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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_v35 |
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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_v35 |
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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: 2.1783 |
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- Rouge1: 0.4993 |
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- Rouge2: 0.2724 |
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- Rougel: 0.4472 |
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- Rougelsum: 0.4467 |
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- Bert precision: 0.8744 |
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- Bert recall: 0.8769 |
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- Average word count: 8.6096 |
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- Max word count: 20 |
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- Min word count: 5 |
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- Average token count: 14.97 |
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- % shortened texts with length > 12: 3.6036 |
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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: 32 |
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- eval_batch_size: 32 |
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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 | 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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| 1.83 | 1.0 | 37 | 1.9642 | 0.457 | 0.2329 | 0.4049 | 0.4054 | 0.8677 | 0.8663 | 8.027 | 13 | 4 | 16.6607 | 2.7027 | |
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| 0.8629 | 2.0 | 74 | 1.6943 | 0.5268 | 0.3019 | 0.4695 | 0.4697 | 0.8758 | 0.8901 | 10.0571 | 19 | 5 | 17.8258 | 19.2192 | |
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| 0.7849 | 3.0 | 111 | 1.6564 | 0.5001 | 0.279 | 0.4553 | 0.4554 | 0.873 | 0.8805 | 8.9099 | 17 | 5 | 15.4865 | 5.1051 | |
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| 0.6116 | 4.0 | 148 | 1.7559 | 0.4638 | 0.2376 | 0.4183 | 0.4188 | 0.863 | 0.8665 | 8.4414 | 15 | 4 | 13.8829 | 0.9009 | |
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| 0.3976 | 5.0 | 185 | 1.6708 | 0.4999 | 0.2723 | 0.4481 | 0.4481 | 0.8744 | 0.8766 | 8.5556 | 16 | 5 | 14.6877 | 3.9039 | |
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| 0.2977 | 6.0 | 222 | 1.7196 | 0.4937 | 0.2699 | 0.4376 | 0.4379 | 0.8684 | 0.877 | 9.1652 | 20 | 5 | 15.3964 | 5.7057 | |
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| 0.2187 | 7.0 | 259 | 1.7942 | 0.5129 | 0.2905 | 0.4572 | 0.4575 | 0.8765 | 0.8803 | 8.7117 | 19 | 5 | 14.6306 | 3.9039 | |
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| 0.1603 | 8.0 | 296 | 1.8003 | 0.4822 | 0.2538 | 0.4237 | 0.4229 | 0.8688 | 0.8722 | 8.6306 | 19 | 5 | 15.4474 | 5.7057 | |
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| 0.1175 | 9.0 | 333 | 2.0138 | 0.5024 | 0.2798 | 0.4486 | 0.4475 | 0.8742 | 0.8791 | 8.7988 | 19 | 5 | 16.1471 | 6.6066 | |
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| 0.0859 | 10.0 | 370 | 2.1783 | 0.4993 | 0.2724 | 0.4472 | 0.4467 | 0.8744 | 0.8769 | 8.6096 | 20 | 5 | 14.97 | 3.6036 | |
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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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