transformers pytorch pegasus text2text-generation generated_from_trainer summarization en autotrain_compatible endpoints_compatible region:us
---
language:
- en
metrics:
- rouge
pipeline_tag: summarization
tags:
- generated_from_trainer
model-index:
- name: pegasus-multi_news-NewsSummarization_BBC
  results: []
---

# 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