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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_v79 |
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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_v79 |
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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: 1.0551 |
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- Bert precision: 0.8947 |
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- Bert recall: 0.8962 |
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- Bert f1-score: 0.895 |
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- Average word count: 6.7804 |
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- Max word count: 16 |
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- Min word count: 1 |
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- Average token count: 10.8466 |
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- % shortened texts with length > 12: 1.5951 |
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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: 7e-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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| 2.0194 | 1.0 | 30 | 1.4487 | 0.8778 | 0.8746 | 0.8755 | 6.7755 | 16 | 1 | 10.7288 | 2.3313 | |
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| 1.58 | 2.0 | 60 | 1.3193 | 0.8835 | 0.8837 | 0.883 | 6.9301 | 16 | 2 | 10.7791 | 2.3313 | |
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| 1.4385 | 3.0 | 90 | 1.2492 | 0.8833 | 0.8855 | 0.8839 | 7.0368 | 16 | 2 | 10.9816 | 2.6994 | |
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| 1.3616 | 4.0 | 120 | 1.2111 | 0.8877 | 0.8873 | 0.887 | 6.8466 | 16 | 2 | 10.7509 | 1.8405 | |
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| 1.2976 | 5.0 | 150 | 1.1685 | 0.8869 | 0.8878 | 0.8868 | 6.8564 | 17 | 2 | 10.8172 | 1.8405 | |
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| 1.2495 | 6.0 | 180 | 1.1559 | 0.8885 | 0.8895 | 0.8885 | 6.8577 | 16 | 2 | 10.8564 | 2.0859 | |
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| 1.201 | 7.0 | 210 | 1.1353 | 0.8889 | 0.891 | 0.8894 | 6.9521 | 16 | 2 | 11.0012 | 2.3313 | |
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| 1.1717 | 8.0 | 240 | 1.1164 | 0.8892 | 0.89 | 0.8891 | 6.8601 | 16 | 1 | 10.8933 | 2.0859 | |
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| 1.1352 | 9.0 | 270 | 1.1110 | 0.8902 | 0.8891 | 0.8891 | 6.708 | 16 | 1 | 10.7436 | 1.1043 | |
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| 1.0984 | 10.0 | 300 | 1.1037 | 0.8901 | 0.8909 | 0.8901 | 6.8233 | 17 | 1 | 10.8503 | 1.9632 | |
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| 1.0745 | 11.0 | 330 | 1.0937 | 0.8894 | 0.892 | 0.8902 | 6.9362 | 17 | 2 | 10.9742 | 2.3313 | |
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| 1.0509 | 12.0 | 360 | 1.0907 | 0.8911 | 0.8916 | 0.8908 | 6.8233 | 17 | 1 | 10.8564 | 1.9632 | |
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| 1.0269 | 13.0 | 390 | 1.0805 | 0.8906 | 0.8934 | 0.8915 | 6.9448 | 17 | 1 | 11.0135 | 2.2086 | |
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| 1.0126 | 14.0 | 420 | 1.0784 | 0.8912 | 0.8935 | 0.8919 | 6.9264 | 17 | 2 | 10.973 | 2.3313 | |
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| 0.9959 | 15.0 | 450 | 1.0725 | 0.8929 | 0.8944 | 0.8932 | 6.8294 | 17 | 1 | 10.8957 | 2.2086 | |
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| 0.9717 | 16.0 | 480 | 1.0715 | 0.8916 | 0.8941 | 0.8924 | 6.919 | 17 | 1 | 10.9963 | 2.0859 | |
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| 0.9552 | 17.0 | 510 | 1.0727 | 0.8935 | 0.8949 | 0.8937 | 6.8282 | 17 | 1 | 10.9055 | 1.9632 | |
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| 0.9461 | 18.0 | 540 | 1.0665 | 0.8947 | 0.8955 | 0.8947 | 6.8061 | 17 | 1 | 10.8613 | 1.5951 | |
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| 0.926 | 19.0 | 570 | 1.0664 | 0.8948 | 0.896 | 0.895 | 6.7853 | 16 | 1 | 10.8515 | 1.3497 | |
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| 0.9192 | 20.0 | 600 | 1.0636 | 0.8948 | 0.8953 | 0.8946 | 6.7718 | 16 | 1 | 10.8209 | 1.4724 | |
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| 0.9101 | 21.0 | 630 | 1.0581 | 0.8954 | 0.897 | 0.8957 | 6.8221 | 16 | 1 | 10.8724 | 1.5951 | |
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| 0.899 | 22.0 | 660 | 1.0599 | 0.8954 | 0.8974 | 0.8959 | 6.8405 | 16 | 1 | 10.8982 | 1.5951 | |
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| 0.8843 | 23.0 | 690 | 1.0586 | 0.8943 | 0.8962 | 0.8948 | 6.8393 | 17 | 2 | 10.9055 | 1.9632 | |
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| 0.8779 | 24.0 | 720 | 1.0572 | 0.8932 | 0.8961 | 0.8942 | 6.8736 | 17 | 2 | 10.9656 | 2.0859 | |
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| 0.8725 | 25.0 | 750 | 1.0573 | 0.8939 | 0.8963 | 0.8947 | 6.8098 | 16 | 2 | 10.9104 | 1.7178 | |
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| 0.8567 | 26.0 | 780 | 1.0591 | 0.8951 | 0.8968 | 0.8955 | 6.7926 | 17 | 1 | 10.8945 | 1.5951 | |
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| 0.8549 | 27.0 | 810 | 1.0577 | 0.8945 | 0.8962 | 0.8948 | 6.8135 | 17 | 1 | 10.9018 | 1.8405 | |
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| 0.8467 | 28.0 | 840 | 1.0570 | 0.8948 | 0.8961 | 0.895 | 6.7669 | 16 | 1 | 10.8405 | 1.4724 | |
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| 0.833 | 29.0 | 870 | 1.0577 | 0.895 | 0.896 | 0.895 | 6.7546 | 16 | 1 | 10.8294 | 1.3497 | |
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| 0.8284 | 30.0 | 900 | 1.0548 | 0.8942 | 0.8957 | 0.8945 | 6.7816 | 16 | 1 | 10.8589 | 1.4724 | |
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| 0.8296 | 31.0 | 930 | 1.0565 | 0.8947 | 0.8967 | 0.8952 | 6.8037 | 16 | 1 | 10.8982 | 1.4724 | |
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| 0.8156 | 32.0 | 960 | 1.0550 | 0.8945 | 0.8961 | 0.8948 | 6.7914 | 16 | 2 | 10.8601 | 1.5951 | |
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| 0.8095 | 33.0 | 990 | 1.0567 | 0.8944 | 0.8962 | 0.8948 | 6.8049 | 16 | 2 | 10.881 | 1.7178 | |
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| 0.8066 | 34.0 | 1020 | 1.0564 | 0.8948 | 0.8961 | 0.895 | 6.7853 | 16 | 1 | 10.8405 | 1.8405 | |
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| 0.817 | 35.0 | 1050 | 1.0567 | 0.8951 | 0.8961 | 0.8952 | 6.7509 | 16 | 1 | 10.8172 | 1.5951 | |
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| 0.8155 | 36.0 | 1080 | 1.0563 | 0.8949 | 0.8964 | 0.8952 | 6.7669 | 16 | 1 | 10.838 | 1.5951 | |
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| 0.808 | 37.0 | 1110 | 1.0560 | 0.8946 | 0.8965 | 0.8951 | 6.7926 | 16 | 1 | 10.8675 | 1.7178 | |
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| 0.8049 | 38.0 | 1140 | 1.0554 | 0.895 | 0.8965 | 0.8953 | 6.7742 | 16 | 1 | 10.8393 | 1.4724 | |
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| 0.8002 | 39.0 | 1170 | 1.0550 | 0.8946 | 0.8962 | 0.8949 | 6.7877 | 16 | 1 | 10.8491 | 1.5951 | |
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| 0.7912 | 40.0 | 1200 | 1.0551 | 0.8947 | 0.8962 | 0.895 | 6.7804 | 16 | 1 | 10.8466 | 1.5951 | |
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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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