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--- |
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license: apache-2.0 |
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base_model: sshleifer/distilbart-xsum-12-6 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: bart-abs-1509-0313-lr-3e-06-bs-4-maxep-6 |
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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-abs-1509-0313-lr-3e-06-bs-4-maxep-6 |
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This model is a fine-tuned version of [sshleifer/distilbart-xsum-12-6](https://huggingface.co/sshleifer/distilbart-xsum-12-6) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 6.1788 |
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- Rouge/rouge1: 0.3111 |
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- Rouge/rouge2: 0.0793 |
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- Rouge/rougel: 0.2212 |
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- Rouge/rougelsum: 0.2213 |
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- Bertscore/bertscore-precision: 0.8659 |
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- Bertscore/bertscore-recall: 0.864 |
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- Bertscore/bertscore-f1: 0.8649 |
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- Meteor: 0.228 |
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- Gen Len: 36.0 |
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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: 3e-06 |
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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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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 6 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge/rouge1 | Rouge/rouge2 | Rouge/rougel | Rouge/rougelsum | Bertscore/bertscore-precision | Bertscore/bertscore-recall | Bertscore/bertscore-f1 | Meteor | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:------:|:-------:| |
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| 0.5854 | 1.0 | 217 | 5.7937 | 0.2578 | 0.0526 | 0.1861 | 0.1862 | 0.8466 | 0.8559 | 0.8512 | 0.265 | 55.0 | |
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| 0.5363 | 2.0 | 434 | 5.9473 | 0.2723 | 0.0677 | 0.226 | 0.2265 | 0.8613 | 0.855 | 0.8581 | 0.2229 | 30.0 | |
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| 0.4911 | 3.0 | 651 | 6.0403 | 0.3064 | 0.0749 | 0.2182 | 0.2185 | 0.865 | 0.8652 | 0.8651 | 0.2255 | 37.0 | |
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| 0.4651 | 4.0 | 868 | 6.1046 | 0.3064 | 0.0749 | 0.2182 | 0.2185 | 0.865 | 0.8652 | 0.8651 | 0.2255 | 37.0 | |
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| 0.4476 | 5.0 | 1085 | 6.1659 | 0.3111 | 0.0793 | 0.2212 | 0.2213 | 0.8659 | 0.864 | 0.8649 | 0.228 | 36.0 | |
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| 0.4415 | 6.0 | 1302 | 6.1788 | 0.3111 | 0.0793 | 0.2212 | 0.2213 | 0.8659 | 0.864 | 0.8649 | 0.228 | 36.0 | |
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### Framework versions |
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- Transformers 4.44.0 |
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- Pytorch 2.4.0 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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