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--- |
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license: mit |
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base_model: gogamza/kobart-summarization |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: modelling |
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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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# modelling |
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This model is a fine-tuned version of [gogamza/kobart-summarization](https://huggingface.co./gogamza/kobart-summarization) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5862 |
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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: 5.6e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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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: linear |
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- lr_scheduler_warmup_steps: 300 |
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- num_epochs: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 1.8136 | 1.42 | 500 | 0.6526 | |
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| 0.4651 | 2.85 | 1000 | 0.5862 | |
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| 0.2643 | 4.27 | 1500 | 0.6752 | |
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| 0.1642 | 5.7 | 2000 | 0.6840 | |
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| 0.1078 | 7.12 | 2500 | 0.7554 | |
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### Framework versions |
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- Transformers 4.37.1 |
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- Pytorch 2.1.2+cu118 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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