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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: 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-2409-1947-lr-3e-06-bs-2-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-2409-1947-lr-3e-06-bs-2-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: 7.7377 |
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- Rouge/rouge1: 0.3097 |
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- Rouge/rouge2: 0.0856 |
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- Rouge/rougel: 0.2463 |
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- Rouge/rougelsum: 0.2464 |
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- Bertscore/bertscore-precision: 0.8589 |
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- Bertscore/bertscore-recall: 0.8656 |
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- Bertscore/bertscore-f1: 0.8622 |
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- Meteor: 0.2246 |
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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: 2 |
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- eval_batch_size: 2 |
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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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| 0.2033 | 1.0 | 434 | 7.5938 | 0.3097 | 0.0856 | 0.2463 | 0.2464 | 0.8589 | 0.8656 | 0.8622 | 0.2246 | 36.0 | |
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| 0.2002 | 2.0 | 868 | 7.6593 | 0.3097 | 0.0856 | 0.2463 | 0.2464 | 0.8589 | 0.8656 | 0.8622 | 0.2246 | 36.0 | |
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| 0.1987 | 3.0 | 1302 | 7.6900 | 0.3097 | 0.0856 | 0.2463 | 0.2464 | 0.8589 | 0.8656 | 0.8622 | 0.2246 | 36.0 | |
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| 0.1973 | 4.0 | 1736 | 7.7234 | 0.3097 | 0.0856 | 0.2463 | 0.2464 | 0.8589 | 0.8656 | 0.8622 | 0.2246 | 36.0 | |
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| 0.1975 | 5.0 | 2170 | 7.7388 | 0.3097 | 0.0856 | 0.2463 | 0.2464 | 0.8589 | 0.8656 | 0.8622 | 0.2246 | 36.0 | |
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| 0.2146 | 6.0 | 2604 | 7.7377 | 0.3097 | 0.0856 | 0.2463 | 0.2464 | 0.8589 | 0.8656 | 0.8622 | 0.2246 | 36.0 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0 |
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- Datasets 3.0.0 |
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- Tokenizers 0.19.1 |
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