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TensorBoard
Safetensors
t5
text2text-generation
Generated from Trainer
text-generation-inference
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xet
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End of training

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  1. README.md +38 -12
  2. runs/Feb14_20-38-08_cbe6401c379d/events.out.tfevents.1739565491.cbe6401c379d.3312.5 +2 -2
README.md CHANGED
@@ -18,11 +18,11 @@ 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-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.6216
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- - Rouge1: 0.138
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- - Rouge2: 0.0455
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- - Rougel: 0.1115
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- - Rougelsum: 0.1115
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  - Gen Len: 20.0
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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- - train_batch_size: 16
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- - eval_batch_size: 16
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  - seed: 42
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  - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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- - num_epochs: 4
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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 |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
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- | No log | 1.0 | 62 | 2.9011 | 0.1198 | 0.0317 | 0.0979 | 0.0979 | 20.0 |
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- | No log | 2.0 | 124 | 2.6950 | 0.1321 | 0.0432 | 0.1075 | 0.1073 | 20.0 |
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- | No log | 3.0 | 186 | 2.6365 | 0.1357 | 0.0437 | 0.1103 | 0.1102 | 20.0 |
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- | No log | 4.0 | 248 | 2.6216 | 0.138 | 0.0455 | 0.1115 | 0.1115 | 20.0 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 2.2923
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+ - Rouge1: 0.1987
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+ - Rouge2: 0.0971
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+ - Rougel: 0.1702
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+ - Rougelsum: 0.1701
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  - Gen Len: 20.0
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  ## Model description
 
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  The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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  - seed: 42
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  - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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+ - num_epochs: 30
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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 |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
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+ | No log | 1.0 | 31 | 2.5664 | 0.1535 | 0.0599 | 0.1259 | 0.126 | 20.0 |
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+ | No log | 2.0 | 62 | 2.5187 | 0.1742 | 0.0706 | 0.1446 | 0.1446 | 20.0 |
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+ | No log | 3.0 | 93 | 2.4849 | 0.1909 | 0.0835 | 0.1607 | 0.1606 | 20.0 |
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+ | No log | 4.0 | 124 | 2.4579 | 0.197 | 0.0876 | 0.1651 | 0.1651 | 20.0 |
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+ | No log | 5.0 | 155 | 2.4365 | 0.1955 | 0.086 | 0.1636 | 0.1634 | 20.0 |
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+ | No log | 6.0 | 186 | 2.4185 | 0.1969 | 0.0877 | 0.1655 | 0.1654 | 20.0 |
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+ | No log | 7.0 | 217 | 2.4042 | 0.1975 | 0.0894 | 0.1669 | 0.1667 | 20.0 |
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+ | No log | 8.0 | 248 | 2.3883 | 0.1967 | 0.089 | 0.1665 | 0.1664 | 20.0 |
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+ | No log | 9.0 | 279 | 2.3775 | 0.1969 | 0.0903 | 0.1672 | 0.1671 | 20.0 |
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+ | No log | 10.0 | 310 | 2.3660 | 0.1977 | 0.0913 | 0.1683 | 0.1684 | 20.0 |
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+ | No log | 11.0 | 341 | 2.3555 | 0.1976 | 0.0919 | 0.1687 | 0.1687 | 20.0 |
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+ | No log | 12.0 | 372 | 2.3491 | 0.198 | 0.092 | 0.1682 | 0.1682 | 20.0 |
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+ | No log | 13.0 | 403 | 2.3410 | 0.1987 | 0.0943 | 0.1692 | 0.1691 | 20.0 |
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+ | No log | 14.0 | 434 | 2.3360 | 0.1998 | 0.0957 | 0.1703 | 0.1702 | 20.0 |
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+ | No log | 15.0 | 465 | 2.3286 | 0.1998 | 0.0952 | 0.1706 | 0.1706 | 20.0 |
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+ | No log | 16.0 | 496 | 2.3226 | 0.1993 | 0.095 | 0.1703 | 0.1704 | 20.0 |
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+ | 2.4711 | 17.0 | 527 | 2.3194 | 0.1992 | 0.0959 | 0.1707 | 0.1707 | 20.0 |
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+ | 2.4711 | 18.0 | 558 | 2.3147 | 0.199 | 0.0958 | 0.1708 | 0.1708 | 20.0 |
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+ | 2.4711 | 19.0 | 589 | 2.3114 | 0.1987 | 0.0962 | 0.1707 | 0.1708 | 20.0 |
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+ | 2.4711 | 20.0 | 620 | 2.3076 | 0.199 | 0.0956 | 0.1704 | 0.1703 | 20.0 |
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+ | 2.4711 | 21.0 | 651 | 2.3041 | 0.1986 | 0.0963 | 0.1698 | 0.1698 | 20.0 |
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+ | 2.4711 | 22.0 | 682 | 2.3012 | 0.1993 | 0.0969 | 0.1707 | 0.1706 | 20.0 |
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+ | 2.4711 | 23.0 | 713 | 2.2982 | 0.1993 | 0.0968 | 0.1704 | 0.1704 | 20.0 |
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+ | 2.4711 | 24.0 | 744 | 2.2975 | 0.1991 | 0.0965 | 0.1704 | 0.1704 | 20.0 |
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+ | 2.4711 | 25.0 | 775 | 2.2968 | 0.1988 | 0.0965 | 0.1701 | 0.17 | 20.0 |
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+ | 2.4711 | 26.0 | 806 | 2.2951 | 0.1983 | 0.0965 | 0.1701 | 0.1699 | 20.0 |
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+ | 2.4711 | 27.0 | 837 | 2.2935 | 0.1986 | 0.0973 | 0.1704 | 0.1702 | 20.0 |
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+ | 2.4711 | 28.0 | 868 | 2.2927 | 0.1987 | 0.0971 | 0.1703 | 0.1702 | 20.0 |
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+ | 2.4711 | 29.0 | 899 | 2.2925 | 0.1987 | 0.0971 | 0.1702 | 0.1701 | 20.0 |
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+ | 2.4711 | 30.0 | 930 | 2.2923 | 0.1987 | 0.0971 | 0.1702 | 0.1701 | 20.0 |
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  ### Framework versions
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