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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_v50 |
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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_v50 |
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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: 1.8296 |
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- Rouge1: 0.5063 |
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- Rouge2: 0.2803 |
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- Rougel: 0.4415 |
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- Rougelsum: 0.4405 |
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- Bert precision: 0.8741 |
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- Bert recall: 0.8787 |
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- Average word count: 8.7857 |
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- Max word count: 16 |
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- Min word count: 3 |
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- Average token count: 16.3942 |
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- % shortened texts with length > 12: 11.9048 |
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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: 0.0001 |
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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: 5 |
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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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| 1.3343 | 1.0 | 83 | 1.4625 | 0.5101 | 0.2866 | 0.4536 | 0.4527 | 0.8751 | 0.877 | 8.3042 | 19 | 4 | 15.1508 | 5.0265 | |
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| 0.7011 | 2.0 | 166 | 1.4296 | 0.5101 | 0.284 | 0.4548 | 0.4551 | 0.8736 | 0.8797 | 8.7593 | 18 | 5 | 16.0529 | 7.672 | |
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| 0.483 | 3.0 | 249 | 1.3880 | 0.5025 | 0.2819 | 0.4433 | 0.442 | 0.8722 | 0.8782 | 8.7698 | 18 | 5 | 14.8492 | 6.3492 | |
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| 0.3876 | 4.0 | 332 | 1.7614 | 0.4934 | 0.2653 | 0.4334 | 0.4327 | 0.8715 | 0.8725 | 8.2249 | 18 | 5 | 16.3042 | 5.5556 | |
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| 0.291 | 5.0 | 415 | 1.8296 | 0.5063 | 0.2803 | 0.4415 | 0.4405 | 0.8741 | 0.8787 | 8.7857 | 16 | 3 | 16.3942 | 11.9048 | |
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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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