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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_summarization_pretrained |
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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: 0.5264 |
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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_summarization_pretrained |
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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: 1.7402 |
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- Rouge1: 0.5264 |
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- Rouge2: 0.2745 |
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- Rougel: 0.3432 |
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- Rougelsum: 0.4049 |
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- Gen Len: 131.0645 |
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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: 1 |
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- eval_batch_size: 1 |
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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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### 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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| 1.7347 | 1.0 | 989 | 1.6263 | 0.5044 | 0.254 | 0.3219 | 0.3734 | 121.8306 | |
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| 1.2029 | 2.0 | 1978 | 1.6037 | 0.5278 | 0.2723 | 0.3351 | 0.3977 | 136.4718 | |
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| 0.8435 | 3.0 | 2967 | 1.6054 | 0.513 | 0.2661 | 0.3357 | 0.3957 | 129.1048 | |
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| 0.6326 | 4.0 | 3956 | 1.7402 | 0.5264 | 0.2745 | 0.3432 | 0.4049 | 131.0645 | |
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### Framework versions |
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- Transformers 4.29.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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