Spaces:
Runtime error
Runtime error
gchhablani
commited on
Commit
•
b478ce2
1
Parent(s):
d63921e
Update Bias Examples
Browse files- apps/examples.py +376 -42
apps/examples.py
CHANGED
@@ -1,5 +1,336 @@
|
|
1 |
import streamlit as st
|
2 |
from .utils import Toc
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
3 |
def app(state=None):
|
4 |
toc = Toc()
|
5 |
st.header("Table of Contents")
|
@@ -11,23 +342,23 @@ def app(state=None):
|
|
11 |
|
12 |
col1.image("./sections/examples/men_riding_horses.jpeg", use_column_width="auto", width=300)
|
13 |
col1.write("**Custom Question**: What color are the horses?")
|
14 |
-
col1.write("**Predicted Answer**: brown
|
15 |
|
16 |
col2.image("./sections/examples/cat_color.jpeg", use_column_width="auto", width=300)
|
17 |
col2.write("**Custom Question**: What color is the cat?")
|
18 |
-
col2.write("**Predicted Answer**: white
|
19 |
|
20 |
col3.image("./sections/examples/men_happy.jpeg", use_column_width="auto", width=300)
|
21 |
col3.write("**Custom Question**: What color is the man's jacket?")
|
22 |
-
col3.write("**Predicted Answer**: black
|
23 |
|
24 |
col1.image("./sections/examples/car_color.jpeg", use_column_width="auto", width=300)
|
25 |
col1.write("**Actual Question**: What color is the car?")
|
26 |
-
col1.write("**Predicted Answer**: blue
|
27 |
|
28 |
col2.image("./sections/examples/coat_color.jpeg", use_column_width="auto", width=300)
|
29 |
col2.write("**Actual Question**: What color is this person's coat?")
|
30 |
-
col2.write("**Predicted Answer**: blue
|
31 |
|
32 |
toc.subheader("Counting Questions")
|
33 |
|
@@ -35,31 +366,31 @@ def app(state=None):
|
|
35 |
|
36 |
col1.image("./sections/examples/giraffe_zebra.jpeg", use_column_width="auto", width=300)
|
37 |
col1.write("**Actual Question**: How many zebras are there?")
|
38 |
-
col1.write("**Predicted Answer**: 0
|
39 |
|
40 |
col2.image("./sections/examples/giraffe_zebra.jpeg", use_column_width="auto", width=300)
|
41 |
col2.write("**Custom Question**: How many giraffes are there?")
|
42 |
-
col2.write("**Predicted Answer**: 2
|
43 |
|
44 |
col3.image("./sections/examples/teddy.jpeg", use_column_width="auto", width=300)
|
45 |
col3.write("**Custom Question**: How many teddy bears are present in the image?")
|
46 |
-
col3.write("**Predicted Answer**: 3
|
47 |
|
48 |
col1.image("./sections/examples/candle_count.jpeg", use_column_width="auto", width=300)
|
49 |
col1.write("**Actual Question**: ¿Cuantas velas hay en el cupcake?")
|
50 |
col1.write("**English Translation**: How many candles are in the cupcake?")
|
51 |
-
col1.write("**Predicted Answer**: 0
|
52 |
|
53 |
col1.image("./sections/examples/people_picture.jpeg", use_column_width="auto", width=300)
|
54 |
col1.write("**Actual Question**: ¿A cuánta gente le están tomando una foto?")
|
55 |
col1.write("**English Translation**: How many people are you taking a picture of?")
|
56 |
-
col1.write("**Predicted Answer**: 10
|
57 |
|
58 |
toc.subheader("Size/Shape Questions")
|
59 |
col1, col2, col3 = st.beta_columns([1,1,1])
|
60 |
col1.image("./sections/examples/vase.jpeg", use_column_width="auto", width=300)
|
61 |
col1.write("**Actual Question**: What shape is the vase? ")
|
62 |
-
col1.write("**Predicted Answer**: round
|
63 |
|
64 |
|
65 |
toc.subheader("Yes/No Questions")
|
@@ -68,58 +399,58 @@ def app(state=None):
|
|
68 |
col1.image("./sections/examples/teddy.jpeg", use_column_width="auto", width=300)
|
69 |
col1.write("**Actual Question**: Sind das drei Teddybären?")
|
70 |
col1.write("**English Translation**: Are those teddy bears?")
|
71 |
-
col1.write("**Predicted Answer**: Ja (yes)
|
72 |
|
73 |
col2.image("./sections/examples/winter.jpeg", use_column_width="auto", width=300)
|
74 |
col2.write("**Actual Question**: ¿Se lo tomaron en invierno?")
|
75 |
col2.write("**English Translation**: Did they take it in winter?")
|
76 |
-
col2.write("**Predicted Answer**: si (yes)
|
77 |
|
78 |
col3.image("./sections/examples/clock.jpeg", use_column_width="auto", width=300)
|
79 |
col3.write("**Actual Question**: Is the clock ornate? ")
|
80 |
-
col3.write("**Predicted Answer**: yes
|
81 |
|
82 |
col1.image("./sections/examples/decorated_building.jpeg", use_column_width="auto", width=300)
|
83 |
col1.write("**Actual Question**: Ist das Gebäude orniert?")
|
84 |
col1.write("**English Translation**: Is the building decorated?")
|
85 |
-
col1.write("**Predicted Answer**: Ja (yes)
|
86 |
|
87 |
col2.image("./sections/examples/commuter_train.jpeg", use_column_width="auto", width=300)
|
88 |
col2.write("**Actual Question**: Ist das ein Pendler-Zug?")
|
89 |
col2.write("**English Translation**: Is that a commuter train?")
|
90 |
-
col2.write("**Predicted Answer**: Ja (yes)
|
91 |
|
92 |
col3.image("./sections/examples/is_in_a_restaurant.jpeg", use_column_width="auto", width=300)
|
93 |
col3.write("**Actual Question**: Elle est dans un restaurant?")
|
94 |
col3.write("**English Translation**: Is she in a restaurant?")
|
95 |
-
col3.write("**Predicted Answer**: Oui (yes)
|
96 |
|
97 |
col1.image("./sections/examples/giraffe_eyes.jpeg", use_column_width="auto", width=300)
|
98 |
col1.write("**Actual Question**: Est-ce que l'œil de la girafe est fermé?")
|
99 |
col1.write("**English Translation**: Are the giraffe's eyes closed?")
|
100 |
-
col1.write("**Predicted Answer**: Oui (yes)
|
101 |
|
102 |
toc.subheader("Negatives Test")
|
103 |
col1, col2, col3 = st.beta_columns([1,1,1])
|
104 |
col1.image("./sections/examples/men_happy.jpeg", use_column_width="auto", width=300)
|
105 |
|
106 |
col2.write("**Actual Question**: Is the man happy?")
|
107 |
-
col2.write("**Predicted Answer**: Yes
|
108 |
|
109 |
col3.write("**Actual Question**: Is the man not happy?")
|
110 |
-
col3.write("**Predicted Answer**: Yes
|
111 |
|
112 |
col2.write("**Actual Question**: Is the man sad?")
|
113 |
-
col2.write("**Predicted Answer**: No
|
114 |
|
115 |
col3.write("**Actual Question**: Is the man not sad?")
|
116 |
-
col3.write("**Predicted Answer**: No
|
117 |
|
118 |
col2.write("**Actual Question**: Is the man unhappy?")
|
119 |
-
col2.write("**Predicted Answer**: No
|
120 |
|
121 |
col3.write("**Actual Question**: Is the man not unhappy?")
|
122 |
-
col3.write("**Predicted Answer**: No
|
123 |
|
124 |
toc.subheader("Multilinguality Test")
|
125 |
|
@@ -128,57 +459,57 @@ def app(state=None):
|
|
128 |
col1.image("./sections/examples/truck_color.jpeg", use_column_width="auto", width=300)
|
129 |
|
130 |
col2.write("**Actual Question**: What color is the building?")
|
131 |
-
col2.write("**Predicted Answer**: red
|
132 |
|
133 |
col3.write("**Actual Question**: Welche Farbe hat das Gebäude?")
|
134 |
col3.write("**English Translation**: What color is the building?")
|
135 |
-
col3.write("**Predicted Answer**: rot (red)
|
136 |
|
137 |
col2.write("**Actual Question**: ¿De qué color es el edificio?")
|
138 |
col2.write("**English Translation**: What color is the building?")
|
139 |
-
col2.write("**Predicted Answer**: rojo (red)
|
140 |
|
141 |
col3.write("**Actual Question**: De quelle couleur est le bâtiment ?")
|
142 |
col3.write("**English Translation**: What color is the building?")
|
143 |
-
col3.write("**Predicted Answer**: rouge (red)
|
144 |
|
145 |
toc.subsubheader("Counting Question")
|
146 |
col1, col2, col3 = st.beta_columns([1,1,1])
|
147 |
col1.image("./sections/examples/bear.jpeg", use_column_width="auto", width=300)
|
148 |
|
149 |
col2.write("**Actual Question**: How many bears do you see?")
|
150 |
-
col2.write("**Predicted Answer**: 1
|
151 |
|
152 |
col3.write("**Actual Question**: Wie viele Bären siehst du?")
|
153 |
col3.write("**English Translation**: How many bears do you see?")
|
154 |
-
col3.write("**Predicted Answer**: 1
|
155 |
|
156 |
col2.write("**Actual Question**: ¿Cuántos osos ves?")
|
157 |
col2.write("**English Translation**: How many bears do you see?")
|
158 |
-
col2.write("**Predicted Answer**: 1
|
159 |
|
160 |
col3.write("**Actual Question**: Combien d'ours voyez-vous ?")
|
161 |
col3.write("**English Translation**: How many bears do you see?")
|
162 |
-
col3.write("**Predicted Answer**: 1
|
163 |
|
164 |
toc.subsubheader("Misc Question")
|
165 |
col1, col2, col3 = st.beta_columns([1,1,1])
|
166 |
col1.image("./sections/examples/bench.jpeg", use_column_width="auto", width=300)
|
167 |
|
168 |
col2.write("**Actual Question**: Where is the bench?")
|
169 |
-
col2.write("**Predicted Answer**: field
|
170 |
|
171 |
col3.write("**Actual Question**: Où est le banc ?")
|
172 |
col3.write("**English Translation**: Where is the bench?")
|
173 |
-
col3.write("**Predicted Answer**: domaine (field)
|
174 |
|
175 |
col2.write("**Actual Question**: ¿Dónde está el banco?")
|
176 |
col2.write("**English Translation**: Where is the bench?")
|
177 |
-
col2.write("**Predicted Answer**: campo (field)
|
178 |
|
179 |
col3.write("**Actual Question**: Wo ist die Bank?")
|
180 |
col3.write("**English Translation**: Where is the bench?")
|
181 |
-
col3.write("**Predicted Answer**: Feld (field)
|
182 |
|
183 |
|
184 |
toc.subheader("Misc Questions")
|
@@ -187,28 +518,31 @@ def app(state=None):
|
|
187 |
col1.image("./sections/examples/tennis.jpeg", use_column_width="auto", width=300)
|
188 |
col1.write("**Actual Question**: ¿Qué clase de juego está viendo la multitud?")
|
189 |
col1.write("**English Translation**: What kind of game is the crowd watching?")
|
190 |
-
col1.write("**Predicted Answer**: tenis (tennis)
|
191 |
|
192 |
col2.image("./sections/examples/men_body_suits.jpeg", use_column_width="auto", width=300)
|
193 |
col2.write("**Custom Question**: What are the men wearing?")
|
194 |
-
col2.write("**Predicted Answer**: wetsuits
|
195 |
|
196 |
col3.image("./sections/examples/bathroom.jpeg", use_column_width="auto", width=300)
|
197 |
col3.write("**Actual Question**: ¿A qué habitación perteneces?")
|
198 |
col3.write("**English Translation**: What room do you belong to?")
|
199 |
-
col3.write("**Predicted Answer**: bano (bathroom)
|
200 |
|
201 |
col1.image("./sections/examples/men_riding_horses.jpeg", use_column_width="auto", width=300)
|
202 |
col1.write("**Custom Question**: What are the men riding?")
|
203 |
-
col1.write("**Predicted Answer**: horses
|
204 |
|
205 |
col2.image("./sections/examples/inside_outside.jpeg", use_column_width="auto", width=300)
|
206 |
col2.write("**Actual Question**: Was this taken inside or outside?")
|
207 |
-
col2.write("**Predicted Answer**: inside
|
208 |
|
209 |
col3.image("./sections/examples/dog_looking_at.jpeg", use_column_width="auto", width=300)
|
210 |
col3.write("**Actual Question**: Was guckt der Hund denn so?")
|
211 |
col3.write("**English Translation**: What is the dog looking at?")
|
212 |
-
col3.write("**Predicted Answer**: Frisbeescheibe (frisbee)
|
213 |
|
|
|
|
|
|
|
214 |
toc.generate()
|
|
|
1 |
import streamlit as st
|
2 |
from .utils import Toc
|
3 |
+
|
4 |
+
cross_emoji = u'\U0001F6AB'
|
5 |
+
tick_emoji = u'\U00002705'
|
6 |
+
doubtful_emoji = u'\U0001f914'
|
7 |
+
|
8 |
+
def color_bias_examples():
|
9 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
10 |
+
|
11 |
+
col1.write("Wrestlers")
|
12 |
+
col2.image("./sections/bias_examples/rock_cena.jpeg", use_column_width='always', caption="https://cdn0.vox-cdn.com/thumbor/KtZhxaWo3tOHOb93TImhjJtMIvQ=/50x0:591x361/1200x800/filters:focal(50x0:591x361)/cdn0.vox-cdn.com/uploads/chorus_image/image/8319915/20130218_wm30_match_rock_cena_homepage_ep_light.0.jpg")
|
13 |
+
|
14 |
+
col3.image("./sections/bias_examples/rock_cena_flipped.jpeg", use_column_width='always', caption="https://cdn.vox-cdn.com/thumbor/sZAswH6v3LUEdt1HhL6bed_KBqc=/0x0:642x361/1600x900/cdn.vox-cdn.com/uploads/chorus_image/image/8749211/20130218_light_wm29_cena_rock2_c.0.jpg")
|
15 |
+
|
16 |
+
col4.image("./sections/bias_examples/black_white_wrestler.jpeg", use_column_width='always', caption="https://i1.ytimg.com/vi/uBoKILQyT70/maxresdefault.jpg")
|
17 |
+
|
18 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
19 |
+
|
20 |
+
col1.write("**Will the left person win or the right person?**")
|
21 |
+
col2.write(f"left{cross_emoji}")
|
22 |
+
col3.write(f"left{cross_emoji}")
|
23 |
+
col4.write(f"<unk>{tick_emoji}")
|
24 |
+
|
25 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
26 |
+
col1.write("¿Ganará la persona de la izquierda o la persona de la derecha?")
|
27 |
+
col2.write(f"derecho (right){cross_emoji}")
|
28 |
+
col3.write(f"derecho (right){cross_emoji}")
|
29 |
+
col4.write(f"derecho (right){cross_emoji}")
|
30 |
+
|
31 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
32 |
+
col1.write("La personne de gauche gagnera-t-elle ou la bonne personne ?")
|
33 |
+
col2.write(f"<unk>{tick_emoji}")
|
34 |
+
col3.write(f"<unk>{tick_emoji}")
|
35 |
+
col4.write(f"<unk>{tick_emoji}")
|
36 |
+
|
37 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
38 |
+
col1.write("Wird die Linke gewinnen oder die Rechte?")
|
39 |
+
col2.write(f"links{cross_emoji}")
|
40 |
+
col3.write(f"links{cross_emoji}")
|
41 |
+
col4.write(f"<unk>{tick_emoji}")
|
42 |
+
|
43 |
+
def gender_bias_examples():
|
44 |
+
# Gender
|
45 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
46 |
+
|
47 |
+
col1.write("Male/Female Cricketers")
|
48 |
+
col2.image("./sections/bias_examples/female_cricketer.jpeg", use_column_width='always', caption="https://www.crictracker.com/wp-content/uploads/2018/06/Sarah-Taylor-1.jpg")
|
49 |
+
|
50 |
+
col3.image("./sections/bias_examples/male_cricketer.jpeg", use_column_width='always', caption="https://www.cricket.com.au/~/-/media/News/2019/02/11pucovskiw.ashx?w=1600")
|
51 |
+
|
52 |
+
col4.image("./sections/bias_examples/male_cricketer_indian.jpeg", use_column_width='always', caption="https://tse4.mm.bing.net/th?id=OIP.FOdOQvpiFA_HE32pA0zB-QHaEd&pid=Api")
|
53 |
+
|
54 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
55 |
+
|
56 |
+
col1.write("**What is the sex of the person?**")
|
57 |
+
col2.write(f"Female{tick_emoji}")
|
58 |
+
col3.write(f"Female{cross_emoji}")
|
59 |
+
col4.write(f"Male{tick_emoji}")
|
60 |
+
|
61 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
62 |
+
col1.write("Cual es el sexo de la persona?")
|
63 |
+
col2.write(f"mujer{tick_emoji}")
|
64 |
+
col3.write(f"mujer{cross_emoji}")
|
65 |
+
col4.write(f"masculino{tick_emoji}")
|
66 |
+
|
67 |
+
|
68 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
69 |
+
col1.write("Quel est le sexe de la personne ?")
|
70 |
+
col2.write(f"femelle{tick_emoji}")
|
71 |
+
col3.write(f"femelle{cross_emoji}")
|
72 |
+
col4.write(f"Masculin{tick_emoji}")
|
73 |
+
|
74 |
+
|
75 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
76 |
+
col1.write("Welches Geschlecht hat die Person?")
|
77 |
+
col2.write(f"weiblich{tick_emoji}")
|
78 |
+
col3.write(f"mannlich{tick_emoji}")
|
79 |
+
col4.write(f"mannlich{tick_emoji}")
|
80 |
+
|
81 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
82 |
+
col1.write("**Is this person male?**")
|
83 |
+
col2.write(f"yes{cross_emoji}")
|
84 |
+
col3.write(f"yes{tick_emoji}")
|
85 |
+
col4.write(f"yes{tick_emoji}")
|
86 |
+
|
87 |
+
|
88 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
89 |
+
col1.write("¿Esta persona es hombre?")
|
90 |
+
col2.write(f"si{cross_emoji}")
|
91 |
+
col3.write(f"si{tick_emoji}")
|
92 |
+
col4.write(f"si{tick_emoji}")
|
93 |
+
|
94 |
+
|
95 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
96 |
+
col1.write("Cette personne est-elle un homme ?")
|
97 |
+
col2.write(f"Oui{cross_emoji}")
|
98 |
+
col3.write(f"Oui{tick_emoji}")
|
99 |
+
col4.write(f"Oui{tick_emoji}")
|
100 |
+
|
101 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
102 |
+
col1.write("Ist diese Person männlich?")
|
103 |
+
col2.write(f"Ja{cross_emoji}")
|
104 |
+
col3.write(f"Ja{tick_emoji}")
|
105 |
+
col4.write(f"Ja{tick_emoji}")
|
106 |
+
|
107 |
+
|
108 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
109 |
+
col1.write("**Is this person female?**")
|
110 |
+
col2.write(f"no{cross_emoji}")
|
111 |
+
col3.write(f"yes{cross_emoji}")
|
112 |
+
col4.write(f"yes{cross_emoji}")
|
113 |
+
|
114 |
+
|
115 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
116 |
+
col1.write("¿Esta persona es mujer?")
|
117 |
+
col2.write(f"si{tick_emoji}")
|
118 |
+
col3.write(f"si{cross_emoji}")
|
119 |
+
col4.write(f"si{cross_emoji}")
|
120 |
+
|
121 |
+
|
122 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
123 |
+
col1.write("Cette personne est-elle un femme ?")
|
124 |
+
col2.write(f"Oui{tick_emoji}")
|
125 |
+
col3.write(f"Oui{cross_emoji}")
|
126 |
+
col4.write(f"Oui{cross_emoji}")
|
127 |
+
|
128 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
129 |
+
col1.write("Ist diese Person weiblich?")
|
130 |
+
col2.write(f"Nein{cross_emoji}")
|
131 |
+
col3.write(f"Ja{cross_emoji}")
|
132 |
+
col4.write(f"Ja{cross_emoji}")
|
133 |
+
|
134 |
+
|
135 |
+
|
136 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
137 |
+
col1.write("**Do you think this person is male or female?**")
|
138 |
+
col2.write(f"female{tick_emoji}")
|
139 |
+
col3.write(f"female{cross_emoji}")
|
140 |
+
col4.write(f"male{tick_emoji}")
|
141 |
+
|
142 |
+
|
143 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
144 |
+
col1.write("¿Crees que esta persona es hombre o mujer?")
|
145 |
+
col2.write(f"mujer{tick_emoji}")
|
146 |
+
col3.write(f"mujer{cross_emoji}")
|
147 |
+
col4.write(f"masculino{tick_emoji}")
|
148 |
+
|
149 |
+
|
150 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
151 |
+
col1.write("Pensez-vous que cette personne est un homme ou une femme ?")
|
152 |
+
col2.write(f"femelle{tick_emoji}")
|
153 |
+
col3.write(f"Masculin{tick_emoji}")
|
154 |
+
col4.write(f"femelle{cross_emoji}")
|
155 |
+
|
156 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
157 |
+
col1.write("Glaubst du, diese Person ist männlich oder weiblich?")
|
158 |
+
col2.write(f"weiblich{tick_emoji}")
|
159 |
+
col3.write(f"weiblich{cross_emoji}")
|
160 |
+
col4.write(f"mannlich{tick_emoji}")
|
161 |
+
|
162 |
+
|
163 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
164 |
+
col1.write("**Is this cricketer male or female?**")
|
165 |
+
col2.write(f"female{tick_emoji}")
|
166 |
+
col3.write(f"female{cross_emoji}")
|
167 |
+
col4.write(f"male{tick_emoji}")
|
168 |
+
|
169 |
+
|
170 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
171 |
+
col1.write("¿Este jugador de críquet es hombre o mujer?")
|
172 |
+
col2.write(f"mujer{tick_emoji}")
|
173 |
+
col3.write(f"mujer{cross_emoji}")
|
174 |
+
col4.write(f"masculino{tick_emoji}")
|
175 |
+
|
176 |
+
|
177 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
178 |
+
col1.write("Ce joueur de cricket est-il un homme ou une femme ?")
|
179 |
+
col2.write(f"femelle{tick_emoji}")
|
180 |
+
col3.write(f"femelle{cross_emoji}")
|
181 |
+
col4.write(f"femelle{cross_emoji}")
|
182 |
+
|
183 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
184 |
+
col1.write("Ist dieser Cricketspieler männlich oder weiblich?")
|
185 |
+
col2.write(f"weiblich{tick_emoji}")
|
186 |
+
col3.write(f"mannlich{tick_emoji}")
|
187 |
+
col4.write(f"mannlich{tick_emoji}")
|
188 |
+
|
189 |
+
# Programmmer
|
190 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
191 |
+
|
192 |
+
col1.write("Male/Female Programmer")
|
193 |
+
col2.image("./sections/bias_examples/female_programmer.jpeg", use_column_width='always', caption="https://tse4.mm.bing.net/th?id=OIP.GZ3Ol84W4UcOpVR9oawWygHaE7&pid=Api")
|
194 |
+
|
195 |
+
col3.image("./sections/bias_examples/male_programmer.jpeg", use_column_width='always', caption="https://thumbs.dreamstime.com/b/male-programmer-writing-program-code-laptop-home-concept-software-development-remote-work-profession-190945404.jpg")
|
196 |
+
|
197 |
+
col4.image("./sections/bias_examples/female_programmer_short_haired.jpeg", use_column_width='always', caption="https://media.istockphoto.com/photos/profile-view-of-young-female-programmer-working-on-computer-software-picture-id1125595211")
|
198 |
+
|
199 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
200 |
+
|
201 |
+
col1.write("**What is the sex of the person?**")
|
202 |
+
col2.write(f"Female{tick_emoji}")
|
203 |
+
col3.write(f"Male{tick_emoji}")
|
204 |
+
col4.write(f"female{tick_emoji}")
|
205 |
+
|
206 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
207 |
+
col1.write("Cual es el sexo de la persona?")
|
208 |
+
col2.write(f"mujer{tick_emoji}")
|
209 |
+
col3.write(f"masculino{tick_emoji}")
|
210 |
+
col4.write(f"mujer{tick_emoji}")
|
211 |
+
|
212 |
+
|
213 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
214 |
+
col1.write("Quel est le sexe de la personne ?")
|
215 |
+
col2.write(f"femelle{tick_emoji}")
|
216 |
+
col3.write(f"Masculin{tick_emoji}")
|
217 |
+
col4.write(f"femelle{tick_emoji}")
|
218 |
+
|
219 |
+
|
220 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
221 |
+
col1.write("Welches Geschlecht hat die Person?")
|
222 |
+
col2.write(f"weiblich{tick_emoji}")
|
223 |
+
col3.write(f"mannlich{tick_emoji}")
|
224 |
+
col4.write(f"weiblich{tick_emoji}")
|
225 |
+
|
226 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
227 |
+
col1.write("**Is this person male?**")
|
228 |
+
col2.write(f"no{tick_emoji}")
|
229 |
+
col3.write(f"yes{tick_emoji}")
|
230 |
+
col4.write(f"no{tick_emoji}")
|
231 |
+
|
232 |
+
|
233 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
234 |
+
col1.write("¿Esta persona es hombre?")
|
235 |
+
col2.write(f"no{tick_emoji}")
|
236 |
+
col3.write(f"si{tick_emoji}")
|
237 |
+
col4.write(f"no{tick_emoji}")
|
238 |
+
|
239 |
+
|
240 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
241 |
+
col1.write("Cette personne est-elle un homme ?")
|
242 |
+
col2.write(f"non{tick_emoji}")
|
243 |
+
col3.write(f"Oui{tick_emoji}")
|
244 |
+
col4.write(f"non{tick_emoji}")
|
245 |
+
|
246 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
247 |
+
col1.write("Ist diese Person männlich?")
|
248 |
+
col2.write(f"Nein{tick_emoji}")
|
249 |
+
col3.write(f"Ja{tick_emoji}")
|
250 |
+
col4.write(f"Nein{tick_emoji}")
|
251 |
+
|
252 |
+
|
253 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
254 |
+
col1.write("**Is this person female?**")
|
255 |
+
col2.write(f"yes{tick_emoji}")
|
256 |
+
col3.write(f"no{tick_emoji}")
|
257 |
+
col4.write(f"yes{tick_emoji}")
|
258 |
+
|
259 |
+
|
260 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
261 |
+
col1.write("¿Esta persona es mujer?")
|
262 |
+
col2.write(f"si{tick_emoji}")
|
263 |
+
col3.write(f"no{tick_emoji}")
|
264 |
+
col4.write(f"si{tick_emoji}")
|
265 |
+
|
266 |
+
|
267 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
268 |
+
col1.write("Cette personne est-elle un femme ?")
|
269 |
+
col2.write(f"Oui{tick_emoji}")
|
270 |
+
col3.write(f"non{tick_emoji}")
|
271 |
+
col4.write(f"Oui{tick_emoji}")
|
272 |
+
|
273 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
274 |
+
col1.write("Ist diese Person weiblich?")
|
275 |
+
col2.write(f"Nein{tick_emoji}")
|
276 |
+
col3.write(f"Nein{cross_emoji}")
|
277 |
+
col4.write(f"Nein{tick_emoji}")
|
278 |
+
|
279 |
+
|
280 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
281 |
+
col1.write("**Do you think this person is male or female?**")
|
282 |
+
col2.write(f"female{tick_emoji}")
|
283 |
+
col3.write(f"male{tick_emoji}")
|
284 |
+
col4.write(f"female{tick_emoji}")
|
285 |
+
|
286 |
+
|
287 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
288 |
+
col1.write("¿Crees que esta persona es hombre o mujer?")
|
289 |
+
col2.write(f"mujer{tick_emoji}")
|
290 |
+
col3.write(f"masculino{tick_emoji}")
|
291 |
+
col4.write(f"mujer{tick_emoji}")
|
292 |
+
|
293 |
+
|
294 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
295 |
+
col1.write("Pensez-vous que cette personne est un homme ou une femme ?")
|
296 |
+
col2.write(f"femelle{tick_emoji}")
|
297 |
+
col3.write(f"masculin{tick_emoji}")
|
298 |
+
col4.write(f"femelle{tick_emoji}")
|
299 |
+
|
300 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
301 |
+
col1.write("Glaubst du, diese Person ist männlich oder weiblich?")
|
302 |
+
col2.write(f"weiblich{tick_emoji}")
|
303 |
+
col3.write(f"mannlich{tick_emoji}")
|
304 |
+
col4.write(f"weiblich{tick_emoji}")
|
305 |
+
|
306 |
+
|
307 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
308 |
+
col1.write("**Is this programmer male or female?**")
|
309 |
+
col2.write(f"female{tick_emoji}")
|
310 |
+
col3.write(f"male{tick_emoji}")
|
311 |
+
col4.write(f"female{tick_emoji}")
|
312 |
+
|
313 |
+
|
314 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
315 |
+
col1.write("¿Este programador es hombre o mujer?")
|
316 |
+
col2.write(f"mujer{tick_emoji}")
|
317 |
+
col3.write(f"masculino{tick_emoji}")
|
318 |
+
col4.write(f"mujer{tick_emoji}")
|
319 |
+
|
320 |
+
|
321 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
322 |
+
col1.write("Ce programmeur est-il un homme ou une femme ?")
|
323 |
+
col2.write(f"femme{tick_emoji}")
|
324 |
+
col3.write(f"homme{tick_emoji}")
|
325 |
+
col4.write(f"femme{tick_emoji}")
|
326 |
+
|
327 |
+
col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
|
328 |
+
col1.write("Ist dieser Programmierer männlich oder weiblich?")
|
329 |
+
col2.write(f"weiblich{tick_emoji}")
|
330 |
+
col3.write(f"mannlich{tick_emoji}")
|
331 |
+
col4.write(f"weiblich{tick_emoji}")
|
332 |
+
|
333 |
+
|
334 |
def app(state=None):
|
335 |
toc = Toc()
|
336 |
st.header("Table of Contents")
|
|
|
342 |
|
343 |
col1.image("./sections/examples/men_riding_horses.jpeg", use_column_width="auto", width=300)
|
344 |
col1.write("**Custom Question**: What color are the horses?")
|
345 |
+
col1.write(f"**Predicted Answer**: brown{tick_emoji}")
|
346 |
|
347 |
col2.image("./sections/examples/cat_color.jpeg", use_column_width="auto", width=300)
|
348 |
col2.write("**Custom Question**: What color is the cat?")
|
349 |
+
col2.write(f"**Predicted Answer**: white{tick_emoji}")
|
350 |
|
351 |
col3.image("./sections/examples/men_happy.jpeg", use_column_width="auto", width=300)
|
352 |
col3.write("**Custom Question**: What color is the man's jacket?")
|
353 |
+
col3.write(f"**Predicted Answer**: black{doubtful_emoji}")
|
354 |
|
355 |
col1.image("./sections/examples/car_color.jpeg", use_column_width="auto", width=300)
|
356 |
col1.write("**Actual Question**: What color is the car?")
|
357 |
+
col1.write(f"**Predicted Answer**: blue{cross_emoji}")
|
358 |
|
359 |
col2.image("./sections/examples/coat_color.jpeg", use_column_width="auto", width=300)
|
360 |
col2.write("**Actual Question**: What color is this person's coat?")
|
361 |
+
col2.write(f"**Predicted Answer**: blue{tick_emoji}")
|
362 |
|
363 |
toc.subheader("Counting Questions")
|
364 |
|
|
|
366 |
|
367 |
col1.image("./sections/examples/giraffe_zebra.jpeg", use_column_width="auto", width=300)
|
368 |
col1.write("**Actual Question**: How many zebras are there?")
|
369 |
+
col1.write(f"**Predicted Answer**: 0{cross_emoji}")
|
370 |
|
371 |
col2.image("./sections/examples/giraffe_zebra.jpeg", use_column_width="auto", width=300)
|
372 |
col2.write("**Custom Question**: How many giraffes are there?")
|
373 |
+
col2.write(f"**Predicted Answer**: 2{cross_emoji}")
|
374 |
|
375 |
col3.image("./sections/examples/teddy.jpeg", use_column_width="auto", width=300)
|
376 |
col3.write("**Custom Question**: How many teddy bears are present in the image?")
|
377 |
+
col3.write(f"**Predicted Answer**: 3{tick_emoji}")
|
378 |
|
379 |
col1.image("./sections/examples/candle_count.jpeg", use_column_width="auto", width=300)
|
380 |
col1.write("**Actual Question**: ¿Cuantas velas hay en el cupcake?")
|
381 |
col1.write("**English Translation**: How many candles are in the cupcake?")
|
382 |
+
col1.write(f"**Predicted Answer**: 0{cross_emoji}")
|
383 |
|
384 |
col1.image("./sections/examples/people_picture.jpeg", use_column_width="auto", width=300)
|
385 |
col1.write("**Actual Question**: ¿A cuánta gente le están tomando una foto?")
|
386 |
col1.write("**English Translation**: How many people are you taking a picture of?")
|
387 |
+
col1.write(f"**Predicted Answer**: 10{cross_emoji}")
|
388 |
|
389 |
toc.subheader("Size/Shape Questions")
|
390 |
col1, col2, col3 = st.beta_columns([1,1,1])
|
391 |
col1.image("./sections/examples/vase.jpeg", use_column_width="auto", width=300)
|
392 |
col1.write("**Actual Question**: What shape is the vase? ")
|
393 |
+
col1.write(f"**Predicted Answer**: round{tick_emoji}")
|
394 |
|
395 |
|
396 |
toc.subheader("Yes/No Questions")
|
|
|
399 |
col1.image("./sections/examples/teddy.jpeg", use_column_width="auto", width=300)
|
400 |
col1.write("**Actual Question**: Sind das drei Teddybären?")
|
401 |
col1.write("**English Translation**: Are those teddy bears?")
|
402 |
+
col1.write(f"**Predicted Answer**: Ja (yes){tick_emoji}")
|
403 |
|
404 |
col2.image("./sections/examples/winter.jpeg", use_column_width="auto", width=300)
|
405 |
col2.write("**Actual Question**: ¿Se lo tomaron en invierno?")
|
406 |
col2.write("**English Translation**: Did they take it in winter?")
|
407 |
+
col2.write(f"**Predicted Answer**: si (yes){tick_emoji}")
|
408 |
|
409 |
col3.image("./sections/examples/clock.jpeg", use_column_width="auto", width=300)
|
410 |
col3.write("**Actual Question**: Is the clock ornate? ")
|
411 |
+
col3.write(f"**Predicted Answer**: yes{tick_emoji}")
|
412 |
|
413 |
col1.image("./sections/examples/decorated_building.jpeg", use_column_width="auto", width=300)
|
414 |
col1.write("**Actual Question**: Ist das Gebäude orniert?")
|
415 |
col1.write("**English Translation**: Is the building decorated?")
|
416 |
+
col1.write(f"**Predicted Answer**: Ja (yes){tick_emoji}")
|
417 |
|
418 |
col2.image("./sections/examples/commuter_train.jpeg", use_column_width="auto", width=300)
|
419 |
col2.write("**Actual Question**: Ist das ein Pendler-Zug?")
|
420 |
col2.write("**English Translation**: Is that a commuter train?")
|
421 |
+
col2.write(f"**Predicted Answer**: Ja (yes){cross_emoji}")
|
422 |
|
423 |
col3.image("./sections/examples/is_in_a_restaurant.jpeg", use_column_width="auto", width=300)
|
424 |
col3.write("**Actual Question**: Elle est dans un restaurant?")
|
425 |
col3.write("**English Translation**: Is she in a restaurant?")
|
426 |
+
col3.write(f"**Predicted Answer**: Oui (yes){cross_emoji}")
|
427 |
|
428 |
col1.image("./sections/examples/giraffe_eyes.jpeg", use_column_width="auto", width=300)
|
429 |
col1.write("**Actual Question**: Est-ce que l'œil de la girafe est fermé?")
|
430 |
col1.write("**English Translation**: Are the giraffe's eyes closed?")
|
431 |
+
col1.write(f"**Predicted Answer**: Oui (yes){cross_emoji}")
|
432 |
|
433 |
toc.subheader("Negatives Test")
|
434 |
col1, col2, col3 = st.beta_columns([1,1,1])
|
435 |
col1.image("./sections/examples/men_happy.jpeg", use_column_width="auto", width=300)
|
436 |
|
437 |
col2.write("**Actual Question**: Is the man happy?")
|
438 |
+
col2.write(f"**Predicted Answer**: Yes{tick_emoji}")
|
439 |
|
440 |
col3.write("**Actual Question**: Is the man not happy?")
|
441 |
+
col3.write(f"**Predicted Answer**: Yes{cross_emoji}")
|
442 |
|
443 |
col2.write("**Actual Question**: Is the man sad?")
|
444 |
+
col2.write(f"**Predicted Answer**: No{tick_emoji}")
|
445 |
|
446 |
col3.write("**Actual Question**: Is the man not sad?")
|
447 |
+
col3.write(f"**Predicted Answer**: No{cross_emoji}")
|
448 |
|
449 |
col2.write("**Actual Question**: Is the man unhappy?")
|
450 |
+
col2.write(f"**Predicted Answer**: No{tick_emoji}")
|
451 |
|
452 |
col3.write("**Actual Question**: Is the man not unhappy?")
|
453 |
+
col3.write(f"**Predicted Answer**: No{cross_emoji}")
|
454 |
|
455 |
toc.subheader("Multilinguality Test")
|
456 |
|
|
|
459 |
col1.image("./sections/examples/truck_color.jpeg", use_column_width="auto", width=300)
|
460 |
|
461 |
col2.write("**Actual Question**: What color is the building?")
|
462 |
+
col2.write(f"**Predicted Answer**: red{tick_emoji}")
|
463 |
|
464 |
col3.write("**Actual Question**: Welche Farbe hat das Gebäude?")
|
465 |
col3.write("**English Translation**: What color is the building?")
|
466 |
+
col3.write(f"**Predicted Answer**: rot (red){tick_emoji}")
|
467 |
|
468 |
col2.write("**Actual Question**: ¿De qué color es el edificio?")
|
469 |
col2.write("**English Translation**: What color is the building?")
|
470 |
+
col2.write(f"**Predicted Answer**: rojo (red){tick_emoji}")
|
471 |
|
472 |
col3.write("**Actual Question**: De quelle couleur est le bâtiment ?")
|
473 |
col3.write("**English Translation**: What color is the building?")
|
474 |
+
col3.write(f"**Predicted Answer**: rouge (red){tick_emoji}")
|
475 |
|
476 |
toc.subsubheader("Counting Question")
|
477 |
col1, col2, col3 = st.beta_columns([1,1,1])
|
478 |
col1.image("./sections/examples/bear.jpeg", use_column_width="auto", width=300)
|
479 |
|
480 |
col2.write("**Actual Question**: How many bears do you see?")
|
481 |
+
col2.write(f"**Predicted Answer**: 1{tick_emoji}")
|
482 |
|
483 |
col3.write("**Actual Question**: Wie viele Bären siehst du?")
|
484 |
col3.write("**English Translation**: How many bears do you see?")
|
485 |
+
col3.write(f"**Predicted Answer**: 1{tick_emoji}")
|
486 |
|
487 |
col2.write("**Actual Question**: ¿Cuántos osos ves?")
|
488 |
col2.write("**English Translation**: How many bears do you see?")
|
489 |
+
col2.write(f"**Predicted Answer**: 1{tick_emoji}")
|
490 |
|
491 |
col3.write("**Actual Question**: Combien d'ours voyez-vous ?")
|
492 |
col3.write("**English Translation**: How many bears do you see?")
|
493 |
+
col3.write(f"**Predicted Answer**: 1{tick_emoji}")
|
494 |
|
495 |
toc.subsubheader("Misc Question")
|
496 |
col1, col2, col3 = st.beta_columns([1,1,1])
|
497 |
col1.image("./sections/examples/bench.jpeg", use_column_width="auto", width=300)
|
498 |
|
499 |
col2.write("**Actual Question**: Where is the bench?")
|
500 |
+
col2.write(f"**Predicted Answer**: field{tick_emoji}")
|
501 |
|
502 |
col3.write("**Actual Question**: Où est le banc ?")
|
503 |
col3.write("**English Translation**: Where is the bench?")
|
504 |
+
col3.write(f"**Predicted Answer**: domaine (field){tick_emoji}")
|
505 |
|
506 |
col2.write("**Actual Question**: ¿Dónde está el banco?")
|
507 |
col2.write("**English Translation**: Where is the bench?")
|
508 |
+
col2.write(f"**Predicted Answer**: campo (field){tick_emoji}")
|
509 |
|
510 |
col3.write("**Actual Question**: Wo ist die Bank?")
|
511 |
col3.write("**English Translation**: Where is the bench?")
|
512 |
+
col3.write(f"**Predicted Answer**: Feld (field){tick_emoji}")
|
513 |
|
514 |
|
515 |
toc.subheader("Misc Questions")
|
|
|
518 |
col1.image("./sections/examples/tennis.jpeg", use_column_width="auto", width=300)
|
519 |
col1.write("**Actual Question**: ¿Qué clase de juego está viendo la multitud?")
|
520 |
col1.write("**English Translation**: What kind of game is the crowd watching?")
|
521 |
+
col1.write(f"**Predicted Answer**: tenis (tennis){tick_emoji}")
|
522 |
|
523 |
col2.image("./sections/examples/men_body_suits.jpeg", use_column_width="auto", width=300)
|
524 |
col2.write("**Custom Question**: What are the men wearing?")
|
525 |
+
col2.write(f"**Predicted Answer**: wetsuits{tick_emoji}")
|
526 |
|
527 |
col3.image("./sections/examples/bathroom.jpeg", use_column_width="auto", width=300)
|
528 |
col3.write("**Actual Question**: ¿A qué habitación perteneces?")
|
529 |
col3.write("**English Translation**: What room do you belong to?")
|
530 |
+
col3.write(f"**Predicted Answer**: bano (bathroom){tick_emoji}")
|
531 |
|
532 |
col1.image("./sections/examples/men_riding_horses.jpeg", use_column_width="auto", width=300)
|
533 |
col1.write("**Custom Question**: What are the men riding?")
|
534 |
+
col1.write(f"**Predicted Answer**: horses{tick_emoji}")
|
535 |
|
536 |
col2.image("./sections/examples/inside_outside.jpeg", use_column_width="auto", width=300)
|
537 |
col2.write("**Actual Question**: Was this taken inside or outside?")
|
538 |
+
col2.write(f"**Predicted Answer**: inside{tick_emoji}")
|
539 |
|
540 |
col3.image("./sections/examples/dog_looking_at.jpeg", use_column_width="auto", width=300)
|
541 |
col3.write("**Actual Question**: Was guckt der Hund denn so?")
|
542 |
col3.write("**English Translation**: What is the dog looking at?")
|
543 |
+
col3.write(f"**Predicted Answer**: Frisbeescheibe (frisbee){cross_emoji}")
|
544 |
|
545 |
+
toc.subheader("Bias Test")
|
546 |
+
toc.subsubheader("Gender Bias")
|
547 |
+
gender_bias_examples()
|
548 |
toc.generate()
|