added jupyter notebbok + some bug fixing and code cleaning

Author: matejMartinc

.gitignore

@@ -4,3 +4,4 @@
 *.idea
 data/*
 BERT/results/*
+gender/src/.ipynb_checkpoints/

README.md

@@ -6,10 +6,17 @@ Please cite these papers [[bib](https://github.com/matejMartinc/PAN2019/blob/mas
 
 Published results were produced in Python 3 programming environment on Linux Mint 18 Cinnamon operating system. Instructions for installation assume the usage of PyPI package manager.<br/>
 
-To only get the source code, clone the repository from github with 'git clone https://github.com/EMBEDDIA/PAN2019'<br/>
+To get the source code, clone the repository from github with 'git clone https://github.com/EMBEDDIA/PAN2019'<br/><br/>
+
+Data for the bot vs male vs female classification can be downloaded from here: <br/>
+https://zenodo.org/record/3692340#.YAARLNYo-Uk <br/>
+Data for the celebrity classification can be downloaded from here: <br/>
+https://zenodo.org/record/3885373#.YAASeNYo-Uk <br/>
 
 Install dependencies if needed: pip install -r requirements.txt
 
+### We have added a Jupyter notebook (see gender/src/example_usage.ipynb) in order to explain specific steps in the code.
+
 ### To reproduce the results of celebrity classification published in the paper run the code in the command line using following commands: ###
 
 Read data and generate features:<br/>

gender/src/evaluate.py

@@ -4,11 +4,7 @@
 import pickle
 from sklearn import preprocessing
 import numpy as np
-from sklearn.metrics import f1_score, accuracy_score
-from sklearn import linear_model
-from sklearn.svm import LinearSVC
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.ensemble import GradientBoostingRegressor
+from sklearn.metrics import accuracy_score
 import logging
 from sklearn.externals import joblib
 logging.basicConfig(format='%(asctime)s - %(message)s', datefmt='%d-%b-%y %H:%M:%S')