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t5-base-finetuned-depression

Text2Text Generation
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  1. README.md +31 -21
  2. model.safetensors +1 -1
  3. runs/Jun22_12-40-34_2a884f4b335f/events.out.tfevents.1719060148.2a884f4b335f.1523.2 +2 -2
README.md CHANGED
@@ -21,16 +21,16 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2149
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- - Rouge1: 89.1258
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- - Rouge2: 22.9211
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- - Rougel: 89.1258
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- - Rougelsum: 89.1258
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- - Gen Len: 2.2452
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- - Precision: 0.9021
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- - Recall: 0.8485
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- - F1: 0.8703
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- - Accuracy: 0.8913
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  ## Model description
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@@ -55,23 +55,33 @@ The following hyperparameters were used during training:
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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: 10
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  - mixed_precision_training: Native AMP
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|:---------:|:------:|:------:|:--------:|
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- | No log | 1.0 | 469 | 0.3452 | 69.8294 | 10.3412 | 69.7228 | 69.7228 | 2.1205 | 0.8443 | 0.4449 | 0.4429 | 0.6972 |
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- | 0.7878 | 2.0 | 938 | 0.2701 | 79.3177 | 18.6567 | 79.1045 | 79.2111 | 2.2004 | 0.8066 | 0.5637 | 0.5899 | 0.7921 |
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- | 0.3152 | 3.0 | 1407 | 0.2429 | 83.049 | 20.8955 | 82.9424 | 82.9424 | 2.2217 | 0.9125 | 0.636 | 0.686 | 0.8294 |
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- | 0.249 | 4.0 | 1876 | 0.1857 | 87.2068 | 22.2814 | 86.9936 | 87.1002 | 2.2377 | 0.8982 | 0.7789 | 0.8241 | 0.871 |
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- | 0.2 | 5.0 | 2345 | 0.1822 | 88.1663 | 22.2814 | 88.1663 | 88.1663 | 2.2313 | 0.8983 | 0.8029 | 0.84 | 0.8817 |
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- | 0.1513 | 6.0 | 2814 | 0.1760 | 88.6994 | 22.8145 | 88.6994 | 88.6994 | 2.242 | 0.9066 | 0.8269 | 0.8593 | 0.887 |
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- | 0.1468 | 7.0 | 3283 | 0.1910 | 88.6994 | 22.9211 | 88.6994 | 88.6994 | 2.2484 | 0.8903 | 0.8462 | 0.8641 | 0.887 |
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- | 0.1323 | 8.0 | 3752 | 0.1864 | 89.2324 | 23.5608 | 89.2324 | 89.2324 | 2.2537 | 0.8966 | 0.852 | 0.8706 | 0.8923 |
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- | 0.1157 | 9.0 | 4221 | 0.2031 | 89.2324 | 23.2409 | 89.2324 | 89.2324 | 2.2505 | 0.9009 | 0.8508 | 0.8717 | 0.8923 |
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- | 0.1002 | 10.0 | 4690 | 0.2149 | 89.1258 | 22.9211 | 89.1258 | 89.1258 | 2.2452 | 0.9021 | 0.8485 | 0.8703 | 0.8913 |
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2260
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+ - Rouge1: 89.7655
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+ - Rouge2: 24.4136
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+ - Rougel: 89.7655
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+ - Rougelsum: 89.7655
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+ - Gen Len: 2.2719
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+ - Precision: 0.8856
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+ - Recall: 0.8807
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+ - F1: 0.8817
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+ - Accuracy: 0.8977
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  ## Model description
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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: 20
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  - mixed_precision_training: Native AMP
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 1.0 | 469 | 0.3428 | 69.6162 | 9.7015 | 69.5096 | 69.6162 | 2.1087 | 0.8545 | 0.4409 | 0.4375 | 0.6962 |
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+ | 0.7863 | 2.0 | 938 | 0.2674 | 79.5309 | 19.0832 | 79.5309 | 79.5309 | 2.2058 | 0.8192 | 0.5744 | 0.6052 | 0.7953 |
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+ | 0.3128 | 3.0 | 1407 | 0.2317 | 84.0085 | 21.322 | 84.0085 | 84.0085 | 2.2239 | 0.9053 | 0.6654 | 0.721 | 0.8401 |
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+ | 0.2367 | 4.0 | 1876 | 0.1736 | 86.887 | 22.3881 | 86.887 | 86.887 | 2.242 | 0.6608 | 0.586 | 0.6155 | 0.8689 |
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+ | 0.1844 | 5.0 | 2345 | 0.1802 | 88.5928 | 22.7079 | 88.5928 | 88.5928 | 2.2388 | 0.9113 | 0.8252 | 0.8597 | 0.8859 |
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+ | 0.135 | 6.0 | 2814 | 0.2000 | 88.4861 | 22.2814 | 88.4861 | 88.4861 | 2.2345 | 0.9045 | 0.8405 | 0.8655 | 0.8849 |
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+ | 0.1247 | 7.0 | 3283 | 0.2048 | 89.5522 | 23.5608 | 89.4989 | 89.5522 | 2.2495 | 0.9108 | 0.8526 | 0.8769 | 0.8955 |
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+ | 0.1071 | 8.0 | 3752 | 0.2361 | 89.1258 | 23.7207 | 89.1258 | 89.1258 | 2.2591 | 0.6783 | 0.6467 | 0.6603 | 0.8913 |
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+ | 0.0832 | 9.0 | 4221 | 0.2486 | 89.8721 | 24.5203 | 89.8721 | 89.8721 | 2.2889 | 0.6695 | 0.6532 | 0.6603 | 0.8987 |
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+ | 0.0652 | 10.0 | 4690 | 0.3051 | 89.339 | 23.1343 | 89.339 | 89.339 | 2.2473 | 0.9065 | 0.8642 | 0.8811 | 0.8934 |
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+ | 0.0674 | 11.0 | 5159 | 0.3269 | 89.7655 | 23.9872 | 89.7655 | 89.7655 | 2.2623 | 0.8973 | 0.8711 | 0.8819 | 0.8977 |
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+ | 0.0575 | 12.0 | 5628 | 0.3241 | 89.4456 | 23.8806 | 89.4456 | 89.4456 | 2.2633 | 0.8903 | 0.8652 | 0.8756 | 0.8945 |
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+ | 0.0422 | 13.0 | 6097 | 0.3088 | 90.0853 | 24.5203 | 90.0853 | 90.0853 | 2.2729 | 0.6754 | 0.6595 | 0.6664 | 0.9009 |
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+ | 0.0395 | 14.0 | 6566 | 0.2781 | 90.0853 | 25.3731 | 90.0853 | 90.0853 | 2.2889 | 0.6801 | 0.6575 | 0.6681 | 0.9009 |
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+ | 0.0341 | 15.0 | 7035 | 0.2658 | 90.1919 | 24.5203 | 90.1919 | 90.1919 | 2.2719 | 0.9043 | 0.8836 | 0.8926 | 0.9019 |
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+ | 0.0336 | 16.0 | 7504 | 0.2433 | 90.0853 | 24.8401 | 90.0853 | 90.0853 | 2.2772 | 0.9048 | 0.8769 | 0.8896 | 0.9009 |
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+ | 0.0336 | 17.0 | 7973 | 0.2363 | 89.8721 | 24.6269 | 89.8721 | 89.8721 | 2.274 | 0.6717 | 0.6563 | 0.6631 | 0.8987 |
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+ | 0.0274 | 18.0 | 8442 | 0.2297 | 90.4051 | 25.2132 | 90.4051 | 90.4051 | 2.2814 | 0.904 | 0.8882 | 0.8953 | 0.9041 |
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+ | 0.0298 | 19.0 | 8911 | 0.2275 | 89.7655 | 24.4136 | 89.7655 | 89.7655 | 2.2719 | 0.8886 | 0.8807 | 0.8832 | 0.8977 |
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+ | 0.0261 | 20.0 | 9380 | 0.2260 | 89.7655 | 24.4136 | 89.7655 | 89.7655 | 2.2719 | 0.8856 | 0.8807 | 0.8817 | 0.8977 |
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  ### Framework versions
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