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--- |
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license: apache-2.0 |
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base_model: google-t5/t5-small |
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tags: |
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- generated_from_trainer |
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metrics: |
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- rouge |
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model-index: |
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- name: my_awesome_billsum_model_26 |
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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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# my_awesome_billsum_model_26 |
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This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2944 |
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- Rouge1: 0.9821 |
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- Rouge2: 0.9347 |
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- Rougel: 0.9494 |
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- Rougelsum: 0.9511 |
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- Gen Len: 5.2708 |
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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: 2e-05 |
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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=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 100 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| |
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| No log | 1.0 | 12 | 2.0408 | 0.4016 | 0.2781 | 0.3809 | 0.3805 | 17.4792 | |
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| No log | 2.0 | 24 | 1.4527 | 0.4407 | 0.3104 | 0.4119 | 0.412 | 16.3125 | |
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| No log | 3.0 | 36 | 0.8914 | 0.6139 | 0.5031 | 0.5902 | 0.5874 | 12.2292 | |
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| No log | 4.0 | 48 | 0.5897 | 0.9653 | 0.8808 | 0.9235 | 0.9251 | 5.0208 | |
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| No log | 5.0 | 60 | 0.5210 | 0.9702 | 0.8931 | 0.9291 | 0.9311 | 5.0417 | |
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| No log | 6.0 | 72 | 0.4877 | 0.968 | 0.8841 | 0.9215 | 0.9241 | 5.0625 | |
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| No log | 7.0 | 84 | 0.4571 | 0.9724 | 0.8944 | 0.9327 | 0.9343 | 5.1458 | |
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| No log | 8.0 | 96 | 0.4342 | 0.9724 | 0.8944 | 0.9327 | 0.9343 | 5.1458 | |
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| No log | 9.0 | 108 | 0.4129 | 0.9724 | 0.8944 | 0.9327 | 0.9343 | 5.1458 | |
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| No log | 10.0 | 120 | 0.3946 | 0.9701 | 0.8859 | 0.9215 | 0.9219 | 5.1667 | |
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| No log | 11.0 | 132 | 0.3824 | 0.9707 | 0.8967 | 0.9308 | 0.9323 | 5.0833 | |
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| No log | 12.0 | 144 | 0.3732 | 0.9678 | 0.8723 | 0.9142 | 0.9157 | 5.1042 | |
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| No log | 13.0 | 156 | 0.3597 | 0.9678 | 0.8723 | 0.9142 | 0.9157 | 5.1042 | |
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| No log | 14.0 | 168 | 0.3501 | 0.9678 | 0.8723 | 0.9142 | 0.9157 | 5.1042 | |
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| No log | 15.0 | 180 | 0.3391 | 0.9713 | 0.8845 | 0.9236 | 0.9236 | 5.125 | |
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| No log | 16.0 | 192 | 0.3338 | 0.9713 | 0.8845 | 0.9236 | 0.9236 | 5.125 | |
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| No log | 17.0 | 204 | 0.3271 | 0.9713 | 0.8845 | 0.9236 | 0.9236 | 5.125 | |
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| No log | 18.0 | 216 | 0.3251 | 0.9713 | 0.8845 | 0.9236 | 0.9236 | 5.125 | |
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| No log | 19.0 | 228 | 0.3243 | 0.9713 | 0.8845 | 0.9236 | 0.9236 | 5.125 | |
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| No log | 20.0 | 240 | 0.3229 | 0.9713 | 0.8773 | 0.9236 | 0.9236 | 5.125 | |
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| No log | 21.0 | 252 | 0.3229 | 0.9713 | 0.8773 | 0.9236 | 0.9236 | 5.125 | |
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| No log | 22.0 | 264 | 0.3182 | 0.9713 | 0.8773 | 0.9236 | 0.9236 | 5.125 | |
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| No log | 23.0 | 276 | 0.3128 | 0.9713 | 0.8773 | 0.9236 | 0.9236 | 5.125 | |
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| No log | 24.0 | 288 | 0.3104 | 0.969 | 0.8773 | 0.9224 | 0.9225 | 5.1458 | |
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| No log | 25.0 | 300 | 0.3100 | 0.969 | 0.8773 | 0.9224 | 0.9225 | 5.1458 | |
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| No log | 26.0 | 312 | 0.3078 | 0.969 | 0.8773 | 0.9224 | 0.9225 | 5.1458 | |
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| No log | 27.0 | 324 | 0.3076 | 0.969 | 0.8773 | 0.9224 | 0.9225 | 5.1458 | |
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| No log | 28.0 | 336 | 0.3063 | 0.966 | 0.875 | 0.9204 | 0.9211 | 5.1667 | |
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| No log | 29.0 | 348 | 0.3014 | 0.9692 | 0.8891 | 0.9291 | 0.9311 | 5.1875 | |
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| No log | 30.0 | 360 | 0.3018 | 0.9692 | 0.8891 | 0.9291 | 0.9311 | 5.1875 | |
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| No log | 31.0 | 372 | 0.3007 | 0.9692 | 0.8891 | 0.9291 | 0.9311 | 5.1875 | |
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| No log | 32.0 | 384 | 0.2968 | 0.9692 | 0.8891 | 0.9291 | 0.9311 | 5.1875 | |
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| No log | 33.0 | 396 | 0.2931 | 0.9692 | 0.8891 | 0.9291 | 0.9311 | 5.1875 | |
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| No log | 34.0 | 408 | 0.2909 | 0.9692 | 0.8891 | 0.9291 | 0.9311 | 5.1875 | |
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| No log | 35.0 | 420 | 0.2893 | 0.9692 | 0.8891 | 0.9291 | 0.9311 | 5.1875 | |
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| No log | 36.0 | 432 | 0.2881 | 0.9692 | 0.8891 | 0.9291 | 0.9311 | 5.1875 | |
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| No log | 37.0 | 444 | 0.2881 | 0.9692 | 0.8891 | 0.9291 | 0.9311 | 5.1875 | |
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| No log | 38.0 | 456 | 0.2877 | 0.9692 | 0.8891 | 0.9291 | 0.9311 | 5.1875 | |
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| No log | 39.0 | 468 | 0.2905 | 0.9692 | 0.8891 | 0.9291 | 0.9311 | 5.1875 | |
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| No log | 40.0 | 480 | 0.2900 | 0.9692 | 0.8891 | 0.9291 | 0.9311 | 5.1875 | |
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| No log | 41.0 | 492 | 0.2901 | 0.9692 | 0.8891 | 0.9291 | 0.9311 | 5.1875 | |
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| 0.4635 | 42.0 | 504 | 0.2904 | 0.9754 | 0.8931 | 0.9315 | 0.9328 | 5.2292 | |
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| 0.4635 | 43.0 | 516 | 0.2885 | 0.9692 | 0.8891 | 0.9291 | 0.9311 | 5.1875 | |
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| 0.4635 | 44.0 | 528 | 0.2895 | 0.9692 | 0.8891 | 0.9291 | 0.9311 | 5.1875 | |
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| 0.4635 | 45.0 | 540 | 0.2898 | 0.9724 | 0.9091 | 0.9437 | 0.9452 | 5.2083 | |
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| 0.4635 | 46.0 | 552 | 0.2869 | 0.9724 | 0.9091 | 0.9437 | 0.9452 | 5.2083 | |
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| 0.4635 | 47.0 | 564 | 0.2880 | 0.9724 | 0.9091 | 0.9437 | 0.9452 | 5.2083 | |
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| 0.4635 | 48.0 | 576 | 0.2893 | 0.9724 | 0.9091 | 0.9385 | 0.9402 | 5.2083 | |
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| 0.4635 | 49.0 | 588 | 0.2916 | 0.9724 | 0.9091 | 0.9437 | 0.9452 | 5.2083 | |
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| 0.4635 | 50.0 | 600 | 0.2903 | 0.9724 | 0.9091 | 0.9385 | 0.9402 | 5.2083 | |
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| 0.4635 | 51.0 | 612 | 0.2870 | 0.9724 | 0.9091 | 0.9385 | 0.9402 | 5.2083 | |
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| 0.4635 | 52.0 | 624 | 0.2856 | 0.9724 | 0.8946 | 0.9335 | 0.935 | 5.2083 | |
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| 0.4635 | 53.0 | 636 | 0.2835 | 0.9715 | 0.8972 | 0.9314 | 0.9327 | 5.1667 | |
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| 0.4635 | 54.0 | 648 | 0.2844 | 0.9724 | 0.9091 | 0.9385 | 0.9402 | 5.2083 | |
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| 0.4635 | 55.0 | 660 | 0.2873 | 0.9724 | 0.9091 | 0.9385 | 0.9402 | 5.2083 | |
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| 0.4635 | 56.0 | 672 | 0.2915 | 0.9756 | 0.9306 | 0.9477 | 0.9494 | 5.2292 | |
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| 0.4635 | 57.0 | 684 | 0.2938 | 0.9756 | 0.9306 | 0.9477 | 0.9494 | 5.2292 | |
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| 0.4635 | 58.0 | 696 | 0.2934 | 0.9756 | 0.9306 | 0.9477 | 0.9494 | 5.2292 | |
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| 0.4635 | 59.0 | 708 | 0.2890 | 0.9756 | 0.9306 | 0.9477 | 0.9494 | 5.2292 | |
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| 0.4635 | 60.0 | 720 | 0.2858 | 0.9756 | 0.9306 | 0.9477 | 0.9494 | 5.2292 | |
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| 0.4635 | 61.0 | 732 | 0.2881 | 0.9756 | 0.9306 | 0.9477 | 0.9494 | 5.2292 | |
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| 0.4635 | 62.0 | 744 | 0.2889 | 0.9756 | 0.9306 | 0.9477 | 0.9494 | 5.2292 | |
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| 0.4635 | 63.0 | 756 | 0.2878 | 0.9724 | 0.9091 | 0.9385 | 0.9402 | 5.2083 | |
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| 0.4635 | 64.0 | 768 | 0.2904 | 0.979 | 0.9134 | 0.9402 | 0.942 | 5.25 | |
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| 0.4635 | 65.0 | 780 | 0.2917 | 0.979 | 0.9134 | 0.9402 | 0.942 | 5.25 | |
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| 0.4635 | 66.0 | 792 | 0.2919 | 0.979 | 0.9134 | 0.9402 | 0.942 | 5.25 | |
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| 0.4635 | 67.0 | 804 | 0.2893 | 0.9821 | 0.9347 | 0.9494 | 0.9511 | 5.2708 | |
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| 0.4635 | 68.0 | 816 | 0.2894 | 0.9821 | 0.9347 | 0.9494 | 0.9511 | 5.2708 | |
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| 0.4635 | 69.0 | 828 | 0.2876 | 0.9821 | 0.9347 | 0.9494 | 0.9511 | 5.2708 | |
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| 0.4635 | 70.0 | 840 | 0.2913 | 0.9821 | 0.9347 | 0.9494 | 0.9511 | 5.2708 | |
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| 0.4635 | 71.0 | 852 | 0.2912 | 0.9821 | 0.9347 | 0.9494 | 0.9511 | 5.2708 | |
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| 0.4635 | 72.0 | 864 | 0.2935 | 0.9821 | 0.9347 | 0.9494 | 0.9511 | 5.2708 | |
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| 0.4635 | 73.0 | 876 | 0.2962 | 0.9821 | 0.9347 | 0.9494 | 0.9511 | 5.2708 | |
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| 0.4635 | 74.0 | 888 | 0.2987 | 0.9821 | 0.9347 | 0.9494 | 0.9511 | 5.2708 | |
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| 0.4635 | 75.0 | 900 | 0.2987 | 0.9821 | 0.9347 | 0.9494 | 0.9511 | 5.2708 | |
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| 0.4635 | 76.0 | 912 | 0.2972 | 0.9821 | 0.9347 | 0.9494 | 0.9511 | 5.2708 | |
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| 0.4635 | 77.0 | 924 | 0.2979 | 0.9821 | 0.9347 | 0.9494 | 0.9511 | 5.2708 | |
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| 0.4635 | 78.0 | 936 | 0.2992 | 0.9821 | 0.9347 | 0.9494 | 0.9511 | 5.2708 | |
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| 0.4635 | 79.0 | 948 | 0.3006 | 0.9821 | 0.9347 | 0.9494 | 0.9511 | 5.2708 | |
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| 0.4635 | 80.0 | 960 | 0.3000 | 0.9821 | 0.9347 | 0.9494 | 0.9511 | 5.2708 | |
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| 0.4635 | 81.0 | 972 | 0.2975 | 0.9821 | 0.9347 | 0.9494 | 0.9511 | 5.2708 | |
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| 0.4635 | 82.0 | 984 | 0.2958 | 0.9821 | 0.9347 | 0.9494 | 0.9511 | 5.2708 | |
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| 0.4635 | 83.0 | 996 | 0.2954 | 0.9821 | 0.9347 | 0.9494 | 0.9511 | 5.2708 | |
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| 0.0955 | 84.0 | 1008 | 0.2949 | 0.9821 | 0.9347 | 0.9494 | 0.9511 | 5.2708 | |
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| 0.0955 | 85.0 | 1020 | 0.2933 | 0.9821 | 0.9347 | 0.9494 | 0.9511 | 5.2708 | |
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| 0.0955 | 86.0 | 1032 | 0.2931 | 0.9821 | 0.9347 | 0.9494 | 0.9511 | 5.2708 | |
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| 0.0955 | 87.0 | 1044 | 0.2927 | 0.9821 | 0.9347 | 0.9494 | 0.9511 | 5.2708 | |
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| 0.0955 | 88.0 | 1056 | 0.2910 | 0.9821 | 0.9347 | 0.9494 | 0.9511 | 5.2708 | |
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| 0.0955 | 89.0 | 1068 | 0.2909 | 0.9821 | 0.9347 | 0.9494 | 0.9511 | 5.2708 | |
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| 0.0955 | 90.0 | 1080 | 0.2910 | 0.9821 | 0.9347 | 0.9494 | 0.9511 | 5.2708 | |
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| 0.0955 | 91.0 | 1092 | 0.2923 | 0.9821 | 0.9347 | 0.9494 | 0.9511 | 5.2708 | |
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| 0.0955 | 92.0 | 1104 | 0.2926 | 0.9821 | 0.9347 | 0.9494 | 0.9511 | 5.2708 | |
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| 0.0955 | 93.0 | 1116 | 0.2928 | 0.9821 | 0.9347 | 0.9494 | 0.9511 | 5.2708 | |
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| 0.0955 | 94.0 | 1128 | 0.2929 | 0.9821 | 0.9347 | 0.9494 | 0.9511 | 5.2708 | |
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| 0.0955 | 95.0 | 1140 | 0.2929 | 0.9821 | 0.9347 | 0.9494 | 0.9511 | 5.2708 | |
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| 0.0955 | 96.0 | 1152 | 0.2931 | 0.9821 | 0.9347 | 0.9494 | 0.9511 | 5.2708 | |
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| 0.0955 | 97.0 | 1164 | 0.2939 | 0.9821 | 0.9347 | 0.9494 | 0.9511 | 5.2708 | |
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| 0.0955 | 98.0 | 1176 | 0.2942 | 0.9821 | 0.9347 | 0.9494 | 0.9511 | 5.2708 | |
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| 0.0955 | 99.0 | 1188 | 0.2944 | 0.9821 | 0.9347 | 0.9494 | 0.9511 | 5.2708 | |
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| 0.0955 | 100.0 | 1200 | 0.2944 | 0.9821 | 0.9347 | 0.9494 | 0.9511 | 5.2708 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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