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--- |
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license: mit |
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base_model: facebook/bart-large-cnn |
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tags: |
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- generated_from_trainer |
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metrics: |
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- rouge |
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model-index: |
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- name: ATS-Bart-Large-CNN |
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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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# ATS-Bart-Large-CNN |
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This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co./facebook/bart-large-cnn) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1044 |
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- Rouge1: 0.6648 |
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- Rouge2: 0.4542 |
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- Rougel: 0.5743 |
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- Rougelsum: 0.5743 |
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- Gen Len: 79.5693 |
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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: 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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- num_epochs: 8 |
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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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| |
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| No log | 1.0 | 274 | 0.8020 | 0.6276 | 0.3994 | 0.5208 | 0.5212 | 77.3942 | |
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| 0.7545 | 2.0 | 548 | 0.8005 | 0.6469 | 0.4278 | 0.5488 | 0.5488 | 79.8577 | |
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| 0.7545 | 3.0 | 822 | 0.8290 | 0.6533 | 0.4371 | 0.56 | 0.56 | 78.7865 | |
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| 0.3296 | 4.0 | 1096 | 0.8996 | 0.6581 | 0.4439 | 0.5636 | 0.5637 | 78.8522 | |
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| 0.3296 | 5.0 | 1370 | 0.9740 | 0.6602 | 0.4486 | 0.5644 | 0.5645 | 78.8869 | |
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| 0.1575 | 6.0 | 1644 | 1.0374 | 0.6617 | 0.4493 | 0.5689 | 0.5687 | 78.7974 | |
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| 0.1575 | 7.0 | 1918 | 1.0972 | 0.6613 | 0.4513 | 0.5706 | 0.5706 | 79.9069 | |
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| 0.0868 | 8.0 | 2192 | 1.1044 | 0.6648 | 0.4542 | 0.5743 | 0.5743 | 79.5693 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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