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--- |
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license: apache-2.0 |
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base_model: ainize/bart-base-cnn |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: bart-samsum |
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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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# bart-samsum |
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This model is a fine-tuned version of [ainize/bart-base-cnn](https://huggingface.co/ainize/bart-base-cnn) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.4587 |
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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: 15 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| 1.2901 | 0.64 | 500 | 1.2203 | |
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| 1.2057 | 1.28 | 1000 | 1.1384 | |
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| 1.1364 | 1.93 | 1500 | 1.1225 | |
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| 0.9711 | 2.57 | 2000 | 1.1362 | |
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| 0.786 | 3.21 | 2500 | 1.1461 | |
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| 0.818 | 3.85 | 3000 | 1.1298 | |
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| 0.7135 | 4.49 | 3500 | 1.1666 | |
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| 0.6222 | 5.14 | 4000 | 1.2114 | |
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| 0.64 | 5.78 | 4500 | 1.2103 | |
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| 0.5272 | 6.42 | 5000 | 1.2571 | |
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| 0.5057 | 7.06 | 5500 | 1.2963 | |
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| 0.4917 | 7.7 | 6000 | 1.2937 | |
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| 0.4291 | 8.35 | 6500 | 1.3286 | |
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| 0.4171 | 8.99 | 7000 | 1.3125 | |
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| 0.418 | 9.63 | 7500 | 1.3516 | |
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| 0.3576 | 10.27 | 8000 | 1.3778 | |
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| 0.3736 | 10.91 | 8500 | 1.3847 | |
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| 0.3443 | 11.56 | 9000 | 1.4215 | |
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| 0.2952 | 12.2 | 9500 | 1.4324 | |
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| 0.3236 | 12.84 | 10000 | 1.4355 | |
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| 0.2978 | 13.48 | 10500 | 1.4473 | |
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| 0.2828 | 14.13 | 11000 | 1.4557 | |
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| 0.304 | 14.77 | 11500 | 1.4587 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.13.0 |
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- Tokenizers 0.13.3 |
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