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--- |
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license: apache-2.0 |
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base_model: facebook/bart-large |
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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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- wer |
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model-index: |
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- name: bart_bertsum_1024_375_1000 |
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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_bertsum_1024_375_1000 |
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This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co./facebook/bart-large) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0535 |
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- Rouge1: 0.6801 |
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- Rouge2: 0.4119 |
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- Rougel: 0.6159 |
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- Rougelsum: 0.616 |
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- Wer: 0.4729 |
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- Bleurt: -0.3664 |
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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: 6 |
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- eval_batch_size: 6 |
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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: 2 |
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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 | Wer | Bleurt | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:------:|:-------:| |
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| No log | 0.13 | 250 | 1.2919 | 0.636 | 0.3519 | 0.567 | 0.567 | 0.5296 | -0.0182 | |
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| 2.2326 | 0.27 | 500 | 1.2002 | 0.6503 | 0.3707 | 0.5816 | 0.5817 | 0.5113 | -0.7073 | |
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| 2.2326 | 0.4 | 750 | 1.1735 | 0.6564 | 0.3791 | 0.5898 | 0.5898 | 0.5048 | -0.3421 | |
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| 1.2886 | 0.53 | 1000 | 1.1476 | 0.661 | 0.3843 | 0.594 | 0.5939 | 0.4994 | 0.0835 | |
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| 1.2886 | 0.66 | 1250 | 1.1289 | 0.6615 | 0.3863 | 0.5938 | 0.5938 | 0.4945 | -0.5247 | |
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| 1.2306 | 0.8 | 1500 | 1.1197 | 0.67 | 0.3952 | 0.6046 | 0.6045 | 0.4909 | -0.192 | |
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| 1.2306 | 0.93 | 1750 | 1.1077 | 0.6734 | 0.3989 | 0.6068 | 0.6067 | 0.4876 | -0.3867 | |
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| 1.1852 | 1.06 | 2000 | 1.0917 | 0.6731 | 0.4027 | 0.609 | 0.609 | 0.4833 | -0.6453 | |
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| 1.1852 | 1.2 | 2250 | 1.0852 | 0.6707 | 0.4013 | 0.6054 | 0.6054 | 0.4824 | -0.5589 | |
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| 1.0875 | 1.33 | 2500 | 1.0785 | 0.6738 | 0.4049 | 0.6096 | 0.6096 | 0.4794 | -0.5107 | |
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| 1.0875 | 1.46 | 2750 | 1.0709 | 0.6743 | 0.4046 | 0.6096 | 0.6095 | 0.478 | -0.3387 | |
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| 1.0857 | 1.6 | 3000 | 1.0627 | 0.6778 | 0.41 | 0.6137 | 0.6137 | 0.4757 | -0.4275 | |
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| 1.0857 | 1.73 | 3250 | 1.0636 | 0.675 | 0.4088 | 0.6121 | 0.612 | 0.4745 | -0.3664 | |
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| 1.0634 | 1.86 | 3500 | 1.0552 | 0.6775 | 0.4103 | 0.6136 | 0.6136 | 0.4729 | -0.3664 | |
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| 1.0634 | 1.99 | 3750 | 1.0535 | 0.6801 | 0.4119 | 0.6159 | 0.616 | 0.4729 | -0.3664 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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