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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_v60 |
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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_v60 |
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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: 0.7251 |
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- Rouge1: 0.7246 |
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- Rouge2: 0.5572 |
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- Rougel: 0.6745 |
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- Rougelsum: 0.6724 |
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- Bert precision: 0.9227 |
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- Bert recall: 0.9242 |
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- Bert f1-score: 0.923 |
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- Average word count: 8.4018 |
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- Max word count: 16 |
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- Min word count: 4 |
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- Average token count: 16.1562 |
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- % shortened texts with length > 12: 7.5893 |
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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: 1e-05 |
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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: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bert precision | Bert recall | Bert f1-score | 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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| 1.4241 | 1.0 | 49 | 0.7533 | 0.7094 | 0.5458 | 0.6655 | 0.6641 | 0.9182 | 0.9214 | 0.9193 | 8.3884 | 17 | 5 | 15.3661 | 6.25 | |
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| 0.5792 | 2.0 | 98 | 0.7279 | 0.7058 | 0.5397 | 0.6587 | 0.6582 | 0.9201 | 0.9193 | 0.9192 | 8.3393 | 17 | 4 | 15.9062 | 5.3571 | |
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| 0.4392 | 3.0 | 147 | 0.7251 | 0.7246 | 0.5572 | 0.6745 | 0.6724 | 0.9227 | 0.9242 | 0.923 | 8.4018 | 16 | 4 | 16.1562 | 7.5893 | |
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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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