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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: my_awesome_billsum_model |
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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: ca_test |
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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: 0.5281 |
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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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# my_awesome_billsum_model |
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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: 1.4680 |
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- Rouge1: 0.5281 |
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- Rouge2: 0.2775 |
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- Rougel: 0.3421 |
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- Rougelsum: 0.4021 |
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- Gen Len: 130.3669 |
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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: 2 |
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- eval_batch_size: 2 |
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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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- num_epochs: 4 |
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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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| No log | 1.0 | 495 | 1.5156 | 0.5029 | 0.2538 | 0.3184 | 0.3865 | 131.4073 | |
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| 1.8254 | 2.0 | 990 | 1.4455 | 0.5102 | 0.2663 | 0.3395 | 0.3949 | 117.4516 | |
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| 1.2925 | 3.0 | 1485 | 1.4291 | 0.5212 | 0.2713 | 0.3392 | 0.3944 | 130.379 | |
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| 0.9936 | 4.0 | 1980 | 1.4680 | 0.5281 | 0.2775 | 0.3421 | 0.4021 | 130.3669 | |
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### Framework versions |
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- Transformers 4.27.3 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.2 |
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