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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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model-index: |
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- name: pegasus-large |
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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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# pegasus-large |
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This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6537 |
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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: 3e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: polynomial |
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- lr_scheduler_warmup_steps: 500 |
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- training_steps: 20000 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| 4.7374 | 0.0 | 500 | 3.7869 | |
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| 0.6947 | 0.01 | 1000 | 0.7657 | |
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| 0.7536 | 0.01 | 1500 | 0.7410 | |
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| 0.6689 | 0.01 | 2000 | 0.7213 | |
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| 0.662 | 0.02 | 2500 | 0.7120 | |
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| 0.7537 | 0.02 | 3000 | 0.7015 | |
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| 0.79 | 0.02 | 3500 | 0.6990 | |
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| 0.6765 | 0.03 | 4000 | 0.6920 | |
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| 0.7353 | 0.03 | 4500 | 0.6881 | |
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| 0.633 | 0.03 | 5000 | 0.6858 | |
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| 0.5015 | 0.04 | 5500 | 0.6828 | |
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| 0.8227 | 0.04 | 6000 | 0.6806 | |
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| 0.832 | 0.05 | 6500 | 0.6790 | |
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| 0.587 | 0.05 | 7000 | 0.6743 | |
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| 0.6121 | 0.05 | 7500 | 0.6726 | |
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| 0.621 | 0.06 | 8000 | 0.6713 | |
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| 0.5112 | 0.06 | 8500 | 0.6714 | |
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| 0.6596 | 0.06 | 9000 | 0.6677 | |
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| 0.7421 | 0.07 | 9500 | 0.6682 | |
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| 0.5891 | 0.07 | 10000 | 0.6655 | |
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| 0.596 | 0.07 | 10500 | 0.6660 | |
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| 0.7527 | 0.08 | 11000 | 0.6639 | |
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| 0.8404 | 0.08 | 11500 | 0.6620 | |
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| 0.6896 | 0.08 | 12000 | 0.6624 | |
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| 0.7312 | 0.09 | 12500 | 0.6598 | |
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| 0.7061 | 0.09 | 13000 | 0.6591 | |
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| 0.5983 | 0.09 | 13500 | 0.6594 | |
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| 0.659 | 0.1 | 14000 | 0.6587 | |
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| 0.8656 | 0.1 | 14500 | 0.6568 | |
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| 0.7991 | 0.1 | 15000 | 0.6571 | |
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| 0.6637 | 0.11 | 15500 | 0.6571 | |
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| 0.5115 | 0.11 | 16000 | 0.6558 | |
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| 0.6464 | 0.11 | 16500 | 0.6566 | |
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| 0.6673 | 0.12 | 17000 | 0.6550 | |
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| 0.6477 | 0.12 | 17500 | 0.6544 | |
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| 0.8145 | 0.13 | 18000 | 0.6542 | |
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| 0.6216 | 0.13 | 18500 | 0.6537 | |
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| 0.943 | 0.13 | 19000 | 0.6539 | |
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| 0.788 | 0.14 | 19500 | 0.6538 | |
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| 0.5921 | 0.14 | 20000 | 0.6537 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.2.2+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.1 |
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