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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_v37 |
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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_v37 |
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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: 2.9472 |
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- Rouge1: 0.4923 |
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- Rouge2: 0.2809 |
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- Rougel: 0.4462 |
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- Rougelsum: 0.4468 |
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- Bert precision: 0.8731 |
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- Bert recall: 0.8773 |
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- Average word count: 9.1021 |
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- Max word count: 15 |
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- Min word count: 5 |
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- Average token count: 16.8198 |
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- % shortened texts with length > 12: 8.7087 |
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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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| 1.5911 | 1.0 | 73 | 1.8586 | 0.4823 | 0.2756 | 0.4416 | 0.4423 | 0.8661 | 0.8758 | 8.9399 | 21 | 4 | 16.9489 | 7.8078 | |
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| 0.9246 | 2.0 | 146 | 2.2274 | 0.4039 | 0.2049 | 0.3771 | 0.3764 | 0.8526 | 0.855 | 8.0991 | 13 | 4 | 14.6006 | 0.6006 | |
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| 0.7574 | 3.0 | 219 | 1.8752 | 0.4463 | 0.2263 | 0.4072 | 0.4071 | 0.8629 | 0.8654 | 8.3934 | 14 | 5 | 14.3303 | 3.003 | |
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| 0.6131 | 4.0 | 292 | 1.8338 | 0.4896 | 0.2691 | 0.4451 | 0.4456 | 0.8747 | 0.8711 | 7.982 | 13 | 4 | 13.9249 | 0.3003 | |
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| 0.4422 | 5.0 | 365 | 1.8257 | 0.492 | 0.2727 | 0.4499 | 0.4504 | 0.8734 | 0.875 | 8.5165 | 16 | 5 | 14.4595 | 3.003 | |
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| 0.4227 | 6.0 | 438 | 2.1249 | 0.4666 | 0.2475 | 0.418 | 0.4178 | 0.8657 | 0.8697 | 9.3874 | 16 | 4 | 16.9399 | 8.4084 | |
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| 0.3714 | 7.0 | 511 | 2.1010 | 0.4838 | 0.274 | 0.436 | 0.4364 | 0.869 | 0.8754 | 9.4264 | 16 | 5 | 14.9369 | 9.009 | |
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| 0.2638 | 8.0 | 584 | 2.0803 | 0.489 | 0.2799 | 0.4404 | 0.4404 | 0.8701 | 0.8751 | 8.976 | 15 | 4 | 15.5736 | 8.4084 | |
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| 0.2103 | 9.0 | 657 | 2.1093 | 0.4888 | 0.2722 | 0.4381 | 0.438 | 0.872 | 0.8751 | 9.1952 | 16 | 5 | 16.7447 | 9.9099 | |
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| 0.1475 | 10.0 | 730 | 2.3159 | 0.4684 | 0.2597 | 0.4243 | 0.4244 | 0.8632 | 0.8721 | 9.4234 | 15 | 5 | 16.8288 | 11.7117 | |
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| 0.122 | 11.0 | 803 | 2.4090 | 0.4845 | 0.2729 | 0.4421 | 0.4427 | 0.8721 | 0.8748 | 8.8018 | 16 | 5 | 16.4264 | 5.7057 | |
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| 0.0915 | 12.0 | 876 | 2.6598 | 0.4838 | 0.2691 | 0.4376 | 0.437 | 0.8698 | 0.8742 | 9.1652 | 16 | 5 | 16.9009 | 10.2102 | |
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| 0.073 | 13.0 | 949 | 2.5266 | 0.4973 | 0.2861 | 0.4479 | 0.4495 | 0.8743 | 0.8776 | 9.0631 | 16 | 5 | 16.5796 | 8.4084 | |
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| 0.0526 | 14.0 | 1022 | 2.7673 | 0.4955 | 0.2821 | 0.4464 | 0.4463 | 0.8716 | 0.8791 | 9.4685 | 16 | 5 | 17.2012 | 10.5105 | |
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| 0.042 | 15.0 | 1095 | 2.9472 | 0.4923 | 0.2809 | 0.4462 | 0.4468 | 0.8731 | 0.8773 | 9.1021 | 15 | 5 | 16.8198 | 8.7087 | |
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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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