Merge branch 'master' of https://git.rwth-aachen.de/sparsh.jauhari/bias-in-bio-lab-cssh

README.md

@@ -12,6 +12,14 @@ to add pretrained word embedding models, write in terminal:
 
 to get debiased version of pretrained word2Vec embedding, use the link: <br /> https://drive.google.com/file/d/1_PvT4ZvtZjhq4HPywA8-u06epht9ccOw/view?usp=sharing
 
+
+<br /> OR <br />
+
+`cd word_embeddings`  <br />
+
+gdown https://drive.google.com/uc?id=0B5vZVlu2WoS5ZTBSekpUX0RSNDg
+
+
 install requirements.txt
 
 

predict.py

@@ -11,7 +11,7 @@ def predict_all_models(X_test):
     
     for embedding in ['cv','self_w2v','w2v']:
         for sampling in ['random','balanced']:
-            for mask in ['raw','bio']:
+            for masking in [True,False]:
                 pred = predict_one(X_test, embedding=embedding, sampling=sampling, masking=masking)
     
                 print("\nembedding :",embedding)
@@ -20,7 +20,7 @@ def predict_all_models(X_test):
                 for i,x in enumerate(X_test):
                     print("sent :",x)
                     print("pred :",pred[i])
-                    print()
+#                    print()
 
     for sampling in ['random','balanced']:
         pred = predict_one(X_test, embedding='elmo', sampling=sampling, masking=True)
@@ -31,7 +31,7 @@ def predict_all_models(X_test):
         for i,x in enumerate(X_test):
             print("sent :",x)
             print("pred :",pred[i])
-            print()
+#            print()
 
 if __name__ == "__main__":
     import argparse
@@ -44,10 +44,11 @@ if __name__ == "__main__":
     parser.add_argument("--embedding", help = "choice of embeddings to be used ('cv': count_vectorize(self-trained), 'w2v': word2vec_embedding(pre-trained), 'self_w2v':w2v(self-trained), 'elmo':elmo(pre-trained))")
     parser.add_argument("--pred_all_models", help = 'yes or no')
 
+    args = parser.parse_args()
+    X_test = ['She works at the hospital','He works at the hospital']
     if args.pred_all_models == 'yes':
         predict_all_models(X_test)
     else:
-        X_test = ['She works at the hospital','He works at the hospital']
 
         pred = predict_one(X_test, embedding=args.embedding, sampling=args.sampling, masking=args.masking)
         
@@ -57,4 +58,4 @@ if __name__ == "__main__":
         for i,x in enumerate(X_test):
             print("sent :",x)
             print("pred :",pred[i])
-            print()
+#            print()

requirements.txt

@@ -3,3 +3,4 @@ pymongo==3.11.4
 virtualenv==20.4.2
 gensim==4.0.1
 dnspython
+gdown