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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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datasets: |
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- samsum |
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metrics: |
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- rouge |
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model-index: |
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- name: pegasus-samsum |
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results: |
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- task: |
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name: Sequence-to-sequence Language Modeling |
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type: text2text-generation |
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dataset: |
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name: samsum |
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type: samsum |
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config: samsum |
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split: validation |
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args: samsum |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 0.4659 |
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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-samsum |
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This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on the samsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.4091 |
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- Rouge1: 0.4659 |
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- Rouge2: 0.2345 |
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- Rougel: 0.3946 |
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- Rougelsum: 0.3951 |
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- Gen Len: 17.7467 |
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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: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 8 |
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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: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| |
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| 1.8025 | 0.27 | 500 | 1.4403 | 0.4466 | 0.2101 | 0.3832 | 0.3841 | 21.64 | |
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| 1.5936 | 0.54 | 1000 | 1.3766 | 0.4786 | 0.2374 | 0.4017 | 0.4013 | 21.24 | |
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| 1.5926 | 0.81 | 1500 | 1.3910 | 0.5118 | 0.2643 | 0.4282 | 0.4286 | 20.2267 | |
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| 1.5067 | 1.09 | 2000 | 1.4028 | 0.4982 | 0.261 | 0.4155 | 0.4157 | 20.4267 | |
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| 1.5712 | 1.36 | 2500 | 1.4236 | 0.4712 | 0.234 | 0.3964 | 0.3969 | 17.0 | |
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| 1.6177 | 1.63 | 3000 | 1.4151 | 0.4768 | 0.2382 | 0.4019 | 0.4022 | 16.28 | |
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| 1.6289 | 1.9 | 3500 | 1.4112 | 0.4744 | 0.2346 | 0.402 | 0.4033 | 17.0267 | |
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| 1.6326 | 2.17 | 4000 | 1.4096 | 0.4682 | 0.234 | 0.3985 | 0.3994 | 17.1333 | |
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| 1.5929 | 2.44 | 4500 | 1.4093 | 0.4637 | 0.2342 | 0.3939 | 0.3942 | 17.16 | |
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| 1.4351 | 2.72 | 5000 | 1.4090 | 0.4684 | 0.2346 | 0.3953 | 0.3955 | 17.8133 | |
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| 1.6445 | 2.99 | 5500 | 1.4091 | 0.4659 | 0.2345 | 0.3946 | 0.3951 | 17.7467 | |
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### Framework versions |
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- Transformers 4.33.1 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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