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--- |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: pegasus-multi_news-NewsSummarization_BBC |
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results: [] |
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language: |
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- en |
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metrics: |
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- rouge |
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pipeline_tag: summarization |
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--- |
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# pegasus-multi_news-NewsSummarization_BBC |
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This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/google/pegasus-multi_news). |
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## Model description |
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This is a text summarization model of news articles. |
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For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Text%20Summarization/Text_Summarization_BBC_News-Pegasus.ipynb |
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## Intended uses & limitations |
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This model is intended to demonstrate my ability to solve a complex problem using technology. |
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## Training and evaluation data |
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Dataset Source: https://www.kaggle.com/datasets/pariza/bbc-news-summary |
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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: 2e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 16 |
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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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- lr_scheduler_warmup_steps: 50 |
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- num_epochs: 2 |
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### Training results |
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Unfortunately, I did not set the metrics to automatically upload here. They are as follows: |
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| Training Loss | Epoch | Step | rouge1 | rouge2 | rougeL | rougeLsum | |
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|:-------------:|:-----:|:----:|:--------:|:--------:|:--------:|:----------:| |
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| 6.41979 | 2.0 | 214 | 0.584474 | 0.463574 | 0.408729 | 0.408431 | |
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### Framework versions |
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- Transformers 4.21.3 |
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- Pytorch 1.12.1 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
