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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-xsum |
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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: bart-large-finetuned-billsum |
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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-large-finetuned-billsum |
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This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1947 |
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- Rouge1: 35.1575 |
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- Rouge2: 27.7021 |
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- Rougel: 32.9801 |
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- Rougelsum: 33.6194 |
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- Gen Len: 31.9873 |
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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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- lr_scheduler_warmup_steps: 500 |
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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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
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|:-------------:|:------:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| |
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| 1.5279 | 0.4221 | 1000 | 1.3638 | 34.2853 | 26.1627 | 31.897 | 32.6399 | 31.999 | |
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| 1.3237 | 0.8442 | 2000 | 1.2357 | 34.7055 | 26.7936 | 32.3811 | 33.0823 | 31.9973 | |
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| 1.1594 | 1.2664 | 3000 | 1.2246 | 34.6975 | 27.0964 | 32.5326 | 33.1883 | 31.982 | |
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| 1.1029 | 1.6885 | 4000 | 1.2092 | 34.4969 | 26.9107 | 32.3644 | 33.0481 | 31.9987 | |
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| 1.0461 | 2.1106 | 5000 | 1.1769 | 35.2419 | 27.6038 | 33.0339 | 33.6849 | 31.9903 | |
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| 0.9535 | 2.5327 | 6000 | 1.1958 | 34.7138 | 27.2185 | 32.5573 | 33.2043 | 31.9947 | |
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| 0.9373 | 2.9548 | 7000 | 1.1600 | 35.1741 | 27.6199 | 32.9618 | 33.6181 | 31.9783 | |
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| 0.8506 | 3.3770 | 8000 | 1.1940 | 34.8976 | 27.4455 | 32.7581 | 33.4013 | 31.99 | |
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| 0.8341 | 3.7991 | 9000 | 1.1716 | 35.1191 | 27.6856 | 32.9822 | 33.6221 | 31.9853 | |
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| 0.8083 | 4.2212 | 10000 | 1.1916 | 35.1839 | 27.7013 | 32.995 | 33.6131 | 31.988 | |
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| 0.7749 | 4.6433 | 11000 | 1.1947 | 35.1575 | 27.7021 | 32.9801 | 33.6194 | 31.9873 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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