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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_v73 |
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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_v73 |
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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: 1.6126 |
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- Bert precision: 0.9015 |
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- Bert recall: 0.9014 |
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- Bert f1-score: 0.901 |
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- Average word count: 6.4004 |
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- Max word count: 16 |
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- Min word count: 2 |
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- Average token count: 10.4705 |
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- % shortened texts with length > 12: 1.1011 |
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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: 0.0005 |
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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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| 1.5857 | 1.0 | 37 | 1.1932 | 0.8846 | 0.8848 | 0.8842 | 6.5315 | 16 | 1 | 10.4525 | 1.7017 | |
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| 1.184 | 2.0 | 74 | 1.0965 | 0.8918 | 0.8915 | 0.8911 | 6.4855 | 17 | 2 | 10.4735 | 0.5005 | |
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| 1.0114 | 3.0 | 111 | 1.0773 | 0.8895 | 0.8962 | 0.8924 | 6.8959 | 18 | 2 | 10.995 | 1.3013 | |
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| 0.8887 | 4.0 | 148 | 1.0798 | 0.8947 | 0.8936 | 0.8937 | 6.4454 | 17 | 2 | 10.4605 | 1.8018 | |
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| 0.7851 | 5.0 | 185 | 1.0807 | 0.8941 | 0.8948 | 0.894 | 6.5676 | 16 | 2 | 10.6016 | 1.6016 | |
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| 0.7116 | 6.0 | 222 | 1.1002 | 0.8984 | 0.8978 | 0.8976 | 6.4605 | 15 | 2 | 10.4174 | 1.2012 | |
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| 0.6472 | 7.0 | 259 | 1.1171 | 0.8982 | 0.8997 | 0.8985 | 6.5836 | 16 | 2 | 10.6426 | 1.3013 | |
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| 0.5872 | 8.0 | 296 | 1.1196 | 0.8998 | 0.9015 | 0.9002 | 6.5415 | 16 | 2 | 10.6226 | 1.5015 | |
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| 0.5393 | 9.0 | 333 | 1.1739 | 0.9007 | 0.8979 | 0.8988 | 6.3333 | 16 | 2 | 10.3063 | 1.1011 | |
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| 0.4879 | 10.0 | 370 | 1.2079 | 0.8997 | 0.8983 | 0.8985 | 6.3343 | 15 | 2 | 10.2913 | 1.001 | |
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| 0.4615 | 11.0 | 407 | 1.2230 | 0.8988 | 0.8997 | 0.8988 | 6.5165 | 15 | 2 | 10.6426 | 1.3013 | |
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| 0.4245 | 12.0 | 444 | 1.2325 | 0.8996 | 0.8979 | 0.8983 | 6.3704 | 15 | 2 | 10.4334 | 1.3013 | |
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| 0.3973 | 13.0 | 481 | 1.2657 | 0.8973 | 0.8987 | 0.8975 | 6.4855 | 15 | 2 | 10.5876 | 1.6016 | |
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| 0.3658 | 14.0 | 518 | 1.2875 | 0.8985 | 0.8993 | 0.8984 | 6.4735 | 15 | 2 | 10.5355 | 1.2012 | |
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| 0.3422 | 15.0 | 555 | 1.3202 | 0.9002 | 0.8991 | 0.8992 | 6.2873 | 14 | 2 | 10.3594 | 1.001 | |
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| 0.3271 | 16.0 | 592 | 1.3315 | 0.9006 | 0.9 | 0.8998 | 6.3784 | 15 | 2 | 10.4454 | 0.9009 | |
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| 0.305 | 17.0 | 629 | 1.3441 | 0.8994 | 0.9005 | 0.8995 | 6.4705 | 16 | 2 | 10.5906 | 1.2012 | |
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| 0.2847 | 18.0 | 666 | 1.3648 | 0.8997 | 0.8989 | 0.8989 | 6.3584 | 14 | 2 | 10.4244 | 0.9009 | |
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| 0.2707 | 19.0 | 703 | 1.3837 | 0.9005 | 0.9011 | 0.9003 | 6.4545 | 16 | 2 | 10.5365 | 1.3013 | |
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| 0.254 | 20.0 | 740 | 1.4180 | 0.8997 | 0.9006 | 0.8997 | 6.4444 | 15 | 2 | 10.5516 | 1.2012 | |
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| 0.2421 | 21.0 | 777 | 1.4100 | 0.9014 | 0.903 | 0.9017 | 6.4755 | 16 | 2 | 10.6016 | 0.9009 | |
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| 0.2301 | 22.0 | 814 | 1.4437 | 0.9 | 0.901 | 0.9 | 6.4825 | 15 | 2 | 10.5626 | 0.8008 | |
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| 0.2183 | 23.0 | 851 | 1.4762 | 0.9003 | 0.9014 | 0.9004 | 6.4995 | 16 | 2 | 10.6116 | 1.3013 | |
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| 0.2148 | 24.0 | 888 | 1.4815 | 0.9007 | 0.9014 | 0.9006 | 6.4484 | 16 | 2 | 10.5495 | 1.1011 | |
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| 0.2013 | 25.0 | 925 | 1.5039 | 0.9018 | 0.9015 | 0.9012 | 6.4144 | 15 | 2 | 10.4925 | 1.001 | |
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| 0.1924 | 26.0 | 962 | 1.5217 | 0.9013 | 0.9014 | 0.9009 | 6.4024 | 16 | 2 | 10.4765 | 1.2012 | |
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| 0.1854 | 27.0 | 999 | 1.5125 | 0.902 | 0.9014 | 0.9012 | 6.3774 | 16 | 2 | 10.4565 | 1.1011 | |
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| 0.1769 | 28.0 | 1036 | 1.5384 | 0.8998 | 0.9011 | 0.9 | 6.4925 | 16 | 2 | 10.6106 | 1.001 | |
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| 0.1713 | 29.0 | 1073 | 1.5627 | 0.9012 | 0.9018 | 0.901 | 6.4715 | 16 | 2 | 10.5395 | 1.2012 | |
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| 0.1685 | 30.0 | 1110 | 1.5473 | 0.9011 | 0.9004 | 0.9002 | 6.4064 | 16 | 2 | 10.4484 | 1.1011 | |
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| 0.1681 | 31.0 | 1147 | 1.5592 | 0.9018 | 0.9018 | 0.9013 | 6.4194 | 15 | 2 | 10.5165 | 0.8008 | |
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| 0.1599 | 32.0 | 1184 | 1.5800 | 0.9006 | 0.9007 | 0.9002 | 6.4254 | 16 | 2 | 10.5005 | 1.001 | |
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| 0.1509 | 33.0 | 1221 | 1.5822 | 0.9012 | 0.9005 | 0.9004 | 6.3994 | 16 | 2 | 10.4314 | 1.001 | |
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| 0.1509 | 34.0 | 1258 | 1.5924 | 0.9013 | 0.9008 | 0.9006 | 6.4084 | 16 | 2 | 10.4655 | 1.1011 | |
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| 0.1408 | 35.0 | 1295 | 1.6045 | 0.9028 | 0.9024 | 0.9021 | 6.4074 | 16 | 2 | 10.4845 | 1.2012 | |
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| 0.1487 | 36.0 | 1332 | 1.6133 | 0.9014 | 0.9012 | 0.9008 | 6.4244 | 16 | 2 | 10.4775 | 1.001 | |
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| 0.1444 | 37.0 | 1369 | 1.6157 | 0.9016 | 0.9016 | 0.9012 | 6.4304 | 16 | 2 | 10.5045 | 1.2012 | |
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| 0.1418 | 38.0 | 1406 | 1.6105 | 0.9012 | 0.9011 | 0.9006 | 6.4084 | 16 | 2 | 10.4615 | 1.1011 | |
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| 0.1402 | 39.0 | 1443 | 1.6116 | 0.9017 | 0.9015 | 0.9011 | 6.3894 | 16 | 2 | 10.4494 | 1.1011 | |
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| 0.1375 | 40.0 | 1480 | 1.6126 | 0.9015 | 0.9014 | 0.901 | 6.4004 | 16 | 2 | 10.4705 | 1.1011 | |
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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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