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--- |
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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-1509-0313-lr-3e-05-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-1509-0313-lr-3e-05-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: 3.3486 |
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- Rouge/rouge1: 0.4705 |
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- Rouge/rouge2: 0.2108 |
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- Rouge/rougel: 0.3877 |
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- Rouge/rougelsum: 0.3894 |
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- Bertscore/bertscore-precision: 0.8936 |
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- Bertscore/bertscore-recall: 0.8936 |
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- Bertscore/bertscore-f1: 0.8934 |
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- Meteor: 0.4277 |
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- Gen Len: 38.4091 |
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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-05 |
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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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| 1.1204 | 1.0 | 434 | 2.2199 | 0.4542 | 0.2173 | 0.3843 | 0.3855 | 0.8945 | 0.8893 | 0.8917 | 0.4072 | 37.2273 | |
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| 0.8222 | 2.0 | 868 | 2.3549 | 0.4613 | 0.2095 | 0.3935 | 0.3957 | 0.8994 | 0.8929 | 0.896 | 0.4089 | 36.8818 | |
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| 0.565 | 3.0 | 1302 | 2.6652 | 0.4686 | 0.2079 | 0.3905 | 0.3911 | 0.8943 | 0.8941 | 0.894 | 0.4207 | 39.6636 | |
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| 0.379 | 4.0 | 1736 | 2.9239 | 0.4614 | 0.2076 | 0.3937 | 0.3951 | 0.8962 | 0.8898 | 0.8928 | 0.401 | 34.8545 | |
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| 0.2543 | 5.0 | 2170 | 3.1849 | 0.4629 | 0.2086 | 0.3988 | 0.3998 | 0.8958 | 0.8914 | 0.8935 | 0.4076 | 36.1091 | |
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| 0.1761 | 6.0 | 2604 | 3.3486 | 0.4705 | 0.2108 | 0.3877 | 0.3894 | 0.8936 | 0.8936 | 0.8934 | 0.4277 | 38.4091 | |
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### Framework versions |
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- Transformers 4.44.0 |
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- Pytorch 2.4.0 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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