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BlueBeagle
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t5-small-finetuned-xsum

Text2Text Generation
Transformers
PyTorch
t5
Generated from Trainer
text-generation-inference
Inference Endpoints
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End of training

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  1. README.md +75 -19
  2. pytorch_model.bin +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -17,12 +17,12 @@ should probably proofread and complete it, then remove this comment. -->
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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: 2.4349
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- - Rouge1: 6.8457
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- - Rouge2: 2.2624
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- - Rougel: 6.3387
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- - Rougelsum: 6.3042
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- - Gen Len: 16.5
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  ## Model description
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@@ -42,25 +42,81 @@ More information needed
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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: 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: 8
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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 | 4 | 3.0927 | 6.1515 | 0.8403 | 5.7746 | 5.7954 | 17.9118 |
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- | No log | 2.0 | 8 | 2.9197 | 6.5077 | 2.1008 | 6.0699 | 6.0465 | 17.6471 |
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- | No log | 3.0 | 12 | 2.7743 | 10.9042 | 6.3025 | 10.4432 | 10.3367 | 17.5882 |
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- | No log | 4.0 | 16 | 2.6619 | 10.4883 | 6.3025 | 9.8383 | 9.822 | 16.7059 |
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- | No log | 5.0 | 20 | 2.5892 | 6.8457 | 2.2624 | 6.3387 | 6.3042 | 16.5294 |
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- | No log | 6.0 | 24 | 2.5140 | 6.8457 | 2.2624 | 6.3387 | 6.3042 | 16.5 |
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- | No log | 7.0 | 28 | 2.4658 | 6.8457 | 2.2624 | 6.3387 | 6.3042 | 16.5 |
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- | No log | 8.0 | 32 | 2.4349 | 6.8457 | 2.2624 | 6.3387 | 6.3042 | 16.5 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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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: 1.2337
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+ - Rouge1: 53.8111
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+ - Rouge2: 48.12
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+ - Rougel: 53.2346
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+ - Rougelsum: 53.7215
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+ - Gen Len: 14.8824
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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: 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: 64
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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 | 2 | 2.3643 | 7.1906 | 2.2624 | 6.694 | 6.615 | 16.5 |
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+ | No log | 2.0 | 4 | 2.3231 | 6.7807 | 2.2624 | 6.3002 | 6.2663 | 16.3824 |
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+ | No log | 3.0 | 6 | 2.2494 | 13.1859 | 9.2308 | 12.8335 | 12.7109 | 16.2647 |
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+ | No log | 4.0 | 8 | 2.1885 | 12.7975 | 9.2308 | 12.8564 | 12.7446 | 16.1765 |
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+ | No log | 5.0 | 10 | 2.1366 | 16.066 | 11.7324 | 15.8326 | 15.8873 | 16.1176 |
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+ | No log | 6.0 | 12 | 2.0779 | 16.066 | 11.7324 | 15.8326 | 15.8873 | 16.1471 |
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+ | No log | 7.0 | 14 | 2.0315 | 16.3696 | 12.1611 | 16.2055 | 16.2766 | 16.3235 |
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+ | No log | 8.0 | 16 | 1.9897 | 16.3696 | 12.1611 | 16.2055 | 16.2766 | 16.1765 |
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+ | No log | 9.0 | 18 | 1.9518 | 16.3696 | 12.1611 | 16.2055 | 16.2766 | 16.2353 |
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+ | No log | 10.0 | 20 | 1.9328 | 16.3696 | 12.1611 | 16.2055 | 16.2766 | 16.2353 |
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+ | No log | 11.0 | 22 | 1.8989 | 16.6256 | 12.4154 | 16.4518 | 16.3985 | 16.1176 |
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+ | No log | 12.0 | 24 | 1.8665 | 17.121 | 12.4154 | 16.7837 | 16.8028 | 16.1176 |
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+ | No log | 13.0 | 26 | 1.8327 | 19.2998 | 14.1 | 19.1944 | 19.058 | 15.8529 |
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+ | No log | 14.0 | 28 | 1.7979 | 21.842 | 16.546 | 21.7303 | 21.6127 | 15.7647 |
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+ | No log | 15.0 | 30 | 1.7690 | 21.9626 | 16.546 | 21.7948 | 21.7027 | 15.7647 |
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+ | No log | 16.0 | 32 | 1.7449 | 21.9626 | 16.546 | 21.7948 | 21.7027 | 15.7647 |
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+ | No log | 17.0 | 34 | 1.7193 | 22.3102 | 16.546 | 22.0997 | 22.0562 | 15.7647 |
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+ | No log | 18.0 | 36 | 1.6982 | 24.848 | 19.412 | 24.7592 | 24.7737 | 15.6765 |
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+ | No log | 19.0 | 38 | 1.6780 | 24.848 | 19.412 | 24.7592 | 24.7737 | 15.6176 |
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+ | No log | 20.0 | 40 | 1.6577 | 27.0829 | 21.4796 | 27.1757 | 26.989 | 15.6765 |
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+ | No log | 21.0 | 42 | 1.6375 | 27.1459 | 21.4796 | 27.2292 | 27.0625 | 15.6471 |
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+ | No log | 22.0 | 44 | 1.6175 | 27.1459 | 21.4796 | 27.2292 | 27.0625 | 15.3235 |
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+ | No log | 23.0 | 46 | 1.5991 | 27.1459 | 21.4796 | 27.2292 | 27.0625 | 15.3235 |
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+ | No log | 24.0 | 48 | 1.5823 | 29.4051 | 23.5934 | 29.8009 | 29.3756 | 16.4412 |
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+ | No log | 25.0 | 50 | 1.5651 | 29.4051 | 23.5934 | 29.8009 | 29.3756 | 16.4412 |
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+ | No log | 26.0 | 52 | 1.5493 | 29.4051 | 23.5934 | 29.8009 | 29.3756 | 16.4412 |
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+ | No log | 27.0 | 54 | 1.5326 | 29.7971 | 23.5934 | 30.2055 | 29.6775 | 16.4118 |
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+ | No log | 28.0 | 56 | 1.5166 | 29.7971 | 23.5934 | 30.2055 | 29.6775 | 16.4118 |
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+ | No log | 29.0 | 58 | 1.5012 | 29.2529 | 23.024 | 29.3423 | 28.8761 | 16.2353 |
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+ | No log | 30.0 | 60 | 1.4866 | 29.3066 | 23.024 | 29.3532 | 28.9278 | 16.1765 |
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+ | No log | 31.0 | 62 | 1.4715 | 34.2571 | 28.0288 | 34.5418 | 34.2628 | 15.6471 |
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+ | No log | 32.0 | 64 | 1.4554 | 34.6223 | 28.0288 | 34.7957 | 34.6712 | 15.6176 |
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+ | No log | 33.0 | 66 | 1.4396 | 34.1994 | 28.0288 | 34.3535 | 34.1686 | 15.5 |
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+ | No log | 34.0 | 68 | 1.4239 | 34.8089 | 28.6815 | 34.9115 | 34.9247 | 15.4706 |
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+ | No log | 35.0 | 70 | 1.4105 | 36.6837 | 30.6181 | 36.6982 | 36.5407 | 15.2647 |
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+ | No log | 36.0 | 72 | 1.4044 | 36.6837 | 30.6181 | 36.6982 | 36.5407 | 15.2647 |
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+ | No log | 37.0 | 74 | 1.3915 | 41.2554 | 35.9199 | 41.138 | 41.3591 | 15.1765 |
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+ | No log | 38.0 | 76 | 1.3779 | 41.2554 | 35.9199 | 41.1075 | 41.3158 | 15.2059 |
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+ | No log | 39.0 | 78 | 1.3646 | 41.2877 | 35.9199 | 41.12 | 41.3654 | 14.9412 |
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+ | No log | 40.0 | 80 | 1.3518 | 41.3288 | 35.9199 | 41.2095 | 41.4213 | 14.9118 |
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+ | No log | 41.0 | 82 | 1.3399 | 41.6286 | 36.3194 | 41.6244 | 41.9893 | 14.8824 |
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+ | No log | 42.0 | 84 | 1.3282 | 41.6286 | 36.3194 | 41.6244 | 41.9893 | 14.8824 |
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+ | No log | 43.0 | 86 | 1.3168 | 42.802 | 38.3443 | 43.026 | 43.1784 | 14.6176 |
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+ | No log | 44.0 | 88 | 1.3062 | 42.802 | 38.3443 | 43.026 | 43.1784 | 14.8529 |
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+ | No log | 45.0 | 90 | 1.2971 | 44.3698 | 39.6695 | 44.3145 | 44.7449 | 14.6471 |
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+ | No log | 46.0 | 92 | 1.2884 | 47.2079 | 41.807 | 46.9035 | 47.806 | 15.0294 |
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+ | No log | 47.0 | 94 | 1.2814 | 48.006 | 42.6861 | 47.6281 | 48.5116 | 14.8529 |
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+ | No log | 48.0 | 96 | 1.2753 | 48.6509 | 42.6861 | 48.2044 | 48.9901 | 15.0 |
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+ | No log | 49.0 | 98 | 1.2693 | 48.6509 | 42.6861 | 48.2044 | 48.9901 | 15.0 |
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+ | No log | 50.0 | 100 | 1.2643 | 50.6717 | 45.4929 | 50.5974 | 50.8919 | 14.8529 |
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+ | No log | 51.0 | 102 | 1.2603 | 51.3096 | 45.4929 | 51.1016 | 51.4281 | 15.0 |
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+ | No log | 52.0 | 104 | 1.2566 | 51.3096 | 45.4929 | 51.1016 | 51.4281 | 15.0588 |
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+ | No log | 53.0 | 106 | 1.2534 | 52.4682 | 47.1163 | 52.2336 | 52.3783 | 14.9706 |
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+ | No log | 54.0 | 108 | 1.2498 | 52.4682 | 47.1163 | 52.2336 | 52.3783 | 14.9706 |
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+ | No log | 55.0 | 110 | 1.2465 | 52.4682 | 47.1163 | 52.2336 | 52.3783 | 14.9706 |
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+ | No log | 56.0 | 112 | 1.2440 | 53.5647 | 48.4619 | 53.5119 | 53.4541 | 14.7353 |
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+ | No log | 57.0 | 114 | 1.2417 | 53.8111 | 48.12 | 53.2346 | 53.7215 | 14.8824 |
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+ | No log | 58.0 | 116 | 1.2398 | 53.8111 | 48.12 | 53.2346 | 53.7215 | 14.8824 |
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+ | No log | 59.0 | 118 | 1.2383 | 53.8111 | 48.12 | 53.2346 | 53.7215 | 14.8824 |
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+ | No log | 60.0 | 120 | 1.2368 | 53.8111 | 48.12 | 53.2346 | 53.7215 | 14.8824 |
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+ | No log | 61.0 | 122 | 1.2360 | 53.8111 | 48.12 | 53.2346 | 53.7215 | 14.8824 |
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+ | No log | 62.0 | 124 | 1.2350 | 53.8111 | 48.12 | 53.2346 | 53.7215 | 14.8824 |
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+ | No log | 63.0 | 126 | 1.2343 | 53.8111 | 48.12 | 53.2346 | 53.7215 | 14.8824 |
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+ | No log | 64.0 | 128 | 1.2337 | 53.8111 | 48.12 | 53.2346 | 53.7215 | 14.8824 |
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  ### Framework versions
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