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--- |
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base_model: google/pegasus-x-base |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: pegasusx-AMI-text-summarizer |
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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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# pegasusx-AMI-text-summarizer |
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This model is a fine-tuned version of [google/pegasus-x-base](https://huggingface.co/google/pegasus-x-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.9024 |
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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: 35 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 4.6836 | 0.77 | 10 | 4.2972 | |
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| 4.6013 | 1.53 | 20 | 4.1099 | |
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| 4.5902 | 2.3 | 30 | 3.9257 | |
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| 4.3479 | 3.06 | 40 | 3.8087 | |
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| 4.1995 | 3.83 | 50 | 3.6779 | |
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| 4.0121 | 4.59 | 60 | 3.5480 | |
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| 3.8925 | 5.36 | 70 | 3.4199 | |
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| 3.7548 | 6.12 | 80 | 3.2936 | |
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| 3.4644 | 6.89 | 90 | 3.1752 | |
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| 3.2484 | 7.66 | 100 | 3.0529 | |
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| 3.2456 | 8.42 | 110 | 2.9345 | |
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| 3.2281 | 9.19 | 120 | 2.8282 | |
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| 2.9944 | 9.95 | 130 | 2.7188 | |
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| 2.8439 | 10.72 | 140 | 2.6208 | |
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| 2.8192 | 11.48 | 150 | 2.5434 | |
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| 2.631 | 12.25 | 160 | 2.4852 | |
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| 2.5715 | 13.01 | 170 | 2.4277 | |
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| 2.5404 | 13.78 | 180 | 2.3876 | |
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| 2.4297 | 14.55 | 190 | 2.3507 | |
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| 2.4243 | 15.31 | 200 | 2.3110 | |
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| 2.4517 | 16.08 | 210 | 2.2733 | |
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| 2.3127 | 16.84 | 220 | 2.2454 | |
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| 2.3058 | 17.61 | 230 | 2.2127 | |
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| 2.1694 | 18.37 | 240 | 2.1808 | |
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| 2.1908 | 19.14 | 250 | 2.1532 | |
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| 2.1474 | 19.9 | 260 | 2.1234 | |
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| 2.1264 | 20.67 | 270 | 2.1139 | |
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| 2.0156 | 21.44 | 280 | 2.0933 | |
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| 2.0264 | 22.2 | 290 | 2.0611 | |
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| 2.0338 | 22.97 | 300 | 2.0448 | |
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| 2.055 | 23.73 | 310 | 2.0302 | |
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| 1.7735 | 24.5 | 320 | 2.0117 | |
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| 1.8999 | 25.26 | 330 | 2.0005 | |
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| 1.8606 | 26.03 | 340 | 1.9795 | |
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| 1.7847 | 26.79 | 350 | 1.9744 | |
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| 1.7478 | 27.56 | 360 | 1.9614 | |
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| 1.8806 | 28.33 | 370 | 1.9514 | |
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| 1.6817 | 29.09 | 380 | 1.9436 | |
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| 1.689 | 29.86 | 390 | 1.9351 | |
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| 1.649 | 30.62 | 400 | 1.9292 | |
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| 1.715 | 31.39 | 410 | 1.9181 | |
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| 1.5847 | 32.15 | 420 | 1.9077 | |
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| 1.6016 | 32.92 | 430 | 1.9112 | |
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| 1.532 | 33.68 | 440 | 1.9018 | |
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| 1.4849 | 34.45 | 450 | 1.9024 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.17.0 |
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- Tokenizers 0.15.2 |
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