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--- |
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license: apache-2.0 |
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base_model: t5-small |
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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_v1 |
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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_v1 |
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This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on a dataset of 699 original-shortened texts pairs of advertising texts. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.9266 |
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- Rouge1: 0.4797 |
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- Rouge2: 0.2787 |
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- Rougel: 0.4325 |
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- Rougelsum: 0.4321 |
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- Bert precision: 0.8713 |
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- Bert recall: 0.8594 |
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- Average word count: 10.0714 |
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- Max word count: 18 |
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- Min word count: 1 |
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- Average token count: 15.45 |
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## Model description |
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Data is cleaned and preprocessed: "summarize" prefix added for each original text input. |
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Loss is a combination of: |
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- CrossEntropy |
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- Custom loss which can be seen as a length penalty: +1 if predicted text length > 12, else 0 |
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Loss = theta * Custom loss + (1 - theta) * CrossEntropy |
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(theta = 0.3) |
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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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699 original-shortened texts pairs of advertising texts of various lengths. |
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- Original texts lengths: > 12 |
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- Shortened texts lengths: < 13 |
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Splitting amongst sub-datasets: |
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- 70% of the dataset is used for training |
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- 20% of the dataset is used for validation |
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- 10% of the dataset is kept for testing |
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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: 0.0001 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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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: 1 |
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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 | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------------:|:-----------:|:------------------:|:--------------:|:--------------:|:-------------------:| |
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| 1.7188 | 1.0 | 8 | 1.9266 | 0.4797 | 0.2787 | 0.4325 | 0.4321 | 0.8713 | 0.8594 | 10.0714 | 18 | 1 | 15.45 | |
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### Framework versions |
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- Transformers 4.32.1 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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