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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_v47 |
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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_v47 |
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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: 6.3912 |
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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: 0.6047 |
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- Bert recall: 0.5681 |
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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: 12.0 |
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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: 0.005 |
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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: 7 |
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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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| 7.822 | 1.0 | 83 | 7.4737 | 0.0776 | 0.0 | 0.0775 | 0.0776 | 0.6348 | 0.6223 | 2.0 | 2 | 2 | 13.0 | 0.0 | |
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| 3.2859 | 2.0 | 166 | 6.6585 | 0.1063 | 0.0 | 0.1063 | 0.1063 | 0.6469 | 0.608 | 5.0026 | 6 | 5 | 12.0 | 0.0 | |
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| 3.0284 | 3.0 | 249 | 6.4761 | 0.116 | 0.0 | 0.116 | 0.1161 | 0.6479 | 0.6388 | 3.9974 | 4 | 3 | 14.0 | 0.0 | |
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| 2.9681 | 4.0 | 332 | 6.4592 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6071 | 0.5723 | 1.0 | 1 | 1 | 12.0 | 0.0 | |
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| 2.9377 | 5.0 | 415 | 6.4142 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6047 | 0.5681 | 1.0 | 1 | 1 | 12.0 | 0.0 | |
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| 2.9168 | 6.0 | 498 | 6.4049 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6049 | 0.5685 | 1.0 | 1 | 1 | 12.0 | 0.0 | |
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| 2.8964 | 7.0 | 581 | 6.3912 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6047 | 0.5681 | 1.0 | 1 | 1 | 12.0 | 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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