You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Copy file name to clipboardExpand all lines: README.md
+1-1Lines changed: 1 addition & 1 deletion
Display the source diff
Display the rich diff
Original file line number
Diff line number
Diff line change
@@ -54,5 +54,5 @@ You have to paste the following lines of code (replace the relevant parts where
54
54
+ Run the command `pip install -r requirements.txt` which will install all required libraries for DoConA in your fresh Python virtual environment
55
55
+ On installation completion of the required libraries in the previous step, change into the `docona/` directory of your local copy of the code (this directory contains all `.py` code files for the tool)
56
56
+ Run the command `python docona.py`. **Note:** the pipeline can take a while to run on a mid-range machine (E.g. Quad-core, 16GB of RAM). For example, given 10,000 documents and a representative random sample of 500 documents, with two additional pretrained models, the pipeline can take close to 24 hours to run on such a machine. It is recommended to run the pipeline on a high performance computing platform
57
-
+ On successful completion of the pipeline, there will a `results.csv` placed in the folder `outputdata/`. DoConA will also generate various other model and data files during the run and will place these in the folders `inputdata/resources` and `outputdata/`
57
+
+ On successful completion of the pipeline, there will a `results.csv` placed in the folder `outputdata/`. DoConA will also generate various other model and data files during the run and will place these in the folders `inputdata/resources/` and `outputdata/`
58
58
+ Please see the [wiki](https://github.com/MaastrichtU-IDS/docona/wiki) for more detailed information about the generated data
0 commit comments