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--- |
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license: mit |
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base_model: facebook/bart-large-xsum |
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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: text_shortening_model_v36 |
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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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# text_shortening_model_v36 |
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This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.9367 |
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- Rouge1: 0.4747 |
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- Rouge2: 0.2555 |
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- Rougel: 0.4212 |
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- Rougelsum: 0.4204 |
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- Bert precision: 0.8696 |
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- Bert recall: 0.87 |
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- Average word count: 8.6396 |
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- Max word count: 17 |
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- Min word count: 4 |
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- Average token count: 16.6216 |
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- % shortened texts with length > 12: 6.3063 |
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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: 0.0003 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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: 15 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bert precision | Bert recall | Average word count | Max word count | Min word count | Average token count | % shortened texts with length > 12 | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------------:|:-----------:|:------------------:|:--------------:|:--------------:|:-------------------:|:----------------------------------:| |
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| 1.571 | 1.0 | 73 | 2.1904 | 0.4703 | 0.2572 | 0.4224 | 0.4216 | 0.8718 | 0.8704 | 8.2583 | 14 | 3 | 14.2973 | 1.5015 | |
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| 0.8809 | 2.0 | 146 | 1.9224 | 0.4588 | 0.2414 | 0.4177 | 0.417 | 0.8734 | 0.8673 | 8.2492 | 22 | 3 | 15.8078 | 3.9039 | |
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| 0.7135 | 3.0 | 219 | 2.7437 | 0.2535 | 0.082 | 0.2289 | 0.2294 | 0.8131 | 0.8149 | 8.4324 | 11 | 5 | 14.973 | 0.0 | |
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| 0.5646 | 4.0 | 292 | 2.0495 | 0.4689 | 0.249 | 0.4155 | 0.4156 | 0.8653 | 0.8736 | 9.8438 | 20 | 4 | 18.0961 | 21.3213 | |
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| 0.4158 | 5.0 | 365 | 2.0101 | 0.4707 | 0.2539 | 0.4241 | 0.4243 | 0.8688 | 0.8725 | 8.9009 | 14 | 4 | 14.7988 | 4.2042 | |
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| 0.3445 | 6.0 | 438 | 2.0642 | 0.4606 | 0.255 | 0.4133 | 0.4132 | 0.866 | 0.8705 | 9.0991 | 15 | 4 | 14.955 | 6.3063 | |
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| 0.2473 | 7.0 | 511 | 2.2675 | 0.4668 | 0.2441 | 0.4137 | 0.413 | 0.8683 | 0.8694 | 8.7177 | 19 | 3 | 16.5766 | 9.3093 | |
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| 0.2084 | 8.0 | 584 | 2.4474 | 0.4793 | 0.2608 | 0.4256 | 0.4257 | 0.8701 | 0.8741 | 9.1021 | 17 | 4 | 17.048 | 9.9099 | |
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| 0.1703 | 9.0 | 657 | 2.3961 | 0.4754 | 0.2609 | 0.4253 | 0.4253 | 0.8676 | 0.8749 | 9.2943 | 17 | 4 | 17.2402 | 11.4114 | |
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| 0.1293 | 10.0 | 730 | 2.4721 | 0.4581 | 0.2463 | 0.409 | 0.4082 | 0.8657 | 0.8671 | 8.7057 | 18 | 4 | 16.3514 | 6.9069 | |
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| 0.1312 | 11.0 | 803 | 2.4027 | 0.4667 | 0.2497 | 0.4117 | 0.4113 | 0.868 | 0.8683 | 8.4925 | 18 | 4 | 15.4294 | 5.1051 | |
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| 0.1424 | 12.0 | 876 | 2.5041 | 0.476 | 0.2506 | 0.4214 | 0.4214 | 0.8715 | 0.8699 | 8.6186 | 18 | 4 | 16.1862 | 6.006 | |
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| 0.0926 | 13.0 | 949 | 2.7011 | 0.4723 | 0.2582 | 0.4238 | 0.4227 | 0.8695 | 0.8701 | 8.6096 | 19 | 4 | 16.4505 | 5.7057 | |
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| 0.0663 | 14.0 | 1022 | 2.8149 | 0.467 | 0.2504 | 0.4157 | 0.4153 | 0.8674 | 0.8675 | 8.6336 | 17 | 4 | 16.4985 | 5.1051 | |
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| 0.0684 | 15.0 | 1095 | 2.9367 | 0.4747 | 0.2555 | 0.4212 | 0.4204 | 0.8696 | 0.87 | 8.6396 | 17 | 4 | 16.6216 | 6.3063 | |
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### Framework versions |
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- Transformers 4.33.1 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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