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---
tags:
- generated_from_trainer
model-index:
- name: pegasus-multi_news-NewsSummarization_BBC
results: []
language:
- en
metrics:
- rouge
pipeline_tag: summarization
---
# pegasus-multi_news-NewsSummarization_BBC
This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/google/pegasus-multi_news).
## Model description
This is a text summarization model of news articles.
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
## Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology.
## Training and evaluation data
Dataset Source: https://www.kaggle.com/datasets/pariza/bbc-news-summary
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 2
### Training results
Unfortunately, I did not set the metrics to automatically upload here. They are as follows:
| Training Loss | Epoch | Step | rouge1 | rouge2 | rougeL | rougeLsum |
|:-------------:|:-----:|:----:|:--------:|:--------:|:--------:|:----------:|
| 6.41979 | 2.0 | 214 | 0.584474 | 0.463574 | 0.408729 | 0.408431 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1 |