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--- |
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base_model: google/pegasus-xsum |
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tags: |
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- generated_from_trainer |
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metrics: |
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- rouge |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: LLM_Teached_Pegasus_100k |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# LLM_Teached_Pegasus_100k |
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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: 1.5004 |
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- Rouge1: 0.4923 |
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- Rouge2: 0.2429 |
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- Rougel: 0.4134 |
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- Rougelsum: 0.4134 |
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- Gen Len: 25.1335 |
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- Precision: 0.9143 |
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- Recall: 0.9124 |
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- F1: 0.9132 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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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: 16 |
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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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| 2.1501 | 1.0 | 781 | 0.9072 | 25.4655 | 1.7062 | 0.9082 | 0.9065 | 0.4566 | 0.209 | 0.3745 | 0.3744 | |
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| 1.7722 | 2.0 | 1562 | 0.9097 | 25.4298 | 1.6314 | 0.9107 | 0.909 | 0.4712 | 0.2226 | 0.3906 | 0.3904 | |
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| 1.7218 | 3.0 | 2343 | 0.9106 | 25.6569 | 1.5948 | 0.9112 | 0.9103 | 0.4776 | 0.2284 | 0.3965 | 0.3963 | |
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| 1.6668 | 4.0 | 3125 | 0.9112 | 25.3451 | 1.5708 | 0.9122 | 0.9107 | 0.481 | 0.2316 | 0.4002 | 0.4 | |
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| 1.6437 | 5.0 | 3906 | 0.9118 | 25.482 | 1.5565 | 0.9127 | 0.9113 | 0.4844 | 0.2346 | 0.4034 | 0.4031 | |
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| 1.6186 | 6.0 | 4687 | 0.912 | 25.4191 | 1.5476 | 0.9129 | 0.9115 | 0.4852 | 0.236 | 0.4047 | 0.4044 | |
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| 1.607 | 7.0 | 5468 | 0.9122 | 25.4949 | 1.5426 | 0.9129 | 0.9118 | 0.486 | 0.2367 | 0.4052 | 0.405 | |
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| 1.5972 | 8.0 | 6248 | 1.5380 | 0.4872 | 0.2387 | 0.407 | 0.4071 | 25.3836| 0.9131 | 0.9118 | 0.9123 | |
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| 1.5836 | 9.0 | 7029 | 1.5273 | 0.4891 | 0.2399 | 0.4088 | 0.4089 | 25.4995| 0.9133 | 0.9122 | 0.9126 | |
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| 1.5667 | 10.0 | 7810 | 1.5196 | 0.4906 | 0.2416 | 0.411 | 0.4112 | 25.3867| 0.9135 | 0.9123 | 0.9127 | |
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| 1.5521 | 11.0 | 8592 | 1.5124 | 0.4899 | 0.2406 | 0.4102 | 0.4103 | 25.2191| 0.9137 | 0.912 | 0.9127 | |
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| 1.5413 | 12.0 | 9373 | 1.5083 | 0.4914 | 0.2416 | 0.4118 | 0.412 | 25.3491| 0.9137 | 0.9123 | 0.9128 | |
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| 1.5291 | 13.0 | 10154 | 1.5044 | 0.4913 | 0.2419 | 0.4118 | 0.4119 | 25.2082| 0.914 | 0.9123 | 0.913 | |
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| 1.527 | 14.0 | 10935 | 1.5026 | 0.4917 | 0.2426 | 0.4126 | 0.4128 | 25.1069| 0.9141 | 0.9123 | 0.913 | |
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| 1.5203 | 15.0 | 11717 | 1.5006 | 0.4921 | 0.243 | 0.4135 | 0.4136 | 25.1062| 0.9143 | 0.9123 | 0.9131 | |
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| 1.5126 | 16.0 | 12496 | 1.5004 | 0.4923 | 0.2429 | 0.4134 | 0.4134 | 25.1335| 0.9143 | 0.9124 | 0.9132 | |
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### Framework versions |
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- Transformers 4.36.0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.14.5 |
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- Tokenizers 0.15.0 |
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