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README.md
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---
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license: mit
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tags:
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- generated_from_trainer
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datasets:
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- billsum
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metrics:
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- rouge
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model-index:
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- name: bart-large-cnn-small-billsum-3epochs
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results:
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- task:
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name: Sequence-to-sequence Language Modeling
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type: text2text-generation
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dataset:
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name: billsum
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type: billsum
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config: default
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split: train
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args: default
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metrics:
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- name: Rouge1
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type: rouge
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value: 0.5409
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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-cnn-small-billsum-3epochs
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This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the billsum dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.7523
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- Rouge1: 0.5409
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- Rouge2: 0.3112
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- Rougel: 0.3929
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- Rougelsum: 0.4633
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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: 2.5764683748161164e-05
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- train_batch_size: 4
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 8
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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: 16
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- num_epochs: 3
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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 |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
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| 2.7132 | 0.32 | 8 | 2.2000 | 0.4619 | 0.2328 | 0.3201 | 0.3939 |
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| 2.236 | 0.64 | 16 | 1.9705 | 0.499 | 0.2768 | 0.3651 | 0.4216 |
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| 2.1109 | 0.96 | 24 | 1.8845 | 0.5214 | 0.2974 | 0.3844 | 0.4417 |
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| 1.7663 | 1.28 | 32 | 1.8211 | 0.5226 | 0.2935 | 0.3718 | 0.4479 |
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| 1.7838 | 1.6 | 40 | 1.7981 | 0.5338 | 0.3001 | 0.383 | 0.4466 |
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| 1.5229 | 1.92 | 48 | 1.7625 | 0.5299 | 0.3012 | 0.3839 | 0.4494 |
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| 1.5221 | 2.24 | 56 | 1.7532 | 0.5384 | 0.3117 | 0.3939 | 0.4637 |
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| 1.2879 | 2.56 | 64 | 1.7560 | 0.5338 | 0.3075 | 0.3865 | 0.4584 |
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| 1.4046 | 2.88 | 72 | 1.7523 | 0.5409 | 0.3112 | 0.3929 | 0.4633 |
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### Framework versions
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- Transformers 4.25.1
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- Pytorch 1.13.0+cu117
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- Datasets 2.7.1
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- Tokenizers 0.13.2
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