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GlycerinLOL
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LLM_Teached_Pegasus_50k

Text2Text Generation
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pegasus
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  1. README.md +17 -13
  2. model.safetensors +1 -1
  3. runs/Mar02_18-05-03_oi5vv8ctr1709312124223-tkfr5/events.out.tfevents.1709373919.oi5vv8ctr1709312124223-tkfr5.10616.0 +2 -2
README.md CHANGED
@@ -19,15 +19,15 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [google/pegasus-xsum](https://huggingface.co/google/pegasus-xsum) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.6796
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- - Rouge1: 0.4613
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- - Rouge2: 0.2127
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- - Rougel: 0.3775
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- - Rougelsum: 0.3772
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- - Gen Len: 26.4655
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- - Precision: 0.9092
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- - Recall: 0.9073
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- - F1: 0.9081
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  ## Model description
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@@ -54,7 +54,7 @@ The following hyperparameters were used during training:
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  - total_train_batch_size: 128
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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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  - mixed_precision_training: Native AMP
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  ### Training results
@@ -66,9 +66,13 @@ The following hyperparameters were used during training:
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  | 1.8794 | 3.0 | 1172 | 0.9066 | 26.4345 | 1.7268 | 0.9078 | 0.9058 | 0.4518 | 0.2061 | 0.3696 | 0.3695 |
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  | 1.8271 | 4.0 | 1560 | 0.9069 | 26.3971 | 1.7157 | 0.9082 | 0.906 | 0.4539 | 0.2075 | 0.3716 | 0.3714 |
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  | 1.8271 | 5.0 | 1951 | 0.9074 | 26.3015 | 1.7033 | 0.9087 | 0.9065 | 0.4561 | 0.2098 | 0.3735 | 0.3734 |
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- | 1.8067 | 6.0 | 2340 | 1.6897 | 0.4592 | 0.2114 | 0.3762 | 0.3759 | 26.4389| 0.9089 | 0.9069 | 0.9077 |
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- | 1.7833 | 7.0 | 2731 | 1.6819 | 0.4598 | 0.2115 | 0.3764 | 0.376 | 26.3745| 0.9092 | 0.9071 | 0.9079 |
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- | 1.7683 | 7.99 | 3120 | 1.6796 | 0.4613 | 0.2127 | 0.3775 | 0.3772 | 26.4655| 0.9092 | 0.9073 | 0.9081 |
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [google/pegasus-xsum](https://huggingface.co/google/pegasus-xsum) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.6541
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+ - Rouge1: 0.4665
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+ - Rouge2: 0.2182
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+ - Rougel: 0.3824
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+ - Rougelsum: 0.3824
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+ - Gen Len: 26.5458
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+ - Precision: 0.9101
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+ - Recall: 0.9085
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+ - F1: 0.9092
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  ## Model description
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  - total_train_batch_size: 128
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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: 12
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  - mixed_precision_training: Native AMP
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  ### Training results
 
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  | 1.8794 | 3.0 | 1172 | 0.9066 | 26.4345 | 1.7268 | 0.9078 | 0.9058 | 0.4518 | 0.2061 | 0.3696 | 0.3695 |
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  | 1.8271 | 4.0 | 1560 | 0.9069 | 26.3971 | 1.7157 | 0.9082 | 0.906 | 0.4539 | 0.2075 | 0.3716 | 0.3714 |
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  | 1.8271 | 5.0 | 1951 | 0.9074 | 26.3015 | 1.7033 | 0.9087 | 0.9065 | 0.4561 | 0.2098 | 0.3735 | 0.3734 |
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+ | 1.8067 | 6.0 | 2340 | 0.9077 | 26.4389 | 1.6897 | 0.9089 | 0.9069 | 0.4592 | 0.2114 | 0.3762 | 0.3759 |
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+ | 1.7833 | 7.0 | 2731 | 0.9079 | 26.3745 | 1.6819 | 0.9092 | 0.9071 | 0.4598 | 0.2115 | 0.3764 | 0.376 |
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+ | 1.7683 | 8.0 | 3120 | 1.6763 | 0.4621 | 0.2133 | 0.3791 | 0.3789 | 26.6204| 0.9094 | 0.9076 | 0.9083 |
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+ | 1.7559 | 9.0 | 3511 | 1.6662 | 0.4632 | 0.215 | 0.38 | 0.3799 | 26.424 | 0.9098 | 0.9078 | 0.9086 |
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+ | 1.7559 | 10.0 | 3902 | 1.6594 | 0.4651 | 0.2168 | 0.3812 | 0.3812 | 26.5425| 0.9099 | 0.9082 | 0.9089 |
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+ | 1.7357 | 11.0 | 4293 | 1.6555 | 0.4663 | 0.2178 | 0.3824 | 0.3823 | 26.6051| 0.91 | 0.9086 | 0.9091 |
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+ | 1.7297 | 11.99 | 4680 | 1.6541 | 0.4665 | 0.2182 | 0.3824 | 0.3824 | 26.5458| 0.9101 | 0.9085 | 0.9092 |
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  ### Framework versions
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