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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-Q4-75p-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-Q4-75p-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: 2.2679 |
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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 | 0.07 | 8000 | 2.5786 | |
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| 2.8197 | 0.13 | 16000 | 2.4788 | |
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| 2.8197 | 0.2 | 24000 | 2.4343 | |
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| 2.5564 | 0.27 | 32000 | 2.4143 | |
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| 2.5564 | 0.34 | 40000 | 2.3826 | |
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| 2.4967 | 0.4 | 48000 | 2.3655 | |
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| 2.4967 | 0.47 | 56000 | 2.3450 | |
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| 2.476 | 0.54 | 64000 | 2.3501 | |
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| 2.476 | 0.61 | 72000 | 2.3315 | |
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| 2.4525 | 0.67 | 80000 | 2.3286 | |
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| 2.4525 | 0.74 | 88000 | 2.3278 | |
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| 2.445 | 0.81 | 96000 | 2.3187 | |
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| 2.445 | 0.88 | 104000 | 2.3113 | |
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| 2.438 | 0.94 | 112000 | 2.3129 | |
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| 2.438 | 1.01 | 120000 | 2.3161 | |
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| 2.4233 | 1.08 | 128000 | 2.3009 | |
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| 2.4233 | 1.15 | 136000 | 2.3072 | |
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| 2.4182 | 1.21 | 144000 | 2.3069 | |
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| 2.4182 | 1.28 | 152000 | 2.3060 | |
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| 2.418 | 1.35 | 160000 | 2.2963 | |
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| 2.418 | 1.41 | 168000 | 2.3017 | |
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| 2.4106 | 1.48 | 176000 | 2.2863 | |
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| 2.4106 | 1.55 | 184000 | 2.2871 | |
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| 2.4093 | 1.62 | 192000 | 2.2870 | |
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| 2.4093 | 1.68 | 200000 | 2.2845 | |
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| 2.4124 | 1.75 | 208000 | 2.2971 | |
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| 2.4124 | 1.82 | 216000 | 2.2833 | |
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| 2.4031 | 1.89 | 224000 | 2.2866 | |
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| 2.4031 | 1.95 | 232000 | 2.2833 | |
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| 2.4056 | 2.02 | 240000 | 2.2877 | |
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| 2.4056 | 2.09 | 248000 | 2.2789 | |
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| 2.4035 | 2.16 | 256000 | 2.2872 | |
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| 2.4035 | 2.22 | 264000 | 2.2771 | |
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| 2.4068 | 2.29 | 272000 | 2.2824 | |
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| 2.4068 | 2.36 | 280000 | 2.2681 | |
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| 2.4069 | 2.43 | 288000 | 2.2866 | |
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| 2.4069 | 2.49 | 296000 | 2.2838 | |
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| 2.4059 | 2.56 | 304000 | 2.2804 | |
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| 2.4059 | 2.63 | 312000 | 2.2757 | |
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| 2.3997 | 2.69 | 320000 | 2.2775 | |
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| 2.3997 | 2.76 | 328000 | 2.2693 | |
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| 2.4025 | 2.83 | 336000 | 2.2751 | |
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| 2.4025 | 2.9 | 344000 | 2.2686 | |
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| 2.399 | 2.96 | 352000 | 2.2784 | |
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| 2.399 | 3.03 | 360000 | 2.2782 | |
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| 2.3953 | 3.1 | 368000 | 2.2694 | |
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| 2.3953 | 3.17 | 376000 | 2.2638 | |
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| 2.4002 | 3.23 | 384000 | 2.2785 | |
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| 2.4002 | 3.3 | 392000 | 2.2785 | |
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| 2.4035 | 3.37 | 400000 | 2.2774 | |
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| 2.4035 | 3.44 | 408000 | 2.2736 | |
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| 2.3985 | 3.5 | 416000 | 2.2808 | |
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| 2.3985 | 3.57 | 424000 | 2.2672 | |
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| 2.3996 | 3.64 | 432000 | 2.2765 | |
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| 2.3996 | 3.71 | 440000 | 2.2748 | |
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| 2.4052 | 3.77 | 448000 | 2.2647 | |
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| 2.4052 | 3.84 | 456000 | 2.2776 | |
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| 2.4025 | 3.91 | 464000 | 2.2734 | |
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| 2.4025 | 3.97 | 472000 | 2.2588 | |
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| 2.4082 | 4.04 | 480000 | 2.2724 | |
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| 2.4082 | 4.11 | 488000 | 2.2740 | |
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| 2.3993 | 4.18 | 496000 | 2.2726 | |
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| 2.3993 | 4.24 | 504000 | 2.2827 | |
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| 2.4029 | 4.31 | 512000 | 2.2728 | |
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| 2.4029 | 4.38 | 520000 | 2.2833 | |
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| 2.407 | 4.45 | 528000 | 2.2636 | |
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| 2.407 | 4.51 | 536000 | 2.2689 | |
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| 2.4039 | 4.58 | 544000 | 2.2741 | |
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| 2.4039 | 4.65 | 552000 | 2.2715 | |
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| 2.3983 | 4.72 | 560000 | 2.2805 | |
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| 2.3983 | 4.78 | 568000 | 2.2744 | |
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| 2.3974 | 4.85 | 576000 | 2.2678 | |
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| 2.3974 | 4.92 | 584000 | 2.2723 | |
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| 2.388 | 4.99 | 592000 | 2.2655 | |
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| 2.388 | 5.05 | 600000 | 2.2716 | |
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| 2.3921 | 5.12 | 608000 | 2.2771 | |
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| 2.3921 | 5.19 | 616000 | 2.2760 | |
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| 2.3963 | 5.25 | 624000 | 2.2806 | |
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| 2.3963 | 5.32 | 632000 | 2.2697 | |
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| 2.3891 | 5.39 | 640000 | 2.2705 | |
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| 2.3891 | 5.46 | 648000 | 2.2708 | |
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| 2.3968 | 5.52 | 656000 | 2.2689 | |
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| 2.3968 | 5.59 | 664000 | 2.2651 | |
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| 2.3951 | 5.66 | 672000 | 2.2766 | |
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| 2.3951 | 5.73 | 680000 | 2.2717 | |
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| 2.3986 | 5.79 | 688000 | 2.2629 | |
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| 2.3986 | 5.86 | 696000 | 2.2624 | |
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| 2.3985 | 5.93 | 704000 | 2.2693 | |
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| 2.3985 | 6.0 | 712000 | 2.2632 | |
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| 2.4009 | 6.06 | 720000 | 2.2715 | |
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| 2.4009 | 6.13 | 728000 | 2.2654 | |
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| 2.4015 | 6.2 | 736000 | 2.2700 | |
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| 2.4015 | 6.27 | 744000 | 2.2673 | |
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| 2.3927 | 6.33 | 752000 | 2.2701 | |
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| 2.3927 | 6.4 | 760000 | 2.2666 | |
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| 2.3941 | 6.47 | 768000 | 2.2585 | |
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| 2.3941 | 6.53 | 776000 | 2.2679 | |
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| 2.393 | 6.6 | 784000 | 2.2624 | |
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| 2.393 | 6.67 | 792000 | 2.2706 | |
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| 2.4025 | 6.74 | 800000 | 2.2785 | |
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| 2.4025 | 6.8 | 808000 | 2.2658 | |
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| 2.3992 | 6.87 | 816000 | 2.2557 | |
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| 2.3992 | 6.94 | 824000 | 2.2581 | |
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| 2.4055 | 7.01 | 832000 | 2.2725 | |
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| 2.4055 | 7.07 | 840000 | 2.2608 | |
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| 2.3965 | 7.14 | 848000 | 2.2717 | |
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| 2.3965 | 7.21 | 856000 | 2.2643 | |
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| 2.4028 | 7.28 | 864000 | 2.2697 | |
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| 2.4028 | 7.34 | 872000 | 2.2691 | |
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| 2.3943 | 7.41 | 880000 | 2.2628 | |
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| 2.3943 | 7.48 | 888000 | 2.2630 | |
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| 2.3918 | 7.55 | 896000 | 2.2691 | |
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| 2.3918 | 7.61 | 904000 | 2.2778 | |
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| 2.3897 | 7.68 | 912000 | 2.2577 | |
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| 2.3897 | 7.75 | 920000 | 2.2690 | |
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| 2.3996 | 7.81 | 928000 | 2.2631 | |
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| 2.3996 | 7.88 | 936000 | 2.2606 | |
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| 2.4016 | 7.95 | 944000 | 2.2742 | |
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| 2.4016 | 8.02 | 952000 | 2.2705 | |
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| 2.3989 | 8.08 | 960000 | 2.2694 | |
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| 2.3989 | 8.15 | 968000 | 2.2676 | |
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| 2.3989 | 8.22 | 976000 | 2.2659 | |
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| 2.3989 | 8.29 | 984000 | 2.2676 | |
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| 2.3995 | 8.35 | 992000 | 2.2752 | |
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| 2.3995 | 8.42 | 1000000 | 2.2760 | |
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| 2.3958 | 8.49 | 1008000 | 2.2779 | |
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| 2.3958 | 8.56 | 1016000 | 2.2626 | |
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| 2.3962 | 8.62 | 1024000 | 2.2646 | |
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| 2.3962 | 8.69 | 1032000 | 2.2645 | |
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| 2.3966 | 8.76 | 1040000 | 2.2603 | |
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| 2.3966 | 8.83 | 1048000 | 2.2549 | |
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| 2.3934 | 8.89 | 1056000 | 2.2669 | |
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| 2.3934 | 8.96 | 1064000 | 2.2576 | |
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| 2.3918 | 9.03 | 1072000 | 2.2707 | |
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| 2.3918 | 9.09 | 1080000 | 2.2618 | |
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| 2.401 | 9.16 | 1088000 | 2.2680 | |
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| 2.401 | 9.23 | 1096000 | 2.2721 | |
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| 2.3938 | 9.3 | 1104000 | 2.2637 | |
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| 2.3938 | 9.36 | 1112000 | 2.2657 | |
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| 2.3982 | 9.43 | 1120000 | 2.2576 | |
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| 2.3982 | 9.5 | 1128000 | 2.2633 | |
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| 2.4006 | 9.57 | 1136000 | 2.2668 | |
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| 2.4006 | 9.63 | 1144000 | 2.2660 | |
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| 2.3971 | 9.7 | 1152000 | 2.2659 | |
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| 2.3971 | 9.77 | 1160000 | 2.2723 | |
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| 2.4004 | 9.84 | 1168000 | 2.2627 | |
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| 2.4004 | 9.9 | 1176000 | 2.2708 | |
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| 2.3903 | 9.97 | 1184000 | 2.2576 | |
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| 2.3903 | 10.04 | 1192000 | 2.2625 | |
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| 2.3909 | 10.11 | 1200000 | 2.2543 | |
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| 2.3909 | 10.17 | 1208000 | 2.2595 | |
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| 2.4004 | 10.24 | 1216000 | 2.2561 | |
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| 2.4004 | 10.31 | 1224000 | 2.2607 | |
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| 2.3964 | 10.37 | 1232000 | 2.2606 | |
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| 2.3964 | 10.44 | 1240000 | 2.2635 | |
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| 2.4007 | 10.51 | 1248000 | 2.2623 | |
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| 2.4007 | 10.58 | 1256000 | 2.2696 | |
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| 2.3993 | 10.64 | 1264000 | 2.2700 | |
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| 2.3993 | 10.71 | 1272000 | 2.2731 | |
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| 2.4048 | 10.78 | 1280000 | 2.2701 | |
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| 2.4048 | 10.85 | 1288000 | 2.2701 | |
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| 2.3936 | 10.91 | 1296000 | 2.2706 | |
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| 2.3936 | 10.98 | 1304000 | 2.2596 | |
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| 2.3951 | 11.05 | 1312000 | 2.2812 | |
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| 2.3951 | 11.12 | 1320000 | 2.2523 | |
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| 2.39 | 11.18 | 1328000 | 2.2596 | |
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| 2.39 | 11.25 | 1336000 | 2.2723 | |
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| 2.393 | 11.32 | 1344000 | 2.2696 | |
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| 2.393 | 11.39 | 1352000 | 2.2614 | |
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| 2.3915 | 11.45 | 1360000 | 2.2687 | |
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| 2.3915 | 11.52 | 1368000 | 2.2567 | |
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| 2.405 | 11.59 | 1376000 | 2.2717 | |
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| 2.405 | 11.65 | 1384000 | 2.2733 | |
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| 2.3898 | 11.72 | 1392000 | 2.2680 | |
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| 2.3898 | 11.79 | 1400000 | 2.2627 | |
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| 2.3956 | 11.86 | 1408000 | 2.2689 | |
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| 2.3956 | 11.92 | 1416000 | 2.2669 | |
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| 2.4041 | 11.99 | 1424000 | 2.2610 | |
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| 2.4041 | 12.06 | 1432000 | 2.2689 | |
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| 2.3968 | 12.13 | 1440000 | 2.2749 | |
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| 2.3968 | 12.19 | 1448000 | 2.2640 | |
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| 2.4048 | 12.26 | 1456000 | 2.2602 | |
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| 2.4048 | 12.33 | 1464000 | 2.2698 | |
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| 2.4025 | 12.4 | 1472000 | 2.2545 | |
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| 2.4025 | 12.46 | 1480000 | 2.2685 | |
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| 2.3977 | 12.53 | 1488000 | 2.2623 | |
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| 2.3977 | 12.6 | 1496000 | 2.2679 | |
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| 2.3965 | 12.67 | 1504000 | 2.2505 | |
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| 2.3965 | 12.73 | 1512000 | 2.2708 | |
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| 2.3945 | 12.8 | 1520000 | 2.2655 | |
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| 2.3945 | 12.87 | 1528000 | 2.2672 | |
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| 2.3957 | 12.93 | 1536000 | 2.2698 | |
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| 2.3957 | 13.0 | 1544000 | 2.2661 | |
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| 2.3951 | 13.07 | 1552000 | 2.2635 | |
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| 2.3951 | 13.14 | 1560000 | 2.2597 | |
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| 2.4005 | 13.2 | 1568000 | 2.2575 | |
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| 2.4005 | 13.27 | 1576000 | 2.2648 | |
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| 2.394 | 13.34 | 1584000 | 2.2746 | |
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| 2.394 | 13.41 | 1592000 | 2.2722 | |
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| 2.4016 | 13.47 | 1600000 | 2.2567 | |
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| 2.4016 | 13.54 | 1608000 | 2.2599 | |
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| 2.392 | 13.61 | 1616000 | 2.2588 | |
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| 2.392 | 13.68 | 1624000 | 2.2644 | |
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| 2.3936 | 13.74 | 1632000 | 2.2668 | |
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| 2.3936 | 13.81 | 1640000 | 2.2447 | |
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| 2.3954 | 13.88 | 1648000 | 2.2502 | |
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| 2.3954 | 13.95 | 1656000 | 2.2737 | |
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| 2.3901 | 14.01 | 1664000 | 2.2701 | |
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| 2.3901 | 14.08 | 1672000 | 2.2632 | |
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| 2.3963 | 14.15 | 1680000 | 2.2661 | |
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| 2.3963 | 14.21 | 1688000 | 2.2628 | |
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| 2.4005 | 14.28 | 1696000 | 2.2606 | |
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| 2.4005 | 14.35 | 1704000 | 2.2578 | |
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| 2.3877 | 14.42 | 1712000 | 2.2674 | |
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| 2.3877 | 14.48 | 1720000 | 2.2631 | |
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| 2.3958 | 14.55 | 1728000 | 2.2675 | |
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| 2.3958 | 14.62 | 1736000 | 2.2752 | |
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| 2.3858 | 14.69 | 1744000 | 2.2623 | |
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| 2.3858 | 14.75 | 1752000 | 2.2577 | |
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| 2.403 | 14.82 | 1760000 | 2.2512 | |
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| 2.403 | 14.89 | 1768000 | 2.2610 | |
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| 2.3969 | 14.96 | 1776000 | 2.2597 | |
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| 2.3969 | 15.02 | 1784000 | 2.2748 | |
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| 2.4016 | 15.09 | 1792000 | 2.2632 | |
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| 2.4016 | 15.16 | 1800000 | 2.2650 | |
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| 2.4018 | 15.23 | 1808000 | 2.2669 | |
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| 2.4018 | 15.29 | 1816000 | 2.2525 | |
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| 2.3954 | 15.36 | 1824000 | 2.2497 | |
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| 2.3954 | 15.43 | 1832000 | 2.2744 | |
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| 2.396 | 15.49 | 1840000 | 2.2673 | |
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| 2.396 | 15.56 | 1848000 | 2.2637 | |
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| 2.3951 | 15.63 | 1856000 | 2.2615 | |
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| 2.3951 | 15.7 | 1864000 | 2.2644 | |
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| 2.4017 | 15.76 | 1872000 | 2.2656 | |
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| 2.4017 | 15.83 | 1880000 | 2.2682 | |
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| 2.3962 | 15.9 | 1888000 | 2.2592 | |
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| 2.3962 | 15.97 | 1896000 | 2.2643 | |
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| 2.3996 | 16.03 | 1904000 | 2.2648 | |
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| 2.3996 | 16.1 | 1912000 | 2.2706 | |
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| 2.3994 | 16.17 | 1920000 | 2.2700 | |
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| 2.3994 | 16.24 | 1928000 | 2.2627 | |
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| 2.3976 | 16.3 | 1936000 | 2.2592 | |
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| 2.3976 | 16.37 | 1944000 | 2.2606 | |
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| 2.3971 | 16.44 | 1952000 | 2.2588 | |
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| 2.3971 | 16.51 | 1960000 | 2.2607 | |
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| 2.3991 | 16.57 | 1968000 | 2.2692 | |
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| 2.3991 | 16.64 | 1976000 | 2.2548 | |
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| 2.3952 | 16.71 | 1984000 | 2.2572 | |
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| 2.3952 | 16.77 | 1992000 | 2.2626 | |
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| 2.4002 | 16.84 | 2000000 | 2.2680 | |
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| 2.4002 | 16.91 | 2008000 | 2.2690 | |
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| 2.3937 | 16.98 | 2016000 | 2.2523 | |
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| 2.3937 | 17.04 | 2024000 | 2.2700 | |
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| 2.3999 | 17.11 | 2032000 | 2.2652 | |
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| 2.3999 | 17.18 | 2040000 | 2.2671 | |
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| 2.3891 | 17.25 | 2048000 | 2.2700 | |
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| 2.3891 | 17.31 | 2056000 | 2.2589 | |
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| 2.397 | 17.38 | 2064000 | 2.2626 | |
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| 2.397 | 17.45 | 2072000 | 2.2607 | |
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| 2.3968 | 17.52 | 2080000 | 2.2663 | |
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| 2.3968 | 17.58 | 2088000 | 2.2637 | |
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| 2.3932 | 17.65 | 2096000 | 2.2623 | |
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| 2.3932 | 17.72 | 2104000 | 2.2673 | |
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| 2.3981 | 17.79 | 2112000 | 2.2547 | |
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| 2.3981 | 17.85 | 2120000 | 2.2598 | |
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| 2.3964 | 17.92 | 2128000 | 2.2690 | |
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| 2.3964 | 17.99 | 2136000 | 2.2619 | |
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| 2.3941 | 18.05 | 2144000 | 2.2558 | |
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| 2.3941 | 18.12 | 2152000 | 2.2659 | |
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| 2.3926 | 18.19 | 2160000 | 2.2552 | |
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| 2.3926 | 18.26 | 2168000 | 2.2671 | |
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| 2.399 | 18.32 | 2176000 | 2.2661 | |
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| 2.399 | 18.39 | 2184000 | 2.2591 | |
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| 2.3941 | 18.46 | 2192000 | 2.2568 | |
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| 2.3941 | 18.53 | 2200000 | 2.2588 | |
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| 2.3975 | 18.59 | 2208000 | 2.2631 | |
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| 2.3975 | 18.66 | 2216000 | 2.2655 | |
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| 2.3884 | 18.73 | 2224000 | 2.2628 | |
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| 2.3884 | 18.8 | 2232000 | 2.2656 | |
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| 2.399 | 18.86 | 2240000 | 2.2644 | |
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| 2.399 | 18.93 | 2248000 | 2.2608 | |
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| 2.4064 | 19.0 | 2256000 | 2.2561 | |
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| 2.4064 | 19.07 | 2264000 | 2.2680 | |
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| 2.3999 | 19.13 | 2272000 | 2.2703 | |
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| 2.3999 | 19.2 | 2280000 | 2.2624 | |
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| 2.398 | 19.27 | 2288000 | 2.2707 | |
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| 2.398 | 19.33 | 2296000 | 2.2646 | |
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| 2.4007 | 19.4 | 2304000 | 2.2659 | |
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| 2.4007 | 19.47 | 2312000 | 2.2710 | |
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| 2.3955 | 19.54 | 2320000 | 2.2720 | |
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| 2.3955 | 19.6 | 2328000 | 2.2569 | |
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| 2.3973 | 19.67 | 2336000 | 2.2641 | |
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| 2.3973 | 19.74 | 2344000 | 2.2633 | |
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| 2.4059 | 19.81 | 2352000 | 2.2622 | |
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| 2.4059 | 19.87 | 2360000 | 2.2539 | |
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| 2.3899 | 19.94 | 2368000 | 2.2665 | |
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| 2.3899 | 20.01 | 2376000 | 2.2629 | |
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| 2.4025 | 20.08 | 2384000 | 2.2551 | |
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| 2.4025 | 20.14 | 2392000 | 2.2546 | |
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| 2.3956 | 20.21 | 2400000 | 2.2620 | |
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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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