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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_v45 |
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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_v45 |
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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: 26.8982 |
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- Rouge1: 0.0 |
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- Rouge2: 0.0 |
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- Rougel: 0.0 |
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- Rougelsum: 0.0 |
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- Bert precision: 0.6649 |
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- Bert recall: 0.672 |
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- Average word count: 1.0 |
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- Max word count: 1 |
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- Min word count: 1 |
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- Average token count: 62.0 |
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- % shortened texts with length > 12: 0.0 |
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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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| 3.3791 | 1.0 | 83 | 6.7318 | 0.0982 | 0.0 | 0.0972 | 0.0969 | 0.6855 | 0.6599 | 1.2937 | 2 | 1 | 16.7619 | 0.0 | |
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| 2.8727 | 2.0 | 166 | 10.3841 | 0.0 | 0.0 | 0.0 | 0.0 | 0.674 | 0.6911 | 3.0 | 3 | 3 | 62.0 | 0.0 | |
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| 2.7805 | 3.0 | 249 | 10.0261 | 0.0345 | 0.0 | 0.0346 | 0.0345 | 0.6746 | 0.6819 | 2.0 | 2 | 2 | 62.0 | 0.0 | |
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| 2.7183 | 4.0 | 332 | 9.5191 | 0.0 | 0.0 | 0.0 | 0.0 | 0.673 | 0.6736 | 1.0 | 1 | 1 | 62.0 | 0.0 | |
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| 2.7086 | 5.0 | 415 | 10.4466 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6568 | 0.6648 | 1.0 | 1 | 1 | 62.0 | 0.0 | |
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| 2.6474 | 6.0 | 498 | 13.9665 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6641 | 0.6709 | 1.0 | 1 | 1 | 62.0 | 0.0 | |
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| 2.63 | 7.0 | 581 | 13.3621 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6457 | 0.6701 | 1.0 | 1 | 1 | 62.0 | 0.0 | |
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| 2.5998 | 8.0 | 664 | 13.0602 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6618 | 0.6672 | 1.0 | 1 | 1 | 62.0 | 0.0 | |
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| 2.5689 | 9.0 | 747 | 15.0760 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6591 | 0.6651 | 1.0 | 1 | 1 | 62.0 | 0.0 | |
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| 2.5508 | 10.0 | 830 | 15.6936 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6649 | 0.6716 | 1.0 | 1 | 1 | 62.0 | 0.0 | |
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| 2.5298 | 11.0 | 913 | 16.8446 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6604 | 0.6648 | 1.0 | 1 | 1 | 62.0 | 0.0 | |
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| 2.5091 | 12.0 | 996 | 21.0673 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6721 | 0.6702 | 1.0 | 1 | 1 | 62.0 | 0.0 | |
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| 2.5019 | 13.0 | 1079 | 25.5628 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6605 | 0.67 | 1.0 | 1 | 1 | 62.0 | 0.0 | |
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| 2.4826 | 14.0 | 1162 | 25.1203 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6725 | 0.6666 | 1.0 | 1 | 1 | 62.0 | 0.0 | |
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| 2.4693 | 15.0 | 1245 | 26.8982 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6649 | 0.672 | 1.0 | 1 | 1 | 62.0 | 0.0 | |
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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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