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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-5epochs
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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[:1%]
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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: 0.5406
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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-5epochs
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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: 1.7206
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- Rouge1: 0.5406
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- Rouge2: 0.312
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- Rougel: 0.3945
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- Rougelsum: 0.4566
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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: 3.373e-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: 4
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- total_train_batch_size: 16
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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: 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 |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
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| 2.3723 | 1.33 | 16 | 1.8534 | 0.5204 | 0.299 | 0.3893 | 0.4441 |
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| 1.6579 | 2.67 | 32 | 1.7208 | 0.5427 | 0.3143 | 0.3915 | 0.459 |
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| 1.2397 | 4.0 | 48 | 1.7206 | 0.5406 | 0.312 | 0.3945 | 0.4566 |
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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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