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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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