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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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metrics: |
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- rouge |
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model-index: |
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- name: pegasus-cnn_dailymail-finetuned-scope-summarization |
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results: [] |
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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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# pegasus-cnn_dailymail-finetuned-scope-summarization |
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This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co./google/pegasus-cnn_dailymail) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1923 |
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- Rouge1: 56.9116 |
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- Rouge2: 45.4236 |
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- Rougel: 49.8645 |
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- Rougelsum: 49.71 |
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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: 5.6e-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: 20 |
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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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| 0.5864 | 1.0 | 158 | 0.3056 | 41.1501 | 21.8098 | 34.2505 | 34.2057 | |
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| 0.3517 | 2.0 | 316 | 0.2787 | 45.9535 | 26.9084 | 37.7287 | 37.7213 | |
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| 0.2835 | 3.0 | 474 | 0.2655 | 49.4653 | 30.6584 | 40.5201 | 40.4494 | |
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| 0.2683 | 4.0 | 632 | 0.2528 | 50.2066 | 32.8862 | 40.4058 | 40.2244 | |
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| 0.2557 | 5.0 | 790 | 0.2469 | 50.3451 | 33.536 | 41.7433 | 41.6118 | |
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| 0.2493 | 6.0 | 948 | 0.2382 | 51.9053 | 36.1533 | 42.0343 | 41.8884 | |
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| 0.2406 | 7.0 | 1106 | 0.2330 | 53.2105 | 38.1 | 43.434 | 43.2194 | |
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| 0.235 | 8.0 | 1264 | 0.2267 | 51.9642 | 38.1903 | 44.4502 | 44.3851 | |
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| 0.2296 | 9.0 | 1422 | 0.2237 | 53.5609 | 38.9875 | 44.7145 | 44.6146 | |
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| 0.2246 | 10.0 | 1580 | 0.2195 | 54.6691 | 41.5464 | 45.7506 | 45.6856 | |
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| 0.221 | 11.0 | 1738 | 0.2141 | 54.4114 | 41.2748 | 45.9992 | 45.8182 | |
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| 0.2145 | 12.0 | 1896 | 0.2097 | 55.3852 | 42.9342 | 48.376 | 48.5267 | |
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| 0.2115 | 13.0 | 2054 | 0.2060 | 55.9251 | 43.4806 | 48.0303 | 47.9584 | |
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| 0.2081 | 14.0 | 2212 | 0.2017 | 55.8426 | 43.1239 | 47.8006 | 47.8356 | |
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| 0.2042 | 15.0 | 2370 | 0.1997 | 55.4631 | 42.78 | 47.307 | 47.3142 | |
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| 0.2031 | 16.0 | 2528 | 0.1970 | 57.0004 | 44.4252 | 49.6236 | 49.5213 | |
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| 0.1996 | 17.0 | 2686 | 0.1953 | 55.438 | 43.8797 | 48.536 | 48.3506 | |
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| 0.1991 | 18.0 | 2844 | 0.1939 | 56.1102 | 44.5176 | 48.5553 | 48.4163 | |
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| 0.1963 | 19.0 | 3002 | 0.1925 | 56.6366 | 45.3753 | 49.4421 | 49.3468 | |
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| 0.1955 | 20.0 | 3160 | 0.1923 | 56.9116 | 45.4236 | 49.8645 | 49.71 | |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
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