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text_shortening_model_v23

Text2Text Generation
Transformers
PyTorch
t5
Generated from Trainer
text-generation-inference
Inference Endpoints
Model card Files Community
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End of training

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  1. README.md +63 -14
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -17,18 +17,18 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.9401
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- - Rouge1: 0.5036
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- - Rouge2: 0.29
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- - Rougel: 0.4624
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- - Rougelsum: 0.462
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- - Bert precision: 0.8687
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- - Bert recall: 0.875
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- - Average word count: 10.6288
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- - Max word count: 17
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- - Min word count: 4
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- - Average token count: 15.4105
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- - % shortened texts with length > 12: 30.5677
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  ## Model description
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@@ -53,13 +53,62 @@ The following hyperparameters were used during training:
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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: 1
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  ### Training results
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  | 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 |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------------:|:-----------:|:------------------:|:--------------:|:--------------:|:-------------------:|:----------------------------------:|
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- | 2.2552 | 1.0 | 100 | 1.9401 | 0.5036 | 0.29 | 0.4624 | 0.462 | 0.8687 | 0.875 | 10.6288 | 17 | 4 | 15.4105 | 30.5677 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.5992
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+ - Rouge1: 0.5244
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+ - Rouge2: 0.3068
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+ - Rougel: 0.4711
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+ - Rougelsum: 0.4712
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+ - Bert precision: 0.8806
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+ - Bert recall: 0.8799
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+ - Average word count: 9.7031
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+ - Max word count: 15
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+ - Min word count: 5
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+ - Average token count: 14.5895
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+ - % shortened texts with length > 12: 13.5371
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  ## Model description
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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: 50
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  ### Training results
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  | 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 |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------------:|:-----------:|:------------------:|:--------------:|:--------------:|:-------------------:|:----------------------------------:|
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+ | 2.2171 | 1.0 | 100 | 1.7694 | 0.514 | 0.2977 | 0.4697 | 0.4699 | 0.8711 | 0.8789 | 10.7598 | 17 | 3 | 15.5764 | 29.6943 |
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+ | 1.8398 | 2.0 | 200 | 1.6351 | 0.5161 | 0.3041 | 0.4676 | 0.4683 | 0.8737 | 0.8815 | 10.655 | 17 | 4 | 15.5284 | 26.6376 |
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+ | 1.6309 | 3.0 | 300 | 1.5497 | 0.5277 | 0.3192 | 0.4741 | 0.4749 | 0.8799 | 0.8836 | 10.179 | 17 | 6 | 15.0218 | 20.9607 |
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+ | 1.5015 | 4.0 | 400 | 1.4933 | 0.5295 | 0.3196 | 0.4783 | 0.4787 | 0.8768 | 0.8838 | 10.5371 | 17 | 6 | 15.393 | 24.4541 |
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+ | 1.3868 | 5.0 | 500 | 1.4555 | 0.5311 | 0.3235 | 0.4721 | 0.4726 | 0.8776 | 0.8839 | 10.5022 | 17 | 5 | 15.4323 | 24.4541 |
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+ | 1.3147 | 6.0 | 600 | 1.4297 | 0.5312 | 0.3234 | 0.476 | 0.4768 | 0.8796 | 0.8826 | 10.0917 | 17 | 5 | 14.9563 | 19.214 |
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+ | 1.2207 | 7.0 | 700 | 1.4147 | 0.5256 | 0.315 | 0.4747 | 0.4753 | 0.877 | 0.8837 | 10.4148 | 17 | 5 | 15.3537 | 23.1441 |
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+ | 1.1465 | 8.0 | 800 | 1.3993 | 0.521 | 0.3112 | 0.4691 | 0.4698 | 0.8784 | 0.8819 | 10.179 | 17 | 5 | 15.0262 | 18.7773 |
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+ | 1.1006 | 9.0 | 900 | 1.3868 | 0.5235 | 0.3122 | 0.4701 | 0.4707 | 0.8766 | 0.8819 | 10.3231 | 17 | 5 | 15.179 | 21.3974 |
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+ | 1.0469 | 10.0 | 1000 | 1.3790 | 0.5174 | 0.3028 | 0.4644 | 0.4647 | 0.877 | 0.8811 | 10.1266 | 17 | 5 | 15.0306 | 17.0306 |
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+ | 0.978 | 11.0 | 1100 | 1.3848 | 0.5226 | 0.3015 | 0.4697 | 0.4704 | 0.8779 | 0.8818 | 10.1528 | 17 | 5 | 15.1397 | 16.5939 |
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+ | 0.9379 | 12.0 | 1200 | 1.3937 | 0.5129 | 0.2966 | 0.457 | 0.4575 | 0.8772 | 0.88 | 10.1048 | 17 | 6 | 14.9301 | 18.3406 |
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+ | 0.8987 | 13.0 | 1300 | 1.3858 | 0.5203 | 0.3057 | 0.4673 | 0.4679 | 0.8798 | 0.8812 | 9.9738 | 17 | 5 | 14.8472 | 14.8472 |
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+ | 0.8455 | 14.0 | 1400 | 1.3936 | 0.519 | 0.3028 | 0.4636 | 0.4639 | 0.8788 | 0.88 | 9.9476 | 17 | 5 | 14.8734 | 17.0306 |
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+ | 0.8106 | 15.0 | 1500 | 1.3965 | 0.5293 | 0.3145 | 0.4771 | 0.4778 | 0.8819 | 0.8828 | 9.7773 | 17 | 5 | 14.6376 | 14.4105 |
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+ | 0.7857 | 16.0 | 1600 | 1.4079 | 0.5239 | 0.3105 | 0.4698 | 0.4702 | 0.8792 | 0.8807 | 9.9127 | 17 | 5 | 14.8166 | 16.5939 |
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+ | 0.7661 | 17.0 | 1700 | 1.4106 | 0.5192 | 0.3058 | 0.4657 | 0.4663 | 0.8787 | 0.8797 | 9.9214 | 17 | 5 | 14.6856 | 17.4672 |
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+ | 0.7239 | 18.0 | 1800 | 1.4206 | 0.5226 | 0.307 | 0.4683 | 0.469 | 0.8797 | 0.8813 | 9.8646 | 17 | 5 | 14.8297 | 14.4105 |
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+ | 0.7021 | 19.0 | 1900 | 1.4213 | 0.5183 | 0.3052 | 0.467 | 0.4669 | 0.8801 | 0.8796 | 9.6943 | 17 | 5 | 14.5066 | 11.7904 |
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+ | 0.6752 | 20.0 | 2000 | 1.4283 | 0.5263 | 0.3102 | 0.4767 | 0.4777 | 0.8819 | 0.8815 | 9.6638 | 17 | 5 | 14.5415 | 11.7904 |
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+ | 0.6642 | 21.0 | 2100 | 1.4261 | 0.5286 | 0.3132 | 0.4746 | 0.4753 | 0.8818 | 0.8808 | 9.607 | 17 | 5 | 14.4148 | 10.0437 |
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+ | 0.6319 | 22.0 | 2200 | 1.4426 | 0.5343 | 0.315 | 0.4763 | 0.4765 | 0.8809 | 0.8819 | 10.0 | 17 | 5 | 14.821 | 16.1572 |
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+ | 0.6149 | 23.0 | 2300 | 1.4537 | 0.5334 | 0.3182 | 0.4808 | 0.4807 | 0.8821 | 0.8811 | 9.6943 | 17 | 5 | 14.5066 | 13.5371 |
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+ | 0.6063 | 24.0 | 2400 | 1.4483 | 0.528 | 0.3117 | 0.4712 | 0.4719 | 0.8808 | 0.8816 | 9.8035 | 17 | 5 | 14.607 | 15.2838 |
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+ | 0.57 | 25.0 | 2500 | 1.4770 | 0.5234 | 0.3059 | 0.4644 | 0.4647 | 0.8814 | 0.8799 | 9.6288 | 17 | 5 | 14.3755 | 13.9738 |
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+ | 0.5585 | 26.0 | 2600 | 1.4928 | 0.5232 | 0.3059 | 0.47 | 0.47 | 0.8795 | 0.8812 | 9.8865 | 17 | 5 | 14.6638 | 14.4105 |
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+ | 0.5568 | 27.0 | 2700 | 1.4829 | 0.529 | 0.3059 | 0.4703 | 0.4704 | 0.8811 | 0.881 | 9.7773 | 17 | 5 | 14.5459 | 14.4105 |
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+ | 0.5404 | 28.0 | 2800 | 1.5009 | 0.5196 | 0.3028 | 0.4664 | 0.4666 | 0.8788 | 0.8789 | 9.7598 | 15 | 5 | 14.6419 | 13.9738 |
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+ | 0.5253 | 29.0 | 2900 | 1.5142 | 0.5168 | 0.2952 | 0.4614 | 0.4617 | 0.8797 | 0.8778 | 9.5502 | 15 | 5 | 14.262 | 12.2271 |
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+ | 0.5176 | 30.0 | 3000 | 1.5150 | 0.523 | 0.3035 | 0.4658 | 0.4659 | 0.8788 | 0.881 | 10.0393 | 17 | 5 | 14.7904 | 19.214 |
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+ | 0.5002 | 31.0 | 3100 | 1.5348 | 0.5291 | 0.3074 | 0.471 | 0.4713 | 0.8791 | 0.882 | 10.0262 | 17 | 5 | 14.8559 | 19.214 |
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+ | 0.4944 | 32.0 | 3200 | 1.5343 | 0.5183 | 0.3028 | 0.4674 | 0.468 | 0.8798 | 0.8791 | 9.69 | 17 | 5 | 14.4279 | 13.9738 |
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+ | 0.493 | 33.0 | 3300 | 1.5319 | 0.5245 | 0.3027 | 0.4685 | 0.4686 | 0.88 | 0.8803 | 9.7948 | 17 | 5 | 14.6594 | 14.4105 |
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+ | 0.4617 | 34.0 | 3400 | 1.5453 | 0.5258 | 0.3052 | 0.4685 | 0.4691 | 0.8807 | 0.8815 | 9.7598 | 17 | 5 | 14.6026 | 13.1004 |
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+ | 0.4642 | 35.0 | 3500 | 1.5520 | 0.532 | 0.3119 | 0.478 | 0.4785 | 0.8821 | 0.8825 | 9.8035 | 17 | 5 | 14.6157 | 15.2838 |
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+ | 0.4559 | 36.0 | 3600 | 1.5570 | 0.5239 | 0.3109 | 0.4694 | 0.4703 | 0.8801 | 0.8815 | 9.8079 | 17 | 5 | 14.7205 | 13.9738 |
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+ | 0.4435 | 37.0 | 3700 | 1.5606 | 0.5222 | 0.3058 | 0.4666 | 0.467 | 0.8792 | 0.8799 | 9.7729 | 17 | 5 | 14.6288 | 14.4105 |
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+ | 0.4423 | 38.0 | 3800 | 1.5744 | 0.524 | 0.3089 | 0.4682 | 0.4687 | 0.881 | 0.88 | 9.7162 | 15 | 5 | 14.4803 | 13.9738 |
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+ | 0.4399 | 39.0 | 3900 | 1.5732 | 0.5245 | 0.3127 | 0.4718 | 0.4721 | 0.8802 | 0.881 | 9.7729 | 15 | 5 | 14.6681 | 13.9738 |
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+ | 0.4265 | 40.0 | 4000 | 1.5692 | 0.5306 | 0.3192 | 0.4784 | 0.4789 | 0.8831 | 0.8816 | 9.607 | 15 | 5 | 14.4061 | 11.7904 |
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+ | 0.435 | 41.0 | 4100 | 1.5752 | 0.526 | 0.31 | 0.4734 | 0.474 | 0.8819 | 0.8803 | 9.6245 | 15 | 5 | 14.476 | 12.6638 |
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+ | 0.414 | 42.0 | 4200 | 1.5803 | 0.5249 | 0.3091 | 0.4707 | 0.47 | 0.8813 | 0.8795 | 9.5939 | 15 | 5 | 14.4061 | 12.6638 |
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+ | 0.4161 | 43.0 | 4300 | 1.5888 | 0.5237 | 0.3045 | 0.4685 | 0.4676 | 0.8808 | 0.8799 | 9.6638 | 15 | 5 | 14.5153 | 12.2271 |
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+ | 0.3968 | 44.0 | 4400 | 1.5946 | 0.5214 | 0.3049 | 0.4677 | 0.4676 | 0.8801 | 0.8803 | 9.7511 | 15 | 5 | 14.6376 | 13.1004 |
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+ | 0.405 | 45.0 | 4500 | 1.5967 | 0.5234 | 0.3066 | 0.4692 | 0.4692 | 0.8808 | 0.8808 | 9.7598 | 15 | 5 | 14.6026 | 13.1004 |
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+ | 0.4063 | 46.0 | 4600 | 1.5984 | 0.5238 | 0.3077 | 0.47 | 0.4703 | 0.8807 | 0.8809 | 9.8297 | 15 | 5 | 14.7031 | 15.2838 |
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+ | 0.4006 | 47.0 | 4700 | 1.5971 | 0.5231 | 0.3082 | 0.4702 | 0.4697 | 0.8807 | 0.8804 | 9.7118 | 15 | 5 | 14.607 | 13.9738 |
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+ | 0.4045 | 48.0 | 4800 | 1.5988 | 0.5232 | 0.3054 | 0.4707 | 0.4707 | 0.881 | 0.8803 | 9.6812 | 15 | 5 | 14.5721 | 13.5371 |
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+ | 0.397 | 49.0 | 4900 | 1.5991 | 0.5244 | 0.3068 | 0.471 | 0.4711 | 0.8806 | 0.8799 | 9.7031 | 15 | 5 | 14.5983 | 13.5371 |
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+ | 0.3963 | 50.0 | 5000 | 1.5992 | 0.5244 | 0.3068 | 0.4711 | 0.4712 | 0.8806 | 0.8799 | 9.7031 | 15 | 5 | 14.5895 | 13.5371 |
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  ### Framework versions
pytorch_model.bin CHANGED
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