000 | 2.2699 | |
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| 2.3973 | 4.09 | 1456000 | 2.2579 | |
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| 2.3973 | 4.11 | 1464000 | 2.2601 | |
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| 2.399 | 4.13 | 1472000 | 2.2609 | |
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| 2.399 | 4.15 | 1480000 | 2.2697 | |
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| 2.399 | 4.18 | 1488000 | 2.2630 | |
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| 2.399 | 4.2 | 1496000 | 2.2658 | |
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| 2.3995 | 4.22 | 1504000 | 2.2656 | |
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| 2.3995 | 4.24 | 1512000 | 2.2689 | |
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| 2.3929 | 4.27 | 1520000 | 2.2678 | |
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| 2.3929 | 4.29 | 1528000 | 2.2694 | |
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| 2.404 | 4.31 | 1536000 | 2.2632 | |
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| 2.404 | 4.33 | 1544000 | 2.2657 | |
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| 2.3932 | 4.36 | 1552000 | 2.2642 | |
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| 2.3932 | 4.38 | 1560000 | 2.2607 | |
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| 2.3985 | 4.4 | 1568000 | 2.2635 | |
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| 2.3985 | 4.42 | 1576000 | 2.2645 | |
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| 2.3997 | 4.45 | 1584000 | 2.2654 | |
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| 2.3997 | 4.47 | 1592000 | 2.2672 | |
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| 2.396 | 4.49 | 1600000 | 2.2666 | |
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| 2.396 | 4.51 | 1608000 | 2.2708 | |
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| 2.4012 | 4.54 | 1616000 | 2.2707 | |
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| 2.4012 | 4.56 | 1624000 | 2.2684 | |
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| 2.4074 | 4.58 | 1632000 | 2.2676 | |
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| 2.4074 | 4.6 | 1640000 | 2.2658 | |
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| 2.3965 | 4.63 | 1648000 | 2.2716 | |
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| 2.3965 | 4.65 | 1656000 | 2.2656 | |
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| 2.4021 | 4.67 | 1664000 | 2.2690 | |
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| 2.4021 | 4.69 | 1672000 | 2.2656 | |
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| 2.3981 | 4.72 | 1680000 | 2.2659 | |
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| 2.3981 | 4.74 | 1688000 | 2.2667 | |
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| 2.3974 | 4.76 | 1696000 | 2.2655 | |
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| 2.3974 | 4.78 | 1704000 | 2.2676 | |
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| 2.3964 | 4.81 | 1712000 | 2.2655 | |
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| 2.3964 | 4.83 | 1720000 | 2.2636 | |
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| 2.3933 | 4.85 | 1728000 | 2.2679 | |
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| 2.3933 | 4.87 | 1736000 | 2.2667 | |
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| 2.4066 | 4.9 | 1744000 | 2.2647 | |
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| 2.4066 | 4.92 | 1752000 | 2.2657 | |
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| 2.4027 | 4.94 | 1760000 | 2.2628 | |
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| 2.4027 | 4.96 | 1768000 | 2.2642 | |
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| 2.4029 | 4.99 | 1776000 | 2.2677 | |
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| 2.4029 | 5.01 | 1784000 | 2.2704 | |
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| 2.3958 | 5.03 | 1792000 | 2.2650 | |
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| 2.3958 | 5.05 | 1800000 | 2.2650 | |
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| 2.4054 | 5.08 | 1808000 | 2.2680 | |
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| 2.4054 | 5.1 | 1816000 | 2.2601 | |
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| 2.3984 | 5.12 | 1824000 | 2.2671 | |
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| 2.3984 | 5.14 | 1832000 | 2.2639 | |
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| 2.4005 | 5.16 | 1840000 | 2.2629 | |
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| 2.4005 | 5.19 | 1848000 | 2.2656 | |
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| 2.3962 | 5.21 | 1856000 | 2.2646 | |
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| 2.3962 | 5.23 | 1864000 | 2.2571 | |
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| 2.4033 | 5.25 | 1872000 | 2.2689 | |
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| 2.4033 | 5.28 | 1880000 | 2.2632 | |
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| 2.4064 | 5.3 | 1888000 | 2.2633 | |
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| 2.4064 | 5.32 | 1896000 | 2.2694 | |
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| 2.3967 | 5.34 | 1904000 | 2.2685 | |
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| 2.3967 | 5.37 | 1912000 | 2.2636 | |
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| 2.4002 | 5.39 | 1920000 | 2.2687 | |
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| 2.4002 | 5.41 | 1928000 | 2.2632 | |
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| 2.4045 | 5.43 | 1936000 | 2.2625 | |
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| 2.4045 | 5.46 | 1944000 | 2.2677 | |
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| 2.4096 | 5.48 | 1952000 | 2.2563 | |
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| 2.4096 | 5.5 | 1960000 | 2.2642 | |
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| 2.4004 | 5.52 | 1968000 | 2.2692 | |
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| 2.4004 | 5.55 | 1976000 | 2.2696 | |
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| 2.4065 | 5.57 | 1984000 | 2.2579 | |
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| 2.4065 | 5.59 | 1992000 | 2.2660 | |
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| 2.4025 | 5.61 | 2000000 | 2.2654 | |
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| 2.4025 | 5.64 | 2008000 | 2.2706 | |
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| 2.3993 | 5.66 | 2016000 | 2.2704 | |
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| 2.3993 | 5.68 | 2024000 | 2.2664 | |
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| 2.4034 | 5.7 | 2032000 | 2.2659 | |
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| 2.4034 | 5.73 | 2040000 | 2.2680 | |
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| 2.4004 | 5.75 | 2048000 | 2.2611 | |
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| 2.4004 | 5.77 | 2056000 | 2.2646 | |
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| 2.4025 | 5.79 | 2064000 | 2.2682 | |
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| 2.4025 | 5.82 | 2072000 | 2.2646 | |
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| 2.4063 | 5.84 | 2080000 | 2.2598 | |
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| 2.4063 | 5.86 | 2088000 | 2.2673 | |
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| 2.4071 | 5.88 | 2096000 | 2.2646 | |
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| 2.4071 | 5.91 | 2104000 | 2.2672 | |
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| 2.401 | 5.93 | 2112000 | 2.2648 | |
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| 2.401 | 5.95 | 2120000 | 2.2654 | |
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| 2.402 | 5.97 | 2128000 | 2.2664 | |
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| 2.402 | 6.0 | 2136000 | 2.2683 | |
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| 2.4004 | 6.02 | 2144000 | 2.2618 | |
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| 2.4004 | 6.04 | 2152000 | 2.2669 | |
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| 2.4001 | 6.06 | 2160000 | 2.2630 | |
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| 2.4001 | 6.09 | 2168000 | 2.2632 | |
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| 2.4046 | 6.11 | 2176000 | 2.2696 | |
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| 2.4046 | 6.13 | 2184000 | 2.2641 | |
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| 2.405 | 6.15 | 2192000 | 2.2627 | |
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| 2.405 | 6.18 | 2200000 | 2.2681 | |
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| 2.4063 | 6.2 | 2208000 | 2.2604 | |
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| 2.4063 | 6.22 | 2216000 | 2.2715 | |
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| 2.3991 | 6.24 | 2224000 | 2.2683 | |
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| 2.3991 | 6.27 | 2232000 | 2.2657 | |
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| 2.405 | 6.29 | 2240000 | 2.2645 | |
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| 2.405 | 6.31 | 2248000 | 2.2676 | |
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| 2.3941 | 6.33 | 2256000 | 2.2706 | |
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| 2.3941 | 6.36 | 2264000 | 2.2593 | |
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| 2.4041 | 6.38 | 2272000 | 2.2679 | |
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| 2.4041 | 6.4 | 2280000 | 2.2643 | |
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| 2.4001 | 6.42 | 2288000 | 2.2728 | |
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| 2.4001 | 6.44 | 2296000 | 2.2631 | |
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| 2.3983 | 6.47 | 2304000 | 2.2636 | |
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| 2.3983 | 6.49 | 2312000 | 2.2630 | |
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| 2.4003 | 6.51 | 2320000 | 2.2663 | |
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| 2.4003 | 6.53 | 2328000 | 2.2647 | |
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| 2.3981 | 6.56 | 2336000 | 2.2669 | |
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| 2.3981 | 6.58 | 2344000 | 2.2660 | |
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| 2.3951 | 6.6 | 2352000 | 2.2692 | |
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| 2.3951 | 6.62 | 2360000 | 2.2644 | |
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| 2.4013 | 6.65 | 2368000 | 2.2610 | |
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| 2.4013 | 6.67 | 2376000 | 2.2655 | |
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| 2.4 | 6.69 | 2384000 | 2.2592 | |
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| 2.4 | 6.71 | 2392000 | 2.2666 | |
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| 2.3975 | 6.74 | 2400000 | 2.2685 | |
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### Framework versions |
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- Transformers 4.35.0.dev0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.0 |
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