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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_v46 |
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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_v46 |
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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: 1.8536 |
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- Rouge1: 0.485 |
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- Rouge2: 0.271 |
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- Rougel: 0.4374 |
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- Rougelsum: 0.4371 |
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- Bert precision: 0.8676 |
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- Bert recall: 0.8761 |
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- Average word count: 9.1032 |
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- Max word count: 17 |
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- Min word count: 4 |
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- Average token count: 15.8254 |
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- % shortened texts with length > 12: 9.5238 |
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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: 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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| 1.8213 | 1.0 | 42 | 2.1030 | 0.4561 | 0.2433 | 0.4131 | 0.4131 | 0.8606 | 0.8724 | 9.0529 | 15 | 5 | 14.7249 | 7.4074 | |
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| 0.8874 | 2.0 | 84 | 1.8034 | 0.4778 | 0.2609 | 0.4316 | 0.4316 | 0.8569 | 0.8787 | 10.6323 | 21 | 5 | 16.2963 | 19.3122 | |
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| 0.603 | 3.0 | 126 | 1.6613 | 0.4749 | 0.2594 | 0.425 | 0.4253 | 0.8576 | 0.8796 | 10.5106 | 21 | 5 | 16.2751 | 23.0159 | |
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| 0.5413 | 4.0 | 168 | 1.5975 | 0.4729 | 0.249 | 0.4258 | 0.4254 | 0.8635 | 0.8696 | 8.6481 | 16 | 4 | 14.3677 | 4.2328 | |
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| 0.3393 | 5.0 | 210 | 1.6755 | 0.4959 | 0.28 | 0.4476 | 0.4473 | 0.8687 | 0.8772 | 8.8942 | 20 | 5 | 15.8915 | 8.4656 | |
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| 0.2573 | 6.0 | 252 | 1.6908 | 0.4775 | 0.2589 | 0.4309 | 0.4307 | 0.866 | 0.873 | 8.9868 | 22 | 4 | 15.4339 | 10.3175 | |
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| 0.173 | 7.0 | 294 | 1.8536 | 0.485 | 0.271 | 0.4374 | 0.4371 | 0.8676 | 0.8761 | 9.1032 | 17 | 4 | 15.8254 | 9.5238 | |
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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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