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GlycerinLOL
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LLM_Teached_Pegasus_50k

Text2Text Generation
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Safetensors
pegasus
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  2. model.safetensors +1 -1
  3. runs/Mar03_16-13-29_oi5vv8ctr1709312124223-tkfr5/events.out.tfevents.1709453623.oi5vv8ctr1709312124223-tkfr5.2179.0 +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: 1.6258
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- - Rouge1: 0.4708
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- - Rouge2: 0.2214
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- - Rougel: 0.3861
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- - Rougelsum: 0.3863
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- - Gen Len: 26.5411
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- - Precision: 0.9108
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- - Recall: 0.9093
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- - F1: 0.9099
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  ## Model description
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@@ -54,33 +54,43 @@ 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: 20
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  - mixed_precision_training: Native AMP
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  ### Training results
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- | Training Loss | Epoch | Step | F1 | Gen Len | Validation Loss | Precision | Recall | Rouge1 | Rouge2 | Rougel | Rougelsum |
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- |:-------------:|:-----:|:----:|:------:|:-------:|:---------------:|:---------:|:------:|:------:|:------:|:------:|:---------:|
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- | No log | 1.0 | 390 | 0.9034 | 26.2967 | 1.8258 | 0.9049 | 0.9023 | 0.4338 | 0.1906 | 0.3496 | 0.3498 |
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- | 2.1621 | 2.0 | 781 | 0.9054 | 26.2727 | 1.7537 | 0.9068 | 0.9044 | 0.4449 | 0.2005 | 0.3633 | 0.3633 |
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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 | 0.9083 | 26.6204 | 1.6763 | 0.9094 | 0.9076 | 0.4621 | 0.2133 | 0.3791 | 0.3789 |
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- | 1.7559 | 9.0 | 3511 | 0.9086 | 26.424 | 1.6662 | 0.9098 | 0.9078 | 0.4632 | 0.215 | 0.38 | 0.3799 |
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- | 1.7559 | 10.0 | 3902 | 0.9089 | 26.5425 | 1.6594 | 0.9099 | 0.9082 | 0.4651 | 0.2168 | 0.3812 | 0.3812 |
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- | 1.7357 | 11.0 | 4293 | 0.9091 | 26.6051 | 1.6555 | 0.91 | 0.9086 | 0.4663 | 0.2178 | 0.3824 | 0.3823 |
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- | 1.7297 | 12.0 | 4680 | 0.9092 | 26.4393 | 1.6508 | 0.9103 | 0.9084 | 0.4668 | 0.2175 | 0.3823 | 0.3822 |
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- | 1.7165 | 13.0 | 5071 | 0.9094 | 26.6385 | 1.6451 | 0.9103 | 0.9089 | 0.4687 | 0.2191 | 0.3834 | 0.3834 |
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- | 1.7165 | 14.0 | 5462 | 0.9095 | 26.4156 | 1.6405 | 0.9106 | 0.9087 | 0.4691 | 0.2193 | 0.3845 | 0.3844 |
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- | 1.7068 | 15.0 | 5853 | 0.9097 | 26.4571 | 1.6383 | 0.9108 | 0.9089 | 0.4699 | 0.2204 | 0.3853 | 0.3853 |
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- | 1.7004 | 16.0 | 6240 | 1.6346 | 0.4703 | 0.2204 | 0.385 | 0.385 | 26.4247| 0.9108 | 0.9089 | 0.9097 |
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- | 1.6923 | 17.0 | 6631 | 1.6305 | 0.4706 | 0.221 | 0.3855 | 0.3856 | 26.4436| 0.911 | 0.9091 | 0.9099 |
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- | 1.6839 | 18.0 | 7022 | 1.6285 | 0.4712 | 0.2215 | 0.3862 | 0.3864 | 26.612 | 0.9106 | 0.9094 | 0.9098 |
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- | 1.6839 | 19.0 | 7413 | 1.6263 | 0.4709 | 0.2217 | 0.3862 | 0.3864 | 26.5291| 0.9108 | 0.9093 | 0.9099 |
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- | 1.6743 | 19.99 | 7800 | 1.6258 | 0.4708 | 0.2214 | 0.3861 | 0.3863 | 26.5411| 0.9108 | 0.9093 | 0.9099 |
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
19
 
20
  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: 1.5934
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+ - Rouge1: 0.4774
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+ - Rouge2: 0.2259
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+ - Rougel: 0.3926
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+ - Rougelsum: 0.3926
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+ - Gen Len: 26.5556
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+ - Precision: 0.9117
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+ - Recall: 0.9103
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+ - F1: 0.9108
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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: 30
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  - mixed_precision_training: Native AMP
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  ### Training results
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+ | Training Loss | Epoch | Step | F1 | Gen Len | Validation Loss | Precision | Recall | Rouge1 | Rouge2 | Rougel | Rougelsum |
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+ |:-------------:|:-----:|:-----:|:------:|:-------:|:---------------:|:---------:|:------:|:------:|:------:|:------:|:---------:|
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+ | No log | 1.0 | 390 | 0.9034 | 26.2967 | 1.8258 | 0.9049 | 0.9023 | 0.4338 | 0.1906 | 0.3496 | 0.3498 |
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+ | 2.1621 | 2.0 | 781 | 0.9054 | 26.2727 | 1.7537 | 0.9068 | 0.9044 | 0.4449 | 0.2005 | 0.3633 | 0.3633 |
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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 | 0.9083 | 26.6204 | 1.6763 | 0.9094 | 0.9076 | 0.4621 | 0.2133 | 0.3791 | 0.3789 |
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+ | 1.7559 | 9.0 | 3511 | 0.9086 | 26.424 | 1.6662 | 0.9098 | 0.9078 | 0.4632 | 0.215 | 0.38 | 0.3799 |
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+ | 1.7559 | 10.0 | 3902 | 0.9089 | 26.5425 | 1.6594 | 0.9099 | 0.9082 | 0.4651 | 0.2168 | 0.3812 | 0.3812 |
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+ | 1.7357 | 11.0 | 4293 | 0.9091 | 26.6051 | 1.6555 | 0.91 | 0.9086 | 0.4663 | 0.2178 | 0.3824 | 0.3823 |
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+ | 1.7297 | 12.0 | 4680 | 0.9092 | 26.4393 | 1.6508 | 0.9103 | 0.9084 | 0.4668 | 0.2175 | 0.3823 | 0.3822 |
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+ | 1.7165 | 13.0 | 5071 | 0.9094 | 26.6385 | 1.6451 | 0.9103 | 0.9089 | 0.4687 | 0.2191 | 0.3834 | 0.3834 |
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+ | 1.7165 | 14.0 | 5462 | 0.9095 | 26.4156 | 1.6405 | 0.9106 | 0.9087 | 0.4691 | 0.2193 | 0.3845 | 0.3844 |
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+ | 1.7068 | 15.0 | 5853 | 0.9097 | 26.4571 | 1.6383 | 0.9108 | 0.9089 | 0.4699 | 0.2204 | 0.3853 | 0.3853 |
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+ | 1.7004 | 16.0 | 6240 | 0.9097 | 26.4247 | 1.6346 | 0.9108 | 0.9089 | 0.4703 | 0.2204 | 0.385 | 0.385 |
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+ | 1.6923 | 17.0 | 6631 | 0.9099 | 26.4436 | 1.6305 | 0.911 | 0.9091 | 0.4706 | 0.221 | 0.3855 | 0.3856 |
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+ | 1.6839 | 18.0 | 7022 | 0.9098 | 26.612 | 1.6285 | 0.9106 | 0.9094 | 0.4712 | 0.2215 | 0.3862 | 0.3864 |
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+ | 1.6839 | 19.0 | 7413 | 0.9099 | 26.5291 | 1.6263 | 0.9108 | 0.9093 | 0.4709 | 0.2217 | 0.3862 | 0.3864 |
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+ | 1.6743 | 20.0 | 7800 | 0.91 | 26.4251 | 1.6205 | 0.9111 | 0.9092 | 0.4727 | 0.2223 | 0.3876 | 0.3876 |
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+ | 1.6692 | 21.0 | 8191 | 0.9102 | 26.7484 | 1.6153 | 0.911 | 0.9098 | 0.4737 | 0.2229 | 0.388 | 0.388 |
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+ | 1.6568 | 22.0 | 8582 | 0.9103 | 26.532 | 1.6104 | 0.9113 | 0.9096 | 0.4733 | 0.2221 | 0.3885 | 0.3886 |
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+ | 1.6568 | 23.0 | 8973 | 0.9104 | 26.82 | 1.6056 | 0.911 | 0.9101 | 0.4756 | 0.2236 | 0.3891 | 0.3891 |
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+ | 1.6418 | 24.0 | 9360 | 1.6021 | 0.476 | 0.2246 | 0.3903 | 0.3903 | 26.5513| 0.9115 | 0.91 | 0.9106 |
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+ | 1.6319 | 25.0 | 9751 | 1.5995 | 0.4751 | 0.2245 | 0.3905 | 0.3905 | 26.4375| 0.9116 | 0.9098 | 0.9105 |
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+ | 1.624 | 26.0 | 10142 | 1.5974 | 0.4756 | 0.2247 | 0.3903 | 0.3904 | 26.6018| 0.9116 | 0.9101 | 0.9107 |
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+ | 1.6184 | 27.0 | 10533 | 1.5953 | 0.4747 | 0.2231 | 0.3899 | 0.3899 | 26.4833| 0.9116 | 0.9099 | 0.9106 |
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+ | 1.6184 | 28.0 | 10923 | 1.5943 | 0.4758 | 0.2243 | 0.3907 | 0.3908 | 26.5604| 0.9116 | 0.9102 | 0.9107 |
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+ | 1.6126 | 29.0 | 11314 | 1.5936 | 0.4776 | 0.226 | 0.3926 | 0.3926 | 26.5775| 0.9117 | 0.9103 | 0.9108 |
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+ | 1.6148 | 29.99 | 11700 | 1.5934 | 0.4774 | 0.2259 | 0.3926 | 0.3926 | 26.5556| 0.9117 | 0.9103 | 0.9108 |
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  ### Framework versions
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