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--- |
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base_model: google/pegasus-cnn_dailymail |
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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: pegasuscnn-dailymail_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: 0.4804 |
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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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# pegasuscnn-dailymail_billsum_model |
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This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the billsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.6747 |
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- Rouge1: 0.4804 |
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- Rouge2: 0.2362 |
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- Rougel: 0.3218 |
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- Rougelsum: 0.3218 |
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- Gen Len: 123.3669 |
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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: 5 |
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- eval_batch_size: 5 |
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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: 10 |
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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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| 2.6227 | 1.0 | 198 | 1.9091 | 0.4289 | 0.1938 | 0.2945 | 0.2947 | 120.1855 | |
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| 1.9714 | 2.0 | 396 | 1.8147 | 0.4517 | 0.2093 | 0.3059 | 0.3061 | 120.7742 | |
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| 1.903 | 3.0 | 594 | 1.7646 | 0.4607 | 0.2207 | 0.3098 | 0.3102 | 121.121 | |
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| 1.7973 | 4.0 | 792 | 1.7362 | 0.4719 | 0.2264 | 0.3179 | 0.3178 | 122.3185 | |
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| 1.7868 | 5.0 | 990 | 1.7137 | 0.4779 | 0.2314 | 0.3191 | 0.3192 | 123.2379 | |
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| 1.7457 | 6.0 | 1188 | 1.6958 | 0.4748 | 0.2296 | 0.3171 | 0.317 | 123.2056 | |
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| 1.6687 | 7.0 | 1386 | 1.6873 | 0.4795 | 0.2352 | 0.3216 | 0.3216 | 123.2702 | |
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| 1.6751 | 8.0 | 1584 | 1.6806 | 0.4835 | 0.2384 | 0.3248 | 0.3245 | 122.8266 | |
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| 1.6564 | 9.0 | 1782 | 1.6758 | 0.4814 | 0.2359 | 0.3217 | 0.3216 | 123.2984 | |
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| 1.6333 | 10.0 | 1980 | 1.6747 | 0.4804 | 0.2362 | 0.3218 | 0.3218 | 123.3669 | |
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### Framework versions |
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- Transformers 4.34.1 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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