metadata

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.

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