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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_v78 |
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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_v78 |
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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: 1.2534 |
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- Bert precision: 0.8992 |
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- Bert recall: 0.8971 |
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- Bert f1-score: 0.8977 |
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- Average word count: 6.6982 |
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- Max word count: 16 |
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- Min word count: 2 |
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- Average token count: 10.8994 |
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- % shortened texts with length > 12: 1.4724 |
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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: 0.0002 |
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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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| 1.7828 | 1.0 | 30 | 1.2876 | 0.8895 | 0.8866 | 0.8876 | 6.7595 | 16 | 2 | 10.6969 | 1.3497 | |
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| 1.3539 | 2.0 | 60 | 1.1852 | 0.8938 | 0.8896 | 0.8912 | 6.6074 | 16 | 2 | 10.6442 | 1.227 | |
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| 1.2004 | 3.0 | 90 | 1.1374 | 0.8942 | 0.891 | 0.8921 | 6.6896 | 16 | 2 | 10.7534 | 1.3497 | |
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| 1.1048 | 4.0 | 120 | 1.1165 | 0.8929 | 0.8931 | 0.8926 | 6.8025 | 16 | 2 | 10.9178 | 1.7178 | |
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| 1.0322 | 5.0 | 150 | 1.0995 | 0.8953 | 0.896 | 0.8952 | 6.827 | 17 | 2 | 11.0037 | 1.9632 | |
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| 0.9687 | 6.0 | 180 | 1.0822 | 0.894 | 0.8947 | 0.8939 | 6.9043 | 17 | 3 | 11.0147 | 2.2086 | |
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| 0.9076 | 7.0 | 210 | 1.0858 | 0.8976 | 0.8965 | 0.8966 | 6.7951 | 17 | 2 | 10.9804 | 1.8405 | |
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| 0.8602 | 8.0 | 240 | 1.0894 | 0.8977 | 0.897 | 0.8969 | 6.7902 | 17 | 2 | 10.9558 | 2.0859 | |
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| 0.8076 | 9.0 | 270 | 1.0945 | 0.8982 | 0.8974 | 0.8974 | 6.7669 | 17 | 2 | 10.9779 | 1.9632 | |
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| 0.7739 | 10.0 | 300 | 1.0843 | 0.8974 | 0.8959 | 0.8962 | 6.719 | 17 | 2 | 10.8564 | 1.9632 | |
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| 0.7361 | 11.0 | 330 | 1.0982 | 0.8972 | 0.8967 | 0.8965 | 6.789 | 17 | 3 | 10.9779 | 1.5951 | |
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| 0.7052 | 12.0 | 360 | 1.0953 | 0.8977 | 0.8958 | 0.8963 | 6.7055 | 17 | 2 | 10.8798 | 1.8405 | |
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| 0.6749 | 13.0 | 390 | 1.1098 | 0.8981 | 0.8967 | 0.897 | 6.7325 | 17 | 2 | 10.908 | 2.0859 | |
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| 0.6441 | 14.0 | 420 | 1.1158 | 0.8996 | 0.8981 | 0.8984 | 6.7043 | 17 | 2 | 10.8761 | 1.5951 | |
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| 0.623 | 15.0 | 450 | 1.1146 | 0.9009 | 0.8989 | 0.8994 | 6.7006 | 17 | 2 | 10.9104 | 1.3497 | |
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| 0.604 | 16.0 | 480 | 1.1267 | 0.902 | 0.8988 | 0.9 | 6.7104 | 17 | 2 | 10.8491 | 1.3497 | |
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| 0.583 | 17.0 | 510 | 1.1357 | 0.8999 | 0.8989 | 0.8989 | 6.7706 | 17 | 2 | 10.9767 | 2.0859 | |
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| 0.5605 | 18.0 | 540 | 1.1513 | 0.8996 | 0.899 | 0.8989 | 6.7534 | 17 | 2 | 11.0356 | 2.0859 | |
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| 0.5439 | 19.0 | 570 | 1.1643 | 0.8993 | 0.8988 | 0.8986 | 6.816 | 17 | 2 | 11.0638 | 1.8405 | |
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| 0.5281 | 20.0 | 600 | 1.1626 | 0.8998 | 0.8985 | 0.8987 | 6.7399 | 16 | 2 | 11.0025 | 1.4724 | |
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| 0.5165 | 21.0 | 630 | 1.1720 | 0.9002 | 0.8974 | 0.8983 | 6.6417 | 16 | 2 | 10.7816 | 1.5951 | |
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| 0.5002 | 22.0 | 660 | 1.1834 | 0.9005 | 0.8995 | 0.8996 | 6.7607 | 17 | 2 | 10.9693 | 2.2086 | |
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| 0.486 | 23.0 | 690 | 1.2005 | 0.9027 | 0.8979 | 0.8999 | 6.5853 | 17 | 2 | 10.7264 | 1.8405 | |
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| 0.4757 | 24.0 | 720 | 1.1916 | 0.9008 | 0.8991 | 0.8995 | 6.6994 | 16 | 2 | 10.8859 | 2.0859 | |
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| 0.4679 | 25.0 | 750 | 1.2023 | 0.8988 | 0.8985 | 0.8982 | 6.7853 | 16 | 2 | 10.984 | 2.0859 | |
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| 0.4502 | 26.0 | 780 | 1.2108 | 0.9012 | 0.8991 | 0.8997 | 6.7141 | 16 | 2 | 10.8577 | 2.3313 | |
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| 0.4452 | 27.0 | 810 | 1.2188 | 0.9015 | 0.8987 | 0.8996 | 6.6098 | 14 | 2 | 10.7853 | 1.8405 | |
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| 0.4351 | 28.0 | 840 | 1.2187 | 0.9005 | 0.8997 | 0.8996 | 6.7681 | 16 | 2 | 10.9877 | 2.0859 | |
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| 0.4223 | 29.0 | 870 | 1.2319 | 0.9001 | 0.8985 | 0.8989 | 6.6798 | 16 | 2 | 10.9031 | 1.7178 | |
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| 0.4186 | 30.0 | 900 | 1.2386 | 0.8987 | 0.8982 | 0.898 | 6.7509 | 16 | 2 | 10.9718 | 1.8405 | |
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| 0.4146 | 31.0 | 930 | 1.2399 | 0.9001 | 0.8989 | 0.8991 | 6.719 | 16 | 2 | 10.9067 | 1.8405 | |
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| 0.4039 | 32.0 | 960 | 1.2467 | 0.8997 | 0.8992 | 0.899 | 6.7632 | 16 | 2 | 11.0074 | 2.0859 | |
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| 0.3998 | 33.0 | 990 | 1.2425 | 0.8997 | 0.899 | 0.8989 | 6.7497 | 16 | 2 | 10.946 | 1.8405 | |
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| 0.396 | 34.0 | 1020 | 1.2474 | 0.9001 | 0.8982 | 0.8987 | 6.6945 | 16 | 2 | 10.8773 | 1.7178 | |
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| 0.3938 | 35.0 | 1050 | 1.2497 | 0.9003 | 0.8977 | 0.8985 | 6.6454 | 16 | 2 | 10.8245 | 0.9816 | |
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| 0.3828 | 36.0 | 1080 | 1.2511 | 0.9002 | 0.8978 | 0.8986 | 6.6798 | 16 | 2 | 10.8663 | 1.4724 | |
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| 0.3842 | 37.0 | 1110 | 1.2482 | 0.9002 | 0.898 | 0.8987 | 6.6883 | 16 | 2 | 10.8883 | 1.7178 | |
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| 0.391 | 38.0 | 1140 | 1.2507 | 0.9001 | 0.8974 | 0.8983 | 6.6577 | 16 | 2 | 10.8528 | 1.3497 | |
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| 0.3796 | 39.0 | 1170 | 1.2529 | 0.8994 | 0.8972 | 0.8979 | 6.6945 | 16 | 2 | 10.8933 | 1.5951 | |
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| 0.3807 | 40.0 | 1200 | 1.2534 | 0.8992 | 0.8971 | 0.8977 | 6.6982 | 16 | 2 | 10.8994 | 1.4724 | |
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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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