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 The two python notebooks deal with dataset preparation. `stories-scraping.ipynb` scrapes prompts (inputs) and stories (outputs) from a [blog](https://blog.reedsy.com/short-stories/science-fiction/) where weekly writing contests are held, and saves them into a temporary `raw_stories` directory. The `stories-analysis.ipynb` performs statistical analysis on the scraped data and prepares the data for training - separates words and punctuation, shortens the stories to a desired length (1800 words) and separates the data into train, valid and testing datasets. It also deletes temporary directories.
 
+Note: Trained model files are available on igor in my directory. (Link in report Appendices)
+
 
 ### Model Reference