Gender samples

app.py

@@ -41,8 +41,8 @@ if menu == "Contributions & Evaluation":
 | Model     | [FullEvaluation](https://docs.google.com/spreadsheets/d/1vXJrIg38cEaKjOG5y4I4PQwAQFUmCkohbViJ9zj_Emg/edit#gid=1809754143) Average     | 20Newsgroups Clustering | StackOverflow DupQuestions | Twitter SemEval2015    |
 |-----------|---------------------------------------|-------|-------|-------|
 | paraphrase-mpnet-base-v2 (previous SOTA)  | 67.97 | 47.79 | 49.03 | 72.36 |
-| all_datasets_v3_roberta-large (400k steps) | **70.22** | 50.12 | 52.18 | 75.28 |
-| all_datasets_v3_mpnet-base (440k steps) | **70.01**   | 50.22 | 52.24 | 76.27 |
+| **all_datasets_v3_roberta-large (400k steps)** | **70.22** | 50.12 | 52.18 | 75.28 |
+| **all_datasets_v3_mpnet-base (440k steps)** | **70.01**   | 50.22 | 52.24 | 76.27 |
 ''')
 elif menu == "Sentence Similarity":
     st.header('Sentence Similarity')
@@ -172,11 +172,22 @@ Hopefully the evaluation performed here can proceed towards improving Gender-neu
 For more cool information on sentence embeddings, see the [sBert project](https://www.sbert.net/examples/applications/computing-embeddings/README.html).
 ''')
 
-    select_models = st.multiselect("Choose models", options=list(MODELS_ID), default=list(MODELS_ID)[0])
+    select_models = st.multiselect("Choose models", options=list(MODELS_ID), default=list(MODELS_ID))
+
+    samples = st.radio("Samples", options=["President of United States", "Professor", "Nurse", "Custom"])
 
-    base_text = st.text_input("Gender Neutral Text", "President of the United States promised relief to Hurricane survivors.")
-    male_text = st.text_input("Male-assumed Text", "He promised relief to Hurricane survivors.")
-    female_text = st.text_input("Female-assumed Text", "She promised relief to Hurricane survivors.")
+    if samples == "President of United States":
+        base_text = st.text_input("Gender Neutral Text", "President of the United States promised relief to Hurricane survivors.")
+        male_text = st.text_input("Male-assumed Text", "He promised relief to Hurricane survivors.")
+        female_text = st.text_input("Female-assumed Text", "She promised relief to Hurricane survivors.")
+    elif samples == "Professor":
+        base_text = st.text_input("Gender Neutral Text", "Professor ended the class earlier than usual.")
+        male_text = st.text_input("Male-assumed Text", "He ended the class earlier than usual.")
+        female_text = st.text_input("Female-assumed Text", "She ended the class earlier than usual.")
+    elif samples == "Nurse":
+        base_text = st.text_input("Gender Neutral Text", "Nurse administered the vaccine and rubbed alcohol.")
+        male_text = st.text_input("Male-assumed Text", "He administered the vaccine and rubbed alcohol.")
+        female_text = st.text_input("Female-assumed Text", "She administered the vaccine and rubbed alcohol.")
 
     enter = st.button("Compare")
     if enter:
@@ -188,7 +199,7 @@ For more cool information on sentence embeddings, see the [sBert project](https:
             softmax = [round(ts.item(), 4) for ts in torch.nn.functional.softmax(torch.from_numpy(value['score'].values))]
             if softmax[0] > softmax[1]:
                 gender = "male"
-            elif abs(softmax[0] - softmax[1]) < 1e-2:
+            elif abs(softmax[0] - softmax[1]) < 1e-3:
                 gender = "neutral"
             else:
                 gender = "female"