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--- |
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license: apache-2.0 |
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base_model: t5-small |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: text_shortening_model_v74 |
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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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# text_shortening_model_v74 |
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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: 1.2644 |
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- Bert precision: 0.8826 |
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- Bert recall: 0.8851 |
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- Bert f1-score: 0.8832 |
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- Average word count: 6.7137 |
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- Max word count: 16 |
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- Min word count: 2 |
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- Average token count: 10.6547 |
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- % shortened texts with length > 12: 2.6026 |
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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: 1e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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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: 40 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Bert precision | Bert recall | Bert f1-score | 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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| 2.5095 | 1.0 | 37 | 1.9952 | 0.8247 | 0.8396 | 0.8308 | 8.5926 | 19 | 0 | 12.961 | 12.8128 | |
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| 2.1271 | 2.0 | 74 | 1.7552 | 0.8393 | 0.8454 | 0.841 | 7.7247 | 17 | 0 | 11.7738 | 9.3093 | |
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| 1.9629 | 3.0 | 111 | 1.6420 | 0.8552 | 0.8582 | 0.8556 | 7.2022 | 17 | 1 | 11.3193 | 6.5065 | |
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| 1.8511 | 4.0 | 148 | 1.5687 | 0.8646 | 0.8639 | 0.8634 | 6.8078 | 17 | 1 | 10.8539 | 4.1041 | |
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| 1.7806 | 5.0 | 185 | 1.5196 | 0.8684 | 0.8693 | 0.8681 | 6.8278 | 16 | 1 | 10.8438 | 3.8038 | |
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| 1.7193 | 6.0 | 222 | 1.4840 | 0.8713 | 0.8736 | 0.8717 | 6.8388 | 18 | 2 | 10.8318 | 3.4034 | |
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| 1.6763 | 7.0 | 259 | 1.4540 | 0.8756 | 0.8765 | 0.8754 | 6.7528 | 18 | 2 | 10.6847 | 3.003 | |
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| 1.6389 | 8.0 | 296 | 1.4316 | 0.8766 | 0.8785 | 0.8769 | 6.7628 | 16 | 2 | 10.6917 | 2.6026 | |
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| 1.6146 | 9.0 | 333 | 1.4149 | 0.8771 | 0.8798 | 0.8778 | 6.8018 | 15 | 2 | 10.7177 | 2.7027 | |
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| 1.597 | 10.0 | 370 | 1.3986 | 0.8782 | 0.8811 | 0.879 | 6.7998 | 15 | 2 | 10.7067 | 2.5025 | |
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| 1.5761 | 11.0 | 407 | 1.3860 | 0.8792 | 0.8815 | 0.8797 | 6.7588 | 15 | 2 | 10.6496 | 2.3023 | |
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| 1.5456 | 12.0 | 444 | 1.3747 | 0.8792 | 0.8813 | 0.8797 | 6.7387 | 16 | 2 | 10.6376 | 2.2022 | |
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| 1.533 | 13.0 | 481 | 1.3647 | 0.88 | 0.8823 | 0.8805 | 6.7347 | 16 | 2 | 10.6276 | 2.1021 | |
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| 1.5142 | 14.0 | 518 | 1.3536 | 0.8805 | 0.8822 | 0.8808 | 6.7047 | 16 | 2 | 10.5746 | 1.9019 | |
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| 1.514 | 15.0 | 555 | 1.3429 | 0.8803 | 0.882 | 0.8805 | 6.6847 | 16 | 2 | 10.5606 | 1.7017 | |
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| 1.4973 | 16.0 | 592 | 1.3353 | 0.8805 | 0.8828 | 0.881 | 6.7467 | 16 | 2 | 10.6627 | 2.1021 | |
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| 1.4792 | 17.0 | 629 | 1.3277 | 0.8811 | 0.8829 | 0.8814 | 6.7077 | 16 | 2 | 10.6166 | 2.002 | |
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| 1.4669 | 18.0 | 666 | 1.3206 | 0.8815 | 0.8831 | 0.8817 | 6.6927 | 16 | 2 | 10.6016 | 2.1021 | |
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| 1.4667 | 19.0 | 703 | 1.3141 | 0.881 | 0.8831 | 0.8815 | 6.7167 | 16 | 2 | 10.6306 | 2.1021 | |
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| 1.4497 | 20.0 | 740 | 1.3097 | 0.8808 | 0.883 | 0.8813 | 6.7227 | 16 | 2 | 10.6416 | 2.1021 | |
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| 1.4533 | 21.0 | 777 | 1.3053 | 0.8814 | 0.8831 | 0.8817 | 6.6997 | 16 | 2 | 10.6086 | 2.1021 | |
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| 1.4408 | 22.0 | 814 | 1.2998 | 0.8808 | 0.8825 | 0.881 | 6.7037 | 16 | 2 | 10.6076 | 2.2022 | |
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| 1.4343 | 23.0 | 851 | 1.2958 | 0.8807 | 0.8829 | 0.8812 | 6.7297 | 16 | 2 | 10.6306 | 2.3023 | |
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| 1.4295 | 24.0 | 888 | 1.2926 | 0.881 | 0.8833 | 0.8816 | 6.7427 | 16 | 2 | 10.6486 | 2.4024 | |
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| 1.4219 | 25.0 | 925 | 1.2887 | 0.8812 | 0.8835 | 0.8818 | 6.7327 | 16 | 2 | 10.6426 | 2.4024 | |
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| 1.4045 | 26.0 | 962 | 1.2855 | 0.8814 | 0.8836 | 0.8819 | 6.7187 | 16 | 2 | 10.6256 | 2.4024 | |
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| 1.409 | 27.0 | 999 | 1.2826 | 0.8817 | 0.884 | 0.8823 | 6.7217 | 16 | 2 | 10.6456 | 2.6026 | |
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| 1.3994 | 28.0 | 1036 | 1.2803 | 0.8826 | 0.8848 | 0.8831 | 6.7047 | 16 | 2 | 10.6226 | 2.7027 | |
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| 1.3905 | 29.0 | 1073 | 1.2778 | 0.8823 | 0.8847 | 0.8829 | 6.7267 | 16 | 2 | 10.6507 | 2.8028 | |
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| 1.4014 | 30.0 | 1110 | 1.2751 | 0.8821 | 0.8845 | 0.8827 | 6.7237 | 16 | 2 | 10.6466 | 2.8028 | |
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| 1.3946 | 31.0 | 1147 | 1.2732 | 0.8826 | 0.8849 | 0.8831 | 6.7167 | 16 | 2 | 10.6426 | 2.8028 | |
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| 1.3915 | 32.0 | 1184 | 1.2712 | 0.8823 | 0.8845 | 0.8828 | 6.7057 | 16 | 2 | 10.6336 | 2.7027 | |
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| 1.3904 | 33.0 | 1221 | 1.2695 | 0.8824 | 0.8847 | 0.883 | 6.7047 | 16 | 2 | 10.6376 | 2.7027 | |
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| 1.3843 | 34.0 | 1258 | 1.2684 | 0.8828 | 0.885 | 0.8833 | 6.7097 | 16 | 2 | 10.6406 | 2.6026 | |
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| 1.3875 | 35.0 | 1295 | 1.2672 | 0.8827 | 0.8852 | 0.8834 | 6.7217 | 16 | 2 | 10.6607 | 2.6026 | |
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| 1.3794 | 36.0 | 1332 | 1.2661 | 0.8828 | 0.8851 | 0.8834 | 6.7087 | 16 | 2 | 10.6426 | 2.6026 | |
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| 1.3906 | 37.0 | 1369 | 1.2654 | 0.8828 | 0.8853 | 0.8835 | 6.7177 | 16 | 2 | 10.6567 | 2.6026 | |
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| 1.3841 | 38.0 | 1406 | 1.2648 | 0.8826 | 0.8851 | 0.8833 | 6.7107 | 16 | 2 | 10.6476 | 2.6026 | |
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| 1.3761 | 39.0 | 1443 | 1.2645 | 0.8825 | 0.885 | 0.8832 | 6.7137 | 16 | 2 | 10.6537 | 2.6026 | |
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| 1.3797 | 40.0 | 1480 | 1.2644 | 0.8826 | 0.8851 | 0.8832 | 6.7137 | 16 | 2 | 10.6547 | 2.6026 | |
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### Framework versions |
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- Transformers 4.33.1 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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