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-# User stories Batch 1
-
-## [x] As a user I want to be able to download the dataset for EITHER comment generation OR code refinment
-
-- When asking for the dataset, there is a parameter to say whether you want to download the context or
-  not (the state of the repo before the PR).
-
-- TODO: actually build the datasets
-
-## [x] As a user I want to be able to submit my predictions for the given downloaded dataset
-
-## [x] As a user I want to be able to see the results of the code refinment, whether the compiled and tested successfully
-
-## [x] Add a download button to the page that gives the user a slightly more detailed version of the result, showing the bleu score for each paraphrase and the number of tests passed for code refinement (?)
-
-## [x] As a user I want to be able to see the performance of my predictions against the benchmark (comment generation: bleu score, code refinement: # tests passed)
-
-# Work flow
-
-First I'll do a basic express server to serve the datasets. No login no frontend. I'll implement
-Batch 1 then move on to some other features (frontend, maybe auth).
-
-# Batch 2
-
-## [ ] As a dev I want to be able to deploy my webapp into a container
-
-## [x] As a user I want to have a webpage to make all the actions mentioned above
-
-## [x] As a user I want to see what the webpage is used for (inspired from https://seart-ghs.si.usi.ch)
-
-## [x] Remove the paths utils, it's useless in python since every path is relative to where you call the script from
-
-# DATASET
-
-## [x] Rebuild the dataset (is doing)
-
-## [x] Match all the current entries in dataset.json and put the selection thing inside dataset.dont-care.json
-
-## [x] Go through manual selection of dataset.dont-care.json to fill missing repos
-
-## [x] The step above will already create all the archives
-
-## [x] Signal the instances that are actually covered
-
-## [x] When submitting for refinement, give the user a link that he can put in a text box to see the results
-
-# Paper
-
-## [x] Write paper