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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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jgibb commited on
Commit
004ba6a
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1 Parent(s): 5639a9f

training completed[dev]: 512 128

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Files changed (4) hide show
  1. README.md +18 -18
  2. all_results.json +6 -6
  3. runs/Apr06_01-17-41_b7d2b20d960d/events.out.tfevents.1712366264.b7d2b20d960d.1095.0 +2 -2
  4. test_results.json +6 -6
README.md CHANGED
@@ -19,9 +19,9 @@ 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: 0.6772
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- - Rouge2: 0.4105
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- - Rougel: 0.6161
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  - Rougelsum: 0.6161
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  - Wer: 0.4869
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  - Bleurt: 0.3779
@@ -56,21 +56,21 @@ 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 | 0.6509 | 0.3768 | 0.5866 | 0.5865 | 0.5217 | 0.3009 |
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- | 1.7919 | 0.27 | 500 | 1.2776 | 0.6593 | 0.3865 | 0.5962 | 0.5962 | 0.5108 | 0.3009 |
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- | 1.7919 | 0.4 | 750 | 1.2513 | 0.6633 | 0.3931 | 0.6015 | 0.6014 | 0.5039 | 0.3009 |
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- | 1.3552 | 0.53 | 1000 | 1.2326 | 0.6667 | 0.3967 | 0.6048 | 0.6047 | 0.5008 | 0.3009 |
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- | 1.3552 | 0.66 | 1250 | 1.2236 | 0.669 | 0.4 | 0.6072 | 0.6072 | 0.4972 | 0.3314 |
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- | 1.3074 | 0.8 | 1500 | 1.2118 | 0.6711 | 0.4022 | 0.6093 | 0.6093 | 0.4953 | 0.3314 |
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- | 1.3074 | 0.93 | 1750 | 1.2022 | 0.6714 | 0.4034 | 0.6105 | 0.6104 | 0.4932 | 0.2798 |
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- | 1.3037 | 1.06 | 2000 | 1.1972 | 0.673 | 0.4053 | 0.6117 | 0.6116 | 0.4916 | 0.3771 |
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- | 1.3037 | 1.2 | 2250 | 1.1909 | 0.6749 | 0.4068 | 0.6136 | 0.6135 | 0.4905 | 0.3314 |
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- | 1.2676 | 1.33 | 2500 | 1.1889 | 0.676 | 0.4086 | 0.6143 | 0.6143 | 0.4893 | 0.3314 |
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- | 1.2676 | 1.46 | 2750 | 1.1848 | 0.6763 | 0.4091 | 0.615 | 0.6149 | 0.4884 | 0.3314 |
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- | 1.2796 | 1.6 | 3000 | 1.1829 | 0.677 | 0.4095 | 0.6154 | 0.6154 | 0.488 | 0.3123 |
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- | 1.2796 | 1.73 | 3250 | 1.1808 | 0.6767 | 0.41 | 0.6157 | 0.6157 | 0.4876 | 0.3779 |
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- | 1.2489 | 1.86 | 3500 | 1.1787 | 0.6771 | 0.4105 | 0.616 | 0.616 | 0.4869 | 0.3771 |
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- | 1.2489 | 1.99 | 3750 | 1.1785 | 0.6772 | 0.4105 | 0.6161 | 0.6161 | 0.4869 | 0.3779 |
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  ### Framework versions
 
19
  This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
20
  It achieves the following results on the evaluation set:
21
  - Loss: 1.1785
22
+ - Rouge1: 0.6774
23
+ - Rouge2: 0.4106
24
+ - Rougel: 0.6163
25
  - Rougelsum: 0.6161
26
  - Wer: 0.4869
27
  - Bleurt: 0.3779
 
56
 
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  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Wer | Bleurt |
58
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:------:|:------:|
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+ | No log | 0.13 | 250 | 1.3316 | 0.6511 | 0.3769 | 0.5868 | 0.5866 | 0.5217 | 0.3009 |
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+ | 1.7919 | 0.27 | 500 | 1.2776 | 0.6595 | 0.3866 | 0.5964 | 0.5962 | 0.5108 | 0.3009 |
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+ | 1.7919 | 0.4 | 750 | 1.2513 | 0.6635 | 0.3932 | 0.6016 | 0.6014 | 0.5039 | 0.3009 |
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+ | 1.3552 | 0.53 | 1000 | 1.2326 | 0.6668 | 0.3968 | 0.605 | 0.6048 | 0.5008 | 0.3009 |
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+ | 1.3552 | 0.66 | 1250 | 1.2236 | 0.6692 | 0.4 | 0.6073 | 0.6072 | 0.4972 | 0.3314 |
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+ | 1.3074 | 0.8 | 1500 | 1.2118 | 0.6713 | 0.4023 | 0.6094 | 0.6093 | 0.4953 | 0.3314 |
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+ | 1.3074 | 0.93 | 1750 | 1.2022 | 0.6716 | 0.4035 | 0.6106 | 0.6105 | 0.4932 | 0.2798 |
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+ | 1.3037 | 1.06 | 2000 | 1.1972 | 0.6731 | 0.4053 | 0.6118 | 0.6117 | 0.4916 | 0.3771 |
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+ | 1.3037 | 1.2 | 2250 | 1.1909 | 0.675 | 0.4069 | 0.6136 | 0.6135 | 0.4905 | 0.3314 |
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+ | 1.2676 | 1.33 | 2500 | 1.1889 | 0.6761 | 0.4087 | 0.6144 | 0.6143 | 0.4893 | 0.3314 |
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+ | 1.2676 | 1.46 | 2750 | 1.1848 | 0.6764 | 0.4091 | 0.6151 | 0.615 | 0.4884 | 0.3314 |
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+ | 1.2796 | 1.6 | 3000 | 1.1829 | 0.6771 | 0.4096 | 0.6156 | 0.6154 | 0.488 | 0.3123 |
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+ | 1.2796 | 1.73 | 3250 | 1.1808 | 0.6769 | 0.4101 | 0.6159 | 0.6158 | 0.4876 | 0.3779 |
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+ | 1.2489 | 1.86 | 3500 | 1.1787 | 0.6772 | 0.4106 | 0.6162 | 0.6161 | 0.4869 | 0.3771 |
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+ | 1.2489 | 1.99 | 3750 | 1.1785 | 0.6774 | 0.4106 | 0.6163 | 0.6161 | 0.4869 | 0.3779 |
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  ### Framework versions
all_results.json CHANGED
@@ -3,10 +3,10 @@
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  "test_WER": 0.4816,
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  "test_loss": 1.195426344871521,
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  "test_rouge1": 0.6827,
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- "test_rouge2": 0.4184,
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- "test_rougeL": 0.6228,
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- "test_rougeLsum": 0.6222,
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- "test_runtime": 31.0979,
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- "test_samples_per_second": 12.123,
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- "test_steps_per_second": 2.026
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  }
 
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  "test_WER": 0.4816,
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  "test_loss": 1.195426344871521,
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  "test_rouge1": 0.6827,
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+ "test_rouge2": 0.419,
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+ "test_rougeL": 0.6225,
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+ "test_rougeLsum": 0.6227,
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+ "test_runtime": 42.9332,
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+ "test_samples_per_second": 8.781,
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+ "test_steps_per_second": 1.467
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  }
runs/Apr06_01-17-41_b7d2b20d960d/events.out.tfevents.1712366264.b7d2b20d960d.1095.0 CHANGED
@@ -1,3 +1,3 @@
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test_results.json CHANGED
@@ -3,10 +3,10 @@
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  "test_WER": 0.4816,
4
  "test_loss": 1.195426344871521,
5
  "test_rouge1": 0.6827,
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- "test_rouge2": 0.4184,
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- "test_rougeL": 0.6228,
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- "test_rougeLsum": 0.6222,
9
- "test_runtime": 31.0979,
10
- "test_samples_per_second": 12.123,
11
- "test_steps_per_second": 2.026
12
  }
 
3
  "test_WER": 0.4816,
4
  "test_loss": 1.195426344871521,
5
  "test_rouge1": 0.6827,
6
+ "test_rouge2": 0.419,
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+ "test_rougeL": 0.6225,
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+ "test_rougeLsum": 0.6227,
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+ "test_runtime": 42.9332,
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+ "test_samples_per_second": 8.781,
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+ "test_steps_per_second": 1.467
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  }