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--- |
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base_model: google/pegasus-large |
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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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- bleu |
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model-index: |
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- name: ALLPrincipalPegasusLargeModel |
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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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# ALLPrincipalPegasusLargeModel |
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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: 5.0232 |
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- Rouge1: 47.6658 |
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- Rouge2: 14.3424 |
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- Rougel: 32.0036 |
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- Rougelsum: 44.3129 |
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- Bertscore Precision: 80.0039 |
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- Bertscore Recall: 82.2832 |
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- Bertscore F1: 81.1232 |
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- Bleu: 0.0968 |
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- Gen Len: 215.5775 |
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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: 5e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 16 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bertscore Precision | Bertscore Recall | Bertscore F1 | Bleu | Gen Len | |
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|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------------------:|:----------------:|:------------:|:------:|:--------:| |
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| 6.0475 | 0.1059 | 500 | 5.8195 | 40.9165 | 11.2781 | 27.4543 | 38.2464 | 77.6812 | 80.818 | 79.2126 | 0.0750 | 215.5775 | |
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| 5.7557 | 0.2118 | 1000 | 5.5171 | 44.1505 | 12.4929 | 29.115 | 41.1231 | 78.4631 | 81.3235 | 79.8626 | 0.0826 | 215.5775 | |
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| 5.5723 | 0.3178 | 1500 | 5.3613 | 45.368 | 13.0491 | 30.0119 | 42.2834 | 79.0906 | 81.6243 | 80.3329 | 0.0866 | 215.5775 | |
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| 5.5134 | 0.4237 | 2000 | 5.2571 | 45.8933 | 13.254 | 30.5132 | 42.7279 | 79.3615 | 81.82 | 80.5674 | 0.0886 | 215.5775 | |
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| 5.3793 | 0.5296 | 2500 | 5.1767 | 46.8804 | 13.7974 | 31.0723 | 43.5411 | 79.5774 | 82.0228 | 80.7769 | 0.0926 | 215.5775 | |
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| 5.4004 | 0.6355 | 3000 | 5.1101 | 46.7592 | 13.9082 | 31.3732 | 43.5105 | 79.7564 | 82.1032 | 80.9084 | 0.0934 | 215.5775 | |
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| 5.2125 | 0.7414 | 3500 | 5.0666 | 47.6221 | 14.2392 | 31.788 | 44.2768 | 79.9394 | 82.2162 | 81.0575 | 0.0954 | 215.5775 | |
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| 5.2604 | 0.8473 | 4000 | 5.0372 | 47.5699 | 14.3045 | 31.92 | 44.208 | 80.008 | 82.2777 | 81.1227 | 0.0967 | 215.5775 | |
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| 5.2959 | 0.9533 | 4500 | 5.0232 | 47.6658 | 14.3424 | 32.0036 | 44.3129 | 80.0039 | 82.2832 | 81.1232 | 0.0968 | 215.5775 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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