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jgibb
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t-5-base-baseline

Text2Text Generation
Transformers
TensorBoard
Safetensors
t5
Generated from Trainer
Inference Endpoints
text-generation-inference
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training completed[dev]: 512 128

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  1. README.md +21 -22
  2. all_results.json +10 -10
  3. runs/Mar31_00-46-22_93badbe1494c/events.out.tfevents.1711845988.93badbe1494c.1089.4 +2 -2
  4. test_results.json +10 -10
README.md CHANGED
@@ -18,13 +18,13 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.1785
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- - Rouge1: 0.6774
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- - Rouge2: 0.4106
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- - Rougel: 0.6163
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- - Rougelsum: 0.6162
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- - Wer: 0.4869
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- - Bleurt: 0.3779
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  ## Model description
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@@ -56,21 +56,20 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Wer | Bleurt |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:------:|:------:|
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- | No log | 0.13 | 250 | 1.3316 | 0.651 | 0.3769 | 0.5867 | 0.5868 | 0.5217 | 0.3009 |
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- | 1.7919 | 0.27 | 500 | 1.2776 | 0.6595 | 0.3865 | 0.5963 | 0.5963 | 0.5108 | 0.3009 |
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- | 1.7919 | 0.4 | 750 | 1.2513 | 0.6635 | 0.393 | 0.6015 | 0.6016 | 0.5039 | 0.3009 |
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- | 1.3552 | 0.53 | 1000 | 1.2326 | 0.6668 | 0.3967 | 0.6049 | 0.6049 | 0.5008 | 0.3009 |
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- | 1.3552 | 0.66 | 1250 | 1.2236 | 0.6692 | 0.4 | 0.6073 | 0.6073 | 0.4972 | 0.3314 |
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- | 1.3074 | 0.8 | 1500 | 1.2118 | 0.6713 | 0.4022 | 0.6094 | 0.6094 | 0.4953 | 0.3314 |
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- | 1.3074 | 0.93 | 1750 | 1.2022 | 0.6715 | 0.4035 | 0.6106 | 0.6106 | 0.4932 | 0.2798 |
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- | 1.3037 | 1.06 | 2000 | 1.1972 | 0.6731 | 0.4053 | 0.6118 | 0.6118 | 0.4916 | 0.3771 |
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- | 1.3037 | 1.2 | 2250 | 1.1909 | 0.675 | 0.4068 | 0.6137 | 0.6136 | 0.4905 | 0.3314 |
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- | 1.2676 | 1.33 | 2500 | 1.1889 | 0.6761 | 0.4087 | 0.6144 | 0.6144 | 0.4893 | 0.3314 |
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- | 1.2676 | 1.46 | 2750 | 1.1848 | 0.6763 | 0.4091 | 0.6151 | 0.6151 | 0.4884 | 0.3314 |
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- | 1.2796 | 1.6 | 3000 | 1.1829 | 0.6771 | 0.4095 | 0.6155 | 0.6155 | 0.488 | 0.3123 |
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- | 1.2796 | 1.73 | 3250 | 1.1808 | 0.6769 | 0.4101 | 0.6159 | 0.6159 | 0.4876 | 0.3779 |
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- | 1.2489 | 1.86 | 3500 | 1.1787 | 0.6772 | 0.4105 | 0.6162 | 0.6162 | 0.4869 | 0.3771 |
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- | 1.2489 | 1.99 | 3750 | 1.1785 | 0.6774 | 0.4106 | 0.6163 | 0.6162 | 0.4869 | 0.3779 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.1670
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+ - Rouge1: 0.6791
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+ - Rouge2: 0.4136
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+ - Rougel: 0.6183
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+ - Rougelsum: 0.6185
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+ - Wer: 0.4846
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+ - Bleurt: 0.3314
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Wer | Bleurt |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:------:|:------:|
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+ | No log | 0.14 | 250 | 1.3197 | 0.6548 | 0.3804 | 0.5904 | 0.5905 | 0.5188 | 0.3009 |
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+ | 1.7026 | 0.27 | 500 | 1.2676 | 0.6613 | 0.3904 | 0.5985 | 0.5987 | 0.5074 | 0.3009 |
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+ | 1.7026 | 0.41 | 750 | 1.2385 | 0.6664 | 0.3961 | 0.6043 | 0.6044 | 0.5023 | 0.3009 |
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+ | 1.3446 | 0.55 | 1000 | 1.2234 | 0.6691 | 0.4009 | 0.6075 | 0.6076 | 0.4972 | 0.3009 |
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+ | 1.3446 | 0.68 | 1250 | 1.2089 | 0.671 | 0.4031 | 0.6099 | 0.6101 | 0.4944 | 0.3009 |
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+ | 1.309 | 0.82 | 1500 | 1.1983 | 0.673 | 0.4064 | 0.6121 | 0.6123 | 0.4914 | 0.3009 |
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+ | 1.309 | 0.96 | 1750 | 1.1900 | 0.6744 | 0.4075 | 0.6136 | 0.6137 | 0.4897 | 0.3009 |
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+ | 1.2783 | 1.09 | 2000 | 1.1840 | 0.6744 | 0.4082 | 0.614 | 0.6141 | 0.4889 | 0.2798 |
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+ | 1.2783 | 1.23 | 2250 | 1.1808 | 0.6759 | 0.4102 | 0.6154 | 0.6156 | 0.4875 | 0.2944 |
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+ | 1.2683 | 1.36 | 2500 | 1.1763 | 0.6771 | 0.4111 | 0.6163 | 0.6165 | 0.4863 | 0.3153 |
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+ | 1.2683 | 1.5 | 2750 | 1.1720 | 0.6772 | 0.4115 | 0.6169 | 0.617 | 0.4859 | 0.4109 |
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+ | 1.2469 | 1.64 | 3000 | 1.1701 | 0.6783 | 0.4127 | 0.6176 | 0.6178 | 0.485 | 0.3314 |
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+ | 1.2469 | 1.77 | 3250 | 1.1680 | 0.6786 | 0.4131 | 0.6178 | 0.6179 | 0.4849 | 0.3314 |
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+ | 1.2171 | 1.91 | 3500 | 1.1670 | 0.6791 | 0.4136 | 0.6183 | 0.6185 | 0.4846 | 0.3314 |
 
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  ### Framework versions
all_results.json CHANGED
@@ -1,12 +1,12 @@
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  {
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- "test_BLEURT": -0.9706,
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- "test_WER": 0.4816,
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- "test_rouge1": 0.6823,
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- "test_rouge2": 0.4189,
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- "test_rougeL": 0.6225,
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- "test_rougeLsum": 0.6219,
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- "test_runtime": 31.876,
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- "test_samples_per_second": 11.827,
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- "test_steps_per_second": 1.976
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  }
 
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+ "test_rougeL": 0.6255,
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+ "test_rougeLsum": 0.6251,
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+ "test_runtime": 47.9856,
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+ "test_samples_per_second": 7.648,
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+ "test_steps_per_second": 1.292
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  }
runs/Mar31_00-46-22_93badbe1494c/events.out.tfevents.1711845988.93badbe1494c.1089.4 CHANGED
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test_results.json CHANGED
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- "test_loss": 1.195426344871521,
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- "test_rouge1": 0.6823,
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- "test_rouge2": 0.4189,
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- "test_rougeL": 0.6225,
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- "test_rougeLsum": 0.6219,
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- "test_runtime": 31.876,
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- "test_samples_per_second": 11.827,
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- "test_steps_per_second": 1.976
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  }
 
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  {
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+ "test_BLEURT": -0.2487,
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+ "test_WER": 0.4777,
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+ "test_loss": 1.1913501024246216,
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+ "test_rouge1": 0.6851,
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