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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_v49 |
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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_v49 |
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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: 1.7760 |
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- Rouge1: 0.5119 |
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- Rouge2: 0.2768 |
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- Rougel: 0.4448 |
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- Rougelsum: 0.4444 |
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- Bert precision: 0.8755 |
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- Bert recall: 0.8801 |
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- Average word count: 8.8492 |
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- Max word count: 20 |
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- Min word count: 5 |
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- Average token count: 16.4709 |
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- % shortened texts with length > 12: 8.7302 |
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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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| 1.8542 | 1.0 | 83 | 1.6189 | 0.5121 | 0.2699 | 0.4302 | 0.4304 | 0.863 | 0.8909 | 11.3386 | 21 | 5 | 19.4312 | 31.746 | |
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| 0.9651 | 2.0 | 166 | 1.4837 | 0.4957 | 0.2664 | 0.4347 | 0.4362 | 0.8687 | 0.8758 | 8.8598 | 19 | 4 | 16.9815 | 9.2593 | |
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| 0.608 | 3.0 | 249 | 1.4074 | 0.5012 | 0.2693 | 0.4346 | 0.4342 | 0.8725 | 0.8781 | 8.836 | 20 | 4 | 15.5265 | 5.5556 | |
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| 0.3788 | 4.0 | 332 | 1.5646 | 0.5202 | 0.2836 | 0.4535 | 0.4537 | 0.876 | 0.881 | 8.9312 | 18 | 5 | 16.4365 | 10.3175 | |
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| 0.2296 | 5.0 | 415 | 1.7760 | 0.5119 | 0.2768 | 0.4448 | 0.4444 | 0.8755 | 0.8801 | 8.8492 | 20 | 5 | 16.4709 | 8.7302 | |
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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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