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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: facebook/bart-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- sacrebleu |
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- rouge |
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model-index: |
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- name: bart-base-finetuned-w-data-augm-4e-5 |
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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-base-finetuned-w-data-augm-4e-5 |
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This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3985 |
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- Sacrebleu: 89.8136 |
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- Rouge1: 95.6369 |
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- Rouge2: 91.8617 |
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- Rougel: 94.6909 |
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- Rougelsum: 94.6811 |
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- Bertscore Precision: 0.9424 |
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- Bertscore Recall: 0.9374 |
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- Bertscore F1: 0.9399 |
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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: 4.4252514647201465e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 32 |
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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: 5 |
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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 | Sacrebleu | Rouge1 | Rouge2 | Rougel | Rougelsum | Bertscore Precision | Bertscore Recall | Bertscore F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|:-------:|:-------:|:---------:|:-------------------:|:----------------:|:------------:| |
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| 0.1107 | 1.0 | 761 | 0.2850 | 90.5237 | 96.15 | 92.6707 | 95.2684 | 95.2821 | 0.9487 | 0.9425 | 0.9456 | |
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| 0.0435 | 2.0 | 1522 | 0.2695 | 91.4933 | 96.4613 | 93.4149 | 95.6712 | 95.6642 | 0.9515 | 0.9522 | 0.9518 | |
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| 0.0421 | 3.0 | 2283 | 0.2579 | 91.4926 | 96.4713 | 93.2669 | 95.7036 | 95.7071 | 0.9522 | 0.9505 | 0.9513 | |
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| 0.0233 | 4.0 | 3044 | 0.2717 | 91.8243 | 96.6369 | 93.443 | 95.8509 | 95.8593 | 0.9537 | 0.9521 | 0.9529 | |
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| 0.0327 | 5.0 | 3805 | 0.2804 | 92.095 | 96.6849 | 93.7485 | 95.9279 | 95.9247 | 0.9551 | 0.9526 | 0.9538 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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