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--- |
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library_name: transformers |
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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: amb-dataset-factor |
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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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# amb-dataset-factor |
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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: 0.8578 |
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- Rouge1: 0.6039 |
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- Rouge2: 0.3487 |
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- Rougel: 0.4805 |
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- Rougelsum: 0.4805 |
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- Gen Len: 101.0 |
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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: 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 | 1 | 1.1889 | 0.5926 | 0.2701 | 0.3828 | 0.3828 | 65.5 | |
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| No log | 2.0 | 2 | 1.0179 | 0.6489 | 0.3333 | 0.458 | 0.458 | 77.5 | |
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| No log | 3.0 | 3 | 0.9405 | 0.6084 | 0.2627 | 0.3783 | 0.3783 | 82.5 | |
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| No log | 4.0 | 4 | 0.8990 | 0.6241 | 0.3058 | 0.4054 | 0.4054 | 86.0 | |
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| No log | 5.0 | 5 | 0.8814 | 0.6746 | 0.3882 | 0.4842 | 0.4842 | 95.0 | |
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| No log | 6.0 | 6 | 0.8679 | 0.5554 | 0.3111 | 0.4127 | 0.4127 | 94.5 | |
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| No log | 7.0 | 7 | 0.8607 | 0.5799 | 0.3016 | 0.4153 | 0.4153 | 100.0 | |
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| No log | 8.0 | 8 | 0.8578 | 0.6039 | 0.3487 | 0.4805 | 0.4805 | 101.0 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.0.1 |
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- Tokenizers 0.19.1 |
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