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GlycerinLOL
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LLM_Teached_Pegasus_100k_FS

Text2Text Generation
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Safetensors
pegasus
generated_from_trainer
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  1. README.md +24 -20
  2. model.safetensors +1 -1
  3. runs/Mar15_23-22-32_d9n20yctr1710463501031-gcv7j/events.out.tfevents.1710516166.d9n20yctr1710463501031-gcv7j.15088.0 +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-large](https://huggingface.co/google/pegasus-large) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.4469
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- - Rouge1: 0.4939
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- - Rouge2: 0.2453
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- - Rougel: 0.4133
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- - Rougelsum: 0.4134
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- - Gen Len: 25.9629
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- - Precision: 0.9133
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- - Recall: 0.9138
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- - F1: 0.9134
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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: 96
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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: 16
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  - mixed_precision_training: Native AMP
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  ### Training results
@@ -66,16 +66,20 @@ The following hyperparameters were used during training:
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  | 1.626 | 4.0 | 2776 | 0.911 | 26.7596 | 1.5171 | 0.9102 | 0.9121 | 0.4834 | 0.2345 | 0.402 | 0.402 |
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  | 1.5918 | 5.0 | 3471 | 0.9112 | 26.6476 | 1.5001 | 0.9106 | 0.9122 | 0.4853 | 0.2365 | 0.4045 | 0.4045 |
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  | 1.5586 | 6.0 | 4164 | 0.9116 | 26.7778 | 1.4880 | 0.9108 | 0.9127 | 0.4875 | 0.2373 | 0.4063 | 0.4063 |
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- | 1.5375 | 7.0 | 4858 | 1.4768 | 0.4898 | 0.24 | 0.4083 | 0.4083 | 26.3991| 0.9116 | 0.9128 | 0.912 |
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- | 1.5146 | 8.0 | 5553 | 1.4686 | 0.4907 | 0.241 | 0.4088 | 0.4089 | 26.156 | 0.9123 | 0.9133 | 0.9126 |
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- | 1.5006 | 9.0 | 6247 | 1.4636 | 0.4914 | 0.2419 | 0.4097 | 0.4099 | 26.2629| 0.9122 | 0.9135 | 0.9127 |
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- | 1.49 | 10.0 | 6942 | 1.4580 | 0.4911 | 0.2429 | 0.4109 | 0.411 | 26.0273| 0.9125 | 0.9133 | 0.9127 |
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- | 1.4749 | 11.0 | 7636 | 1.4546 | 0.4932 | 0.244 | 0.4121 | 0.4123 | 26.2304| 0.9127 | 0.9138 | 0.9131 |
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- | 1.4661 | 12.0 | 8331 | 1.4514 | 0.4937 | 0.2448 | 0.4126 | 0.4127 | 25.8778| 0.9133 | 0.9136 | 0.9132 |
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- | 1.4575 | 13.0 | 9025 | 1.4499 | 0.4947 | 0.2453 | 0.4139 | 0.414 | 26.1151| 0.913 | 0.914 | 0.9133 |
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- | 1.4511 | 14.0 | 9720 | 1.4478 | 0.4939 | 0.2451 | 0.4133 | 0.4134 | 26.0287| 0.9131 | 0.9138 | 0.9133 |
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- | 1.4519 | 15.0 | 10414 | 1.4471 | 0.4938 | 0.2451 | 0.4134 | 0.4134 | 25.9078| 0.9132 | 0.9137 | 0.9133 |
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- | 1.4439 | 15.99 | 11104 | 1.4469 | 0.4939 | 0.2453 | 0.4133 | 0.4134 | 25.9629| 0.9133 | 0.9138 | 0.9134 |
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.4433
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+ - Rouge1: 0.4961
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+ - Rouge2: 0.2476
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+ - Rougel: 0.4155
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+ - Rougelsum: 0.4154
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+ - Gen Len: 25.8629
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+ - Precision: 0.9136
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+ - Recall: 0.914
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+ - F1: 0.9137
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  ## Model description
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  - total_train_batch_size: 96
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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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  | 1.626 | 4.0 | 2776 | 0.911 | 26.7596 | 1.5171 | 0.9102 | 0.9121 | 0.4834 | 0.2345 | 0.402 | 0.402 |
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  | 1.5918 | 5.0 | 3471 | 0.9112 | 26.6476 | 1.5001 | 0.9106 | 0.9122 | 0.4853 | 0.2365 | 0.4045 | 0.4045 |
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  | 1.5586 | 6.0 | 4164 | 0.9116 | 26.7778 | 1.4880 | 0.9108 | 0.9127 | 0.4875 | 0.2373 | 0.4063 | 0.4063 |
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+ | 1.5375 | 7.0 | 4858 | 0.912 | 26.3991 | 1.4768 | 0.9116 | 0.9128 | 0.4898 | 0.24 | 0.4083 | 0.4083 |
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+ | 1.5146 | 8.0 | 5553 | 0.9126 | 26.156 | 1.4686 | 0.9123 | 0.9133 | 0.4907 | 0.241 | 0.4088 | 0.4089 |
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+ | 1.5006 | 9.0 | 6247 | 0.9127 | 26.2629 | 1.4636 | 0.9122 | 0.9135 | 0.4914 | 0.2419 | 0.4097 | 0.4099 |
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+ | 1.49 | 10.0 | 6942 | 0.9127 | 26.0273 | 1.4580 | 0.9125 | 0.9133 | 0.4911 | 0.2429 | 0.4109 | 0.411 |
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+ | 1.4749 | 11.0 | 7636 | 0.9131 | 26.2304 | 1.4546 | 0.9127 | 0.9138 | 0.4932 | 0.244 | 0.4121 | 0.4123 |
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+ | 1.4661 | 12.0 | 8331 | 0.9132 | 25.8778 | 1.4514 | 0.9133 | 0.9136 | 0.4937 | 0.2448 | 0.4126 | 0.4127 |
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+ | 1.4575 | 13.0 | 9025 | 0.9133 | 26.1151 | 1.4499 | 0.913 | 0.914 | 0.4947 | 0.2453 | 0.4139 | 0.414 |
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+ | 1.4511 | 14.0 | 9720 | 0.9133 | 26.0287 | 1.4478 | 0.9131 | 0.9138 | 0.4939 | 0.2451 | 0.4133 | 0.4134 |
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+ | 1.4519 | 15.0 | 10414 | 0.9133 | 25.9078 | 1.4471 | 0.9132 | 0.9137 | 0.4938 | 0.2451 | 0.4134 | 0.4134 |
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+ | 1.4439 | 16.0 | 11104 | 1.4474 | 0.4942 | 0.2456 | 0.4133 | 0.4134 | 26.0345| 0.9131 | 0.9139 | 0.9133 |
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+ | 1.4441 | 17.0 | 11799 | 1.4447 | 0.4945 | 0.2457 | 0.4139 | 0.414 | 25.9391| 0.9133 | 0.9138 | 0.9134 |
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+ | 1.444 | 18.0 | 12493 | 1.4446 | 0.4957 | 0.2473 | 0.415 | 0.4151 | 26.0107| 0.9133 | 0.9141 | 0.9135 |
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+ | 1.4375 | 19.0 | 13188 | 1.4433 | 0.4961 | 0.2473 | 0.4153 | 0.4153 | 25.8869| 0.9136 | 0.914 | 0.9136 |
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+ | 1.4361 | 20.0 | 13880 | 1.4433 | 0.4961 | 0.2476 | 0.4155 | 0.4154 | 25.8629| 0.9136 | 0.914 | 0.9137 |
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  ### Framework versions
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