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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: LifeMainSectionsPegasusLargeModel |
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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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# LifeMainSectionsPegasusLargeModel |
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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: 5.8222 |
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- Rouge1: 42.8874 |
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- Rouge2: 11.2364 |
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- Rougel: 27.5064 |
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- Rougelsum: 39.4117 |
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- Bertscore Precision: 77.6554 |
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- Bertscore Recall: 81.0536 |
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- Bertscore F1: 79.3129 |
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- Bleu: 0.0730 |
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- Gen Len: 227.0514 |
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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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| 6.9298 | 0.0888 | 100 | 6.6509 | 32.8681 | 7.0176 | 21.3819 | 29.8291 | 74.6005 | 79.1172 | 76.7856 | 0.0454 | 227.0514 | |
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| 6.6156 | 0.1776 | 200 | 6.3485 | 36.1792 | 8.8919 | 24.1486 | 33.0206 | 75.7824 | 79.9379 | 77.7981 | 0.0585 | 227.0514 | |
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| 6.4148 | 0.2664 | 300 | 6.2282 | 39.2098 | 10.0222 | 25.6326 | 35.6 | 76.2724 | 80.3779 | 78.2642 | 0.0655 | 227.0514 | |
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| 6.3735 | 0.3552 | 400 | 6.1269 | 39.6145 | 10.3955 | 25.9037 | 36.142 | 76.2138 | 80.4372 | 78.2611 | 0.0675 | 227.0514 | |
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| 6.2031 | 0.4440 | 500 | 6.0437 | 40.2044 | 10.3808 | 26.2646 | 36.6302 | 76.3888 | 80.5312 | 78.3982 | 0.0674 | 227.0514 | |
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| 6.1976 | 0.5328 | 600 | 5.9679 | 41.4546 | 10.6953 | 26.6781 | 37.9643 | 76.9224 | 80.7265 | 78.7724 | 0.0695 | 227.0514 | |
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| 6.1576 | 0.6216 | 700 | 5.9134 | 41.8873 | 10.8176 | 26.9451 | 38.6303 | 77.3503 | 80.8194 | 79.0412 | 0.0704 | 227.0514 | |
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| 6.123 | 0.7104 | 800 | 5.8734 | 41.4092 | 10.7374 | 26.9369 | 37.8851 | 77.0833 | 80.8538 | 78.9178 | 0.0699 | 227.0514 | |
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| 6.0612 | 0.7992 | 900 | 5.8530 | 43.3281 | 11.3695 | 27.52 | 39.7303 | 77.5853 | 81.0567 | 79.2776 | 0.0736 | 227.0514 | |
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| 6.0503 | 0.8880 | 1000 | 5.8316 | 42.7407 | 11.1329 | 27.4419 | 39.2638 | 77.6592 | 81.0404 | 79.3085 | 0.0724 | 227.0514 | |
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| 6.1028 | 0.9767 | 1100 | 5.8222 | 42.8874 | 11.2364 | 27.5064 | 39.4117 | 77.6554 | 81.0536 | 79.3129 | 0.0730 | 227.0514 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.1.2 |
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- Datasets 2.2.1 |
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- Tokenizers 0.19.1 |
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