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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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