generated_from_trainer

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text_shortening_model_v25

This model is a fine-tuned version of t5-small on the None dataset. It achieves the following results on the evaluation set:

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:

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Bert precision Bert recall Average word count Max word count Min word count Average token count % shortened texts with length > 12
0.9535 1.0 100 1.7458 0.5167 0.3187 0.4841 0.4855 0.8803 0.8796 9.8996 18 4 14.3799 20.524
0.8999 2.0 200 1.6253 0.5209 0.3071 0.474 0.4749 0.8796 0.8803 10.0611 17 3 14.5153 23.5808
0.8388 3.0 300 1.5631 0.5245 0.3124 0.4823 0.4823 0.8773 0.8828 10.5022 17 3 15.1223 27.9476
0.816 4.0 400 1.4961 0.5349 0.3108 0.4786 0.479 0.8733 0.8862 11.1659 17 4 16.0655 32.3144
0.8173 5.0 500 1.4751 0.5243 0.3064 0.4738 0.4733 0.879 0.8832 10.3406 17 4 15.1179 24.4541
0.7685 6.0 600 1.4447 0.5305 0.3111 0.4814 0.4808 0.88 0.8855 10.3712 17 5 15.2009 22.2707
0.7293 7.0 700 1.4249 0.5343 0.3199 0.4823 0.4833 0.879 0.8865 10.6681 17 5 15.5109 25.3275
0.7312 8.0 800 1.3878 0.5344 0.3216 0.487 0.4873 0.8842 0.8863 10.1659 17 5 14.9607 17.0306
0.7256 9.0 900 1.4002 0.532 0.32 0.483 0.4835 0.8785 0.8858 10.6681 17 5 15.5721 25.7642
0.7125 10.0 1000 1.4156 0.5406 0.3301 0.497 0.4969 0.8815 0.889 10.6332 17 5 15.3799 24.4541
0.6937 11.0 1100 1.4109 0.5346 0.3128 0.4893 0.4892 0.8826 0.886 10.2926 17 4 15.1092 20.524
0.6755 12.0 1200 1.3998 0.5388 0.327 0.4936 0.4937 0.8845 0.8882 10.3362 17 4 15.131 20.9607
0.6722 13.0 1300 1.4058 0.538 0.3192 0.4933 0.4925 0.8836 0.8869 10.214 17 4 14.9476 19.6507
0.6656 14.0 1400 1.4237 0.5367 0.3241 0.4917 0.4922 0.8821 0.8857 10.2183 17 4 14.9913 19.214
0.6274 15.0 1500 1.4059 0.5365 0.3315 0.4967 0.4959 0.8848 0.8853 9.9039 17 4 14.6681 14.8472
0.6309 16.0 1600 1.4130 0.5355 0.3311 0.4938 0.494 0.8842 0.8866 10.0568 17 4 14.8515 16.1572
0.6185 17.0 1700 1.4334 0.5357 0.3193 0.483 0.483 0.8826 0.8876 10.5066 17 4 15.345 22.2707
0.6095 18.0 1800 1.4426 0.5425 0.3329 0.498 0.4977 0.8866 0.8869 10.0087 17 4 14.6943 14.4105
0.5951 19.0 1900 1.4640 0.5437 0.3357 0.5013 0.501 0.8864 0.8878 10.0917 17 5 14.786 15.2838
0.5893 20.0 2000 1.4577 0.532 0.3289 0.4879 0.488 0.8842 0.8863 10.1659 17 4 14.9389 19.6507
0.6105 21.0 2100 1.4789 0.5399 0.3336 0.4986 0.498 0.8855 0.888 10.1921 17 5 15.048 17.0306
0.5712 22.0 2200 1.4992 0.533 0.3235 0.4825 0.4821 0.8825 0.8854 10.2664 18 4 15.0786 19.214
0.5961 23.0 2300 1.5211 0.5291 0.3168 0.4826 0.4821 0.8813 0.8849 10.2489 17 5 15.0437 20.0873
0.566 24.0 2400 1.5313 0.5355 0.318 0.4875 0.4872 0.8853 0.8868 9.9563 17 5 14.7424 14.8472
0.5747 25.0 2500 1.5177 0.545 0.3403 0.5029 0.5017 0.888 0.8886 9.8908 17 5 14.6245 13.1004
0.5576 26.0 2600 1.5409 0.5314 0.3258 0.4817 0.4813 0.8818 0.886 10.2795 16 5 15.0655 15.7205
0.5669 27.0 2700 1.5500 0.5293 0.3229 0.4831 0.4835 0.8835 0.8852 9.9301 17 5 14.6507 14.4105
0.5577 28.0 2800 1.5767 0.525 0.3185 0.4825 0.4827 0.8844 0.8837 9.7598 16 5 14.476 13.5371
0.5551 29.0 2900 1.5956 0.5344 0.3269 0.4904 0.4906 0.8864 0.8854 9.786 17 5 14.4192 13.5371
0.517 30.0 3000 1.6067 0.5239 0.3132 0.4798 0.4793 0.8826 0.8831 9.9607 17 5 14.7074 13.5371
0.5316 31.0 3100 1.6107 0.5277 0.3264 0.4835 0.483 0.8835 0.8853 9.9476 16 5 14.7031 14.8472
0.5263 32.0 3200 1.6188 0.527 0.316 0.4788 0.4786 0.8806 0.8844 10.1441 17 5 14.9913 17.4672
0.5397 33.0 3300 1.6249 0.5245 0.3124 0.477 0.4757 0.8813 0.8833 10.0 16 4 14.8428 15.2838
0.52 34.0 3400 1.6383 0.5232 0.3154 0.4782 0.4771 0.8828 0.8838 9.9563 17 4 14.7162 13.5371
0.5331 35.0 3500 1.6546 0.5205 0.3181 0.4755 0.4746 0.882 0.8821 9.869 16 5 14.5677 13.9738
0.5144 36.0 3600 1.6702 0.5295 0.3216 0.4874 0.4865 0.8831 0.885 9.9738 16 4 14.7773 13.5371
0.5076 37.0 3700 1.6865 0.5185 0.3101 0.4703 0.4696 0.8804 0.8817 10.0611 16 4 14.8384 17.9039
0.5222 38.0 3800 1.6799 0.5249 0.3186 0.4837 0.4831 0.8833 0.8838 9.8515 17 4 14.6594 13.1004
0.4992 39.0 3900 1.6934 0.5258 0.3207 0.4866 0.4853 0.8847 0.8829 9.6288 16 4 14.4105 11.7904
0.5135 40.0 4000 1.7291 0.5225 0.3151 0.4768 0.476 0.8833 0.8829 9.8079 17 4 14.5764 11.3537
0.4912 41.0 4100 1.7379 0.5137 0.3089 0.4696 0.4684 0.8818 0.8808 9.7336 16 4 14.5284 13.5371
0.51 42.0 4200 1.7384 0.5177 0.3147 0.4772 0.4765 0.8824 0.8819 9.6856 16 4 14.4934 11.7904
0.5171 43.0 4300 1.7543 0.526 0.3181 0.4779 0.4768 0.884 0.8848 9.9083 17 5 14.6594 13.5371
0.4925 44.0 4400 1.7793 0.5193 0.3162 0.4749 0.4736 0.8831 0.8824 9.6769 16 5 14.4803 13.9738
0.4986 45.0 4500 1.7716 0.5125 0.3124 0.469 0.4678 0.8831 0.8807 9.4585 16 4 14.2489 11.3537
0.4723 46.0 4600 1.7763 0.5146 0.3147 0.4726 0.4714 0.8827 0.8814 9.6463 17 5 14.5022 12.2271
0.4952 47.0 4700 1.8000 0.5184 0.3143 0.4758 0.4744 0.884 0.8814 9.4541 16 4 14.2926 7.8603
0.4882 48.0 4800 1.7944 0.5178 0.3192 0.4715 0.4703 0.8823 0.8814 9.6681 17 5 14.3712 10.4803
0.4815 49.0 4900 1.8060 0.5206 0.3187 0.4762 0.4754 0.8839 0.8813 9.4105 16 4 14.0655 9.1703
0.4607 50.0 5000 1.8159 0.5152 0.3139 0.4695 0.4692 0.8829 0.88 9.5546 16 4 14.2664 9.607
0.4616 51.0 5100 1.8268 0.5201 0.3165 0.4784 0.4776 0.8842 0.8809 9.4847 16 4 14.345 10.0437
0.4581 52.0 5200 1.8350 0.5171 0.3153 0.4745 0.4736 0.8844 0.8807 9.4498 17 4 14.2838 10.917
0.5018 53.0 5300 1.8249 0.5216 0.3233 0.4822 0.4813 0.886 0.8822 9.3712 16 4 14.1834 8.7336
0.4942 54.0 5400 1.8318 0.5164 0.3143 0.4735 0.4737 0.881 0.8816 9.7162 16 4 14.6157 13.5371
0.454 55.0 5500 1.8374 0.5132 0.3099 0.4737 0.4728 0.8828 0.88 9.4323 16 4 14.2882 10.0437
0.4627 56.0 5600 1.8656 0.5188 0.3148 0.4752 0.4747 0.8826 0.8804 9.4672 16 4 14.2358 8.2969
0.5064 57.0 5700 1.8658 0.5158 0.3116 0.4721 0.4712 0.8844 0.8806 9.4454 16 4 14.2096 9.607
0.4612 58.0 5800 1.8849 0.5117 0.3077 0.4667 0.4666 0.8809 0.8787 9.5328 17 4 14.3712 9.607
0.4787 59.0 5900 1.8980 0.5138 0.3073 0.4706 0.4701 0.8818 0.8805 9.5415 17 4 14.3144 10.4803
0.4738 60.0 6000 1.8939 0.5145 0.3117 0.4742 0.4738 0.8829 0.8808 9.4672 16 4 14.2402 9.1703
0.4506 61.0 6100 1.9135 0.5094 0.3029 0.4662 0.4656 0.8799 0.8796 9.7118 16 4 14.4891 11.3537
0.4714 62.0 6200 1.9088 0.5036 0.3044 0.4651 0.4645 0.8791 0.8781 9.7293 17 4 14.3537 15.7205
0.4715 63.0 6300 1.9201 0.5052 0.3015 0.47 0.4691 0.8805 0.878 9.5895 16 4 14.345 12.6638
0.4768 64.0 6400 1.9271 0.5028 0.3037 0.4631 0.4623 0.8781 0.8776 9.7555 17 4 14.4367 14.8472
0.4549 65.0 6500 1.9241 0.5091 0.3092 0.4687 0.4683 0.8811 0.8799 9.6376 17 4 14.3144 12.6638
0.4603 66.0 6600 1.9316 0.5026 0.3007 0.4635 0.4634 0.8798 0.8785 9.6943 17 4 14.4323 13.1004
0.4368 67.0 6700 1.9312 0.5085 0.3055 0.4686 0.468 0.881 0.879 9.5852 16 4 14.262 13.1004
0.4517 68.0 6800 1.9407 0.5079 0.3039 0.4681 0.4676 0.8796 0.879 9.6376 16 4 14.3581 11.3537
0.4509 69.0 6900 1.9491 0.5016 0.2956 0.4632 0.4617 0.8797 0.8779 9.6026 17 4 14.3188 11.3537
0.4792 70.0 7000 1.9537 0.5049 0.2979 0.4646 0.4641 0.8801 0.8793 9.7118 17 4 14.3886 12.2271
0.481 71.0 7100 1.9519 0.5092 0.3063 0.4729 0.4723 0.8812 0.8801 9.6288 17 4 14.4105 11.3537
0.4638 72.0 7200 1.9549 0.5009 0.2977 0.4649 0.4638 0.8792 0.8784 9.6943 17 4 14.4672 11.7904
0.4659 73.0 7300 1.9684 0.4997 0.2973 0.4627 0.4623 0.8768 0.8778 9.8384 17 4 14.6026 13.9738
0.4543 74.0 7400 1.9707 0.5003 0.2962 0.4649 0.4642 0.8778 0.8779 9.6856 16 4 14.4279 12.2271
0.4676 75.0 7500 1.9719 0.5003 0.2955 0.465 0.4649 0.8785 0.8775 9.6332 16 5 14.3493 11.3537
0.4689 76.0 7600 1.9824 0.501 0.3007 0.4687 0.4679 0.8798 0.8783 9.5459 17 4 14.3275 10.4803
0.448 77.0 7700 1.9763 0.5033 0.2996 0.4669 0.4661 0.8789 0.8777 9.6157 16 4 14.3886 12.6638
0.4778 78.0 7800 1.9798 0.5008 0.2944 0.4613 0.4615 0.878 0.8766 9.6638 16 4 14.3013 13.9738
0.4656 79.0 7900 1.9814 0.5014 0.2972 0.4649 0.4649 0.8792 0.8771 9.5459 16 4 14.2576 11.3537
0.4546 80.0 8000 1.9921 0.5024 0.302 0.4663 0.4652 0.8789 0.8772 9.6114 16 4 14.3275 13.1004
0.4781 81.0 8100 1.9996 0.5025 0.2988 0.465 0.4645 0.8788 0.8762 9.5328 16 4 14.214 11.3537
0.4642 82.0 8200 2.0029 0.4974 0.2947 0.4571 0.4565 0.8774 0.8755 9.6725 16 4 14.3231 13.9738
0.4343 83.0 8300 2.0066 0.4979 0.2961 0.4594 0.4584 0.8777 0.8759 9.5939 16 5 14.3275 10.917
0.4439 84.0 8400 2.0091 0.5018 0.2983 0.4624 0.4623 0.8788 0.877 9.5939 16 5 14.3188 10.917
0.4439 85.0 8500 2.0188 0.5057 0.3003 0.4668 0.4669 0.8795 0.8774 9.5502 16 5 14.3057 10.4803
0.4349 86.0 8600 2.0250 0.5129 0.3041 0.4708 0.4703 0.8807 0.8793 9.6943 16 5 14.4323 12.2271
0.4677 87.0 8700 2.0260 0.5057 0.3017 0.4668 0.4657 0.8796 0.8783 9.6376 16 5 14.3712 11.3537
0.4412 88.0 8800 2.0310 0.5057 0.3032 0.4658 0.4645 0.8799 0.8782 9.6681 16 5 14.4148 12.2271
0.4533 89.0 8900 2.0284 0.5061 0.3028 0.4669 0.4657 0.8796 0.8783 9.6594 16 5 14.3886 11.7904
0.423 90.0 9000 2.0317 0.5037 0.2994 0.4656 0.4642 0.879 0.8778 9.6638 16 5 14.4279 11.3537
0.4384 91.0 9100 2.0351 0.5058 0.3003 0.4667 0.4653 0.8792 0.8781 9.6332 16 5 14.3755 10.917
0.4662 92.0 9200 2.0362 0.5043 0.3014 0.4667 0.4655 0.8797 0.8779 9.5808 16 5 14.3188 10.0437
0.4479 93.0 9300 2.0393 0.5051 0.3032 0.4672 0.466 0.8795 0.8779 9.5895 16 5 14.3275 10.0437
0.4609 94.0 9400 2.0400 0.5035 0.2998 0.4667 0.4648 0.8792 0.8775 9.5895 16 5 14.3275 10.0437
0.4513 95.0 9500 2.0434 0.5045 0.3019 0.4671 0.4656 0.8793 0.8778 9.5983 16 5 14.3188 10.0437
0.4496 96.0 9600 2.0439 0.5031 0.3009 0.4657 0.4637 0.8792 0.8777 9.5983 16 5 14.3231 10.4803
0.4481 97.0 9700 2.0464 0.5027 0.3016 0.4645 0.4624 0.8791 0.8777 9.6245 16 5 14.3406 11.3537
0.4522 98.0 9800 2.0459 0.5011 0.3002 0.4642 0.4622 0.8788 0.8771 9.6026 16 5 14.3188 10.917
0.4338 99.0 9900 2.0466 0.5016 0.301 0.4642 0.4623 0.8789 0.8772 9.6201 16 5 14.3319 11.3537
0.4325 100.0 10000 2.0466 0.5016 0.301 0.4642 0.4623 0.8789 0.8772 9.6201 16 5 14.3319 11.3537

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