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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_v68 |
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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_v68 |
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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: 1.1011 |
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- Bert precision: 0.8904 |
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- Bert recall: 0.8919 |
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- Bert f1-score: 0.8906 |
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- Average word count: 6.7117 |
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- Max word count: 18 |
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- Min word count: 2 |
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- Average token count: 10.7497 |
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- % shortened texts with length > 12: 2.002 |
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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: 5e-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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| 2.3576 | 1.0 | 37 | 1.6358 | 0.847 | 0.8396 | 0.8424 | 6.3233 | 16 | 0 | 10.7958 | 6.3063 | |
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| 1.7631 | 2.0 | 74 | 1.4404 | 0.8739 | 0.8715 | 0.8721 | 6.5666 | 16 | 0 | 10.4695 | 2.3023 | |
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| 1.5884 | 3.0 | 111 | 1.3492 | 0.8807 | 0.8787 | 0.8791 | 6.6006 | 16 | 2 | 10.3904 | 2.002 | |
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| 1.5068 | 4.0 | 148 | 1.2953 | 0.8829 | 0.881 | 0.8813 | 6.6006 | 16 | 2 | 10.4194 | 1.7017 | |
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| 1.4361 | 5.0 | 185 | 1.2638 | 0.8847 | 0.8836 | 0.8836 | 6.6547 | 16 | 2 | 10.4965 | 1.3013 | |
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| 1.3844 | 6.0 | 222 | 1.2357 | 0.8851 | 0.8846 | 0.8843 | 6.6747 | 16 | 2 | 10.5105 | 1.9019 | |
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| 1.351 | 7.0 | 259 | 1.2146 | 0.8832 | 0.8858 | 0.8839 | 6.8649 | 16 | 2 | 10.7628 | 2.3023 | |
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| 1.2944 | 8.0 | 296 | 1.2008 | 0.8848 | 0.8867 | 0.8852 | 6.7728 | 15 | 2 | 10.7047 | 2.1021 | |
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| 1.2785 | 9.0 | 333 | 1.1889 | 0.8856 | 0.8872 | 0.8858 | 6.7538 | 16 | 2 | 10.6987 | 1.8018 | |
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| 1.2469 | 10.0 | 370 | 1.1774 | 0.8851 | 0.8868 | 0.8854 | 6.7247 | 15 | 2 | 10.6627 | 2.1021 | |
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| 1.2206 | 11.0 | 407 | 1.1674 | 0.886 | 0.8882 | 0.8865 | 6.7558 | 16 | 2 | 10.7477 | 1.9019 | |
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| 1.1955 | 12.0 | 444 | 1.1614 | 0.8851 | 0.8875 | 0.8858 | 6.7748 | 15 | 2 | 10.7848 | 1.9019 | |
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| 1.1707 | 13.0 | 481 | 1.1516 | 0.8854 | 0.8879 | 0.8861 | 6.7698 | 15 | 2 | 10.7908 | 2.002 | |
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| 1.1455 | 14.0 | 518 | 1.1470 | 0.8871 | 0.8882 | 0.8872 | 6.6817 | 17 | 1 | 10.6867 | 1.9019 | |
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| 1.1392 | 15.0 | 555 | 1.1384 | 0.8861 | 0.8889 | 0.887 | 6.7658 | 17 | 1 | 10.8008 | 1.8018 | |
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| 1.1212 | 16.0 | 592 | 1.1351 | 0.8876 | 0.8902 | 0.8883 | 6.7528 | 17 | 1 | 10.8078 | 2.002 | |
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| 1.0965 | 17.0 | 629 | 1.1316 | 0.8861 | 0.8893 | 0.8872 | 6.7918 | 17 | 1 | 10.8639 | 2.3023 | |
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| 1.1 | 18.0 | 666 | 1.1269 | 0.8869 | 0.8901 | 0.8879 | 6.8218 | 17 | 2 | 10.8809 | 2.2022 | |
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| 1.0679 | 19.0 | 703 | 1.1220 | 0.8867 | 0.8889 | 0.8873 | 6.7157 | 17 | 1 | 10.7658 | 1.5015 | |
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| 1.0708 | 20.0 | 740 | 1.1209 | 0.8865 | 0.8889 | 0.8872 | 6.7618 | 17 | 1 | 10.7898 | 1.8018 | |
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| 1.0444 | 21.0 | 777 | 1.1178 | 0.8872 | 0.8892 | 0.8877 | 6.7047 | 17 | 2 | 10.7598 | 1.8018 | |
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| 1.0347 | 22.0 | 814 | 1.1161 | 0.8882 | 0.8902 | 0.8887 | 6.7167 | 17 | 2 | 10.7568 | 1.6016 | |
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| 1.0212 | 23.0 | 851 | 1.1147 | 0.8883 | 0.89 | 0.8886 | 6.7017 | 17 | 2 | 10.7467 | 1.8018 | |
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| 1.0264 | 24.0 | 888 | 1.1113 | 0.8879 | 0.8899 | 0.8884 | 6.6987 | 17 | 2 | 10.7397 | 1.8018 | |
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| 1.0186 | 25.0 | 925 | 1.1099 | 0.8876 | 0.8893 | 0.8879 | 6.6997 | 17 | 2 | 10.7417 | 1.7017 | |
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| 1.0124 | 26.0 | 962 | 1.1102 | 0.8882 | 0.8903 | 0.8888 | 6.7277 | 17 | 2 | 10.7718 | 2.1021 | |
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| 1.0081 | 27.0 | 999 | 1.1082 | 0.8889 | 0.8901 | 0.889 | 6.6687 | 17 | 2 | 10.6877 | 1.7017 | |
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| 1.0107 | 28.0 | 1036 | 1.1044 | 0.8893 | 0.8906 | 0.8894 | 6.6567 | 17 | 2 | 10.6807 | 1.7017 | |
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| 0.9788 | 29.0 | 1073 | 1.1060 | 0.8891 | 0.8905 | 0.8893 | 6.6817 | 18 | 2 | 10.7137 | 2.002 | |
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| 0.9899 | 30.0 | 1110 | 1.1052 | 0.8894 | 0.8915 | 0.8899 | 6.7357 | 18 | 2 | 10.7598 | 2.2022 | |
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| 0.9736 | 31.0 | 1147 | 1.1050 | 0.8896 | 0.8915 | 0.8901 | 6.7027 | 18 | 2 | 10.7367 | 2.002 | |
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| 0.9779 | 32.0 | 1184 | 1.1051 | 0.8899 | 0.892 | 0.8905 | 6.7237 | 18 | 2 | 10.7618 | 2.1021 | |
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| 0.9704 | 33.0 | 1221 | 1.1033 | 0.89 | 0.8914 | 0.8902 | 6.6877 | 18 | 2 | 10.7117 | 1.8018 | |
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| 0.9711 | 34.0 | 1258 | 1.1021 | 0.8894 | 0.8912 | 0.8898 | 6.7027 | 18 | 2 | 10.7327 | 1.8018 | |
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| 0.9637 | 35.0 | 1295 | 1.1019 | 0.89 | 0.8913 | 0.8901 | 6.6907 | 18 | 2 | 10.7217 | 1.9019 | |
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| 0.9525 | 36.0 | 1332 | 1.1016 | 0.8901 | 0.8915 | 0.8903 | 6.6997 | 18 | 2 | 10.7177 | 1.9019 | |
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| 0.9668 | 37.0 | 1369 | 1.1009 | 0.8902 | 0.8918 | 0.8905 | 6.7127 | 18 | 2 | 10.7497 | 2.002 | |
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| 0.9704 | 38.0 | 1406 | 1.1013 | 0.8904 | 0.8921 | 0.8908 | 6.7187 | 18 | 2 | 10.7528 | 2.1021 | |
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| 0.9531 | 39.0 | 1443 | 1.1010 | 0.8904 | 0.8919 | 0.8906 | 6.7117 | 18 | 2 | 10.7497 | 2.002 | |
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| 0.958 | 40.0 | 1480 | 1.1011 | 0.8904 | 0.8919 | 0.8906 | 6.7117 | 18 | 2 | 10.7497 | 2.002 | |
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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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