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  1. .gitattributes +31 -0
  2. .ipynb_checkpoints/tests-checkpoint.ipynb +287 -0
  3. README.md +13 -0
  4. app.py +97 -0
  5. promptsadjectives.csv +151 -0
  6. requirements.txt +2 -0
  7. zipped_images/.ipynb_checkpoints.zip +3 -0
  8. zipped_images/dall-e2/aerospace_engineer.zip +3 -0
  9. zipped_images/dall-e2/aide.zip +3 -0
  10. zipped_images/dall-e2/air_conditioning_installer.zip +3 -0
  11. zipped_images/dall-e2/ambitious_accountant.zip +3 -0
  12. zipped_images/dall-e2/ambitious_aerospace_engineer.zip +3 -0
  13. zipped_images/dall-e2/ambitious_aide.zip +3 -0
  14. zipped_images/dall-e2/ambitious_air_conditioning_installer.zip +3 -0
  15. zipped_images/dall-e2/ambitious_architect.zip +3 -0
  16. zipped_images/dall-e2/ambitious_author.zip +3 -0
  17. zipped_images/dall-e2/ambitious_baker.zip +3 -0
  18. zipped_images/dall-e2/ambitious_bartender.zip +3 -0
  19. zipped_images/dall-e2/ambitious_bus_driver.zip +3 -0
  20. zipped_images/dall-e2/ambitious_career_counselor.zip +3 -0
  21. zipped_images/dall-e2/ambitious_carpet_installer.zip +3 -0
  22. zipped_images/dall-e2/ambitious_cashier.zip +3 -0
  23. zipped_images/dall-e2/ambitious_ceo.zip +3 -0
  24. zipped_images/dall-e2/ambitious_childcare_worker.zip +3 -0
  25. zipped_images/dall-e2/ambitious_civil_engineer.zip +3 -0
  26. zipped_images/dall-e2/ambitious_cleaner.zip +3 -0
  27. zipped_images/dall-e2/ambitious_clergy.zip +3 -0
  28. zipped_images/dall-e2/ambitious_clerk.zip +3 -0
  29. zipped_images/dall-e2/ambitious_coach.zip +3 -0
  30. zipped_images/dall-e2/ambitious_community_manager.zip +3 -0
  31. zipped_images/dall-e2/ambitious_compliance_officer.zip +3 -0
  32. zipped_images/dall-e2/ambitious_computer_programmer.zip +3 -0
  33. zipped_images/dall-e2/ambitious_computer_support_specialist.zip +3 -0
  34. zipped_images/dall-e2/ambitious_computer_systems_analyst.zip +3 -0
  35. zipped_images/dall-e2/ambitious_construction_worker.zip +3 -0
  36. zipped_images/dall-e2/ambitious_cook.zip +3 -0
  37. zipped_images/dall-e2/ambitious_correctional_officer.zip +3 -0
  38. zipped_images/dall-e2/ambitious_courier.zip +3 -0
  39. zipped_images/dall-e2/ambitious_credit_counselor.zip +3 -0
  40. zipped_images/dall-e2/ambitious_customer_service_representative.zip +3 -0
  41. zipped_images/dall-e2/ambitious_data_entry_keyer.zip +3 -0
  42. zipped_images/dall-e2/ambitious_dental_assistant.zip +3 -0
  43. zipped_images/dall-e2/ambitious_dentist.zip +3 -0
  44. zipped_images/dall-e2/ambitious_designer.zip +3 -0
  45. zipped_images/dall-e2/ambitious_detective.zip +3 -0
  46. zipped_images/dall-e2/ambitious_director.zip +3 -0
  47. zipped_images/dall-e2/ambitious_dishwasher.zip +3 -0
  48. zipped_images/dall-e2/ambitious_dispatcher.zip +3 -0
  49. zipped_images/dall-e2/ambitious_doctor.zip +3 -0
  50. zipped_images/dall-e2/ambitious_drywall_installer.zip +3 -0
.gitattributes ADDED
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+ *.h5 filter=lfs diff=lfs merge=lfs -text
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+ *.joblib filter=lfs diff=lfs merge=lfs -text
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+ *.lfs.* filter=lfs diff=lfs merge=lfs -text
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+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+ *.tflite filter=lfs diff=lfs merge=lfs -text
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+ *.tgz filter=lfs diff=lfs merge=lfs -text
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.ipynb_checkpoints/tests-checkpoint.ipynb ADDED
@@ -0,0 +1,287 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": 60,
6
+ "id": "49140416",
7
+ "metadata": {},
8
+ "outputs": [],
9
+ "source": [
10
+ "import os,re\n",
11
+ "import pandas as pd\n",
12
+ "import numpy as np"
13
+ ]
14
+ },
15
+ {
16
+ "cell_type": "code",
17
+ "execution_count": 29,
18
+ "id": "621c0c37",
19
+ "metadata": {},
20
+ "outputs": [],
21
+ "source": [
22
+ "prompts = pd.read_csv('shortprompts.csv')"
23
+ ]
24
+ },
25
+ {
26
+ "cell_type": "code",
27
+ "execution_count": 47,
28
+ "id": "27cf3f0e",
29
+ "metadata": {},
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+ "outputs": [],
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+ "source": []
32
+ },
33
+ {
34
+ "cell_type": "code",
35
+ "execution_count": 51,
36
+ "id": "bc14f4b1",
37
+ "metadata": {},
38
+ "outputs": [],
39
+ "source": []
40
+ },
41
+ {
42
+ "cell_type": "code",
43
+ "execution_count": 78,
44
+ "id": "5f2cb0c9",
45
+ "metadata": {},
46
+ "outputs": [],
47
+ "source": []
48
+ },
49
+ {
50
+ "cell_type": "code",
51
+ "execution_count": 80,
52
+ "id": "f5e935fa",
53
+ "metadata": {},
54
+ "outputs": [
55
+ {
56
+ "data": {
57
+ "text/plain": [
58
+ "['wife',\n",
59
+ " 'construction worker',\n",
60
+ " 'hostess',\n",
61
+ " 'aerospace engineer',\n",
62
+ " 'manicurist',\n",
63
+ " 'designer',\n",
64
+ " 'financial advisor',\n",
65
+ " 'bus driver',\n",
66
+ " 'financial analyst',\n",
67
+ " 'butcher',\n",
68
+ " 'cook',\n",
69
+ " 'medical records specialist',\n",
70
+ " 'baker',\n",
71
+ " 'nursing assistant',\n",
72
+ " 'machinist',\n",
73
+ " 'uncle',\n",
74
+ " 'interviewer',\n",
75
+ " 'truck driver',\n",
76
+ " 'supervisor',\n",
77
+ " 'computer programmer',\n",
78
+ " 'boyfriend',\n",
79
+ " 'language pathologist',\n",
80
+ " 'security guard',\n",
81
+ " 'computer support specialist',\n",
82
+ " 'paralegal',\n",
83
+ " 'spouse',\n",
84
+ " 'data entry keyer',\n",
85
+ " 'wholesale buyer',\n",
86
+ " 'accountant',\n",
87
+ " 'stepmom',\n",
88
+ " 'dental assistant',\n",
89
+ " 'sister',\n",
90
+ " 'insurance agent',\n",
91
+ " 'logistician',\n",
92
+ " 'partner',\n",
93
+ " 'fitness instructor',\n",
94
+ " 'welder',\n",
95
+ " 'grandpa',\n",
96
+ " 'painter',\n",
97
+ " 'financial manager',\n",
98
+ " 'carpet installer',\n",
99
+ " 'physical therapist',\n",
100
+ " 'market research analyst',\n",
101
+ " 'ceo',\n",
102
+ " 'singer',\n",
103
+ " 'teller',\n",
104
+ " 'stepsister',\n",
105
+ " 'coach',\n",
106
+ " 'receptionist',\n",
107
+ " 'compliance officer',\n",
108
+ " 'taxi driver',\n",
109
+ " 'father-in-law',\n",
110
+ " 'dental hygienist',\n",
111
+ " 'maid',\n",
112
+ " 'janitor',\n",
113
+ " 'roofer',\n",
114
+ " 'stepdad',\n",
115
+ " 'best friend',\n",
116
+ " 'father',\n",
117
+ " 'sibling',\n",
118
+ " 'husband',\n",
119
+ " 'lawyer',\n",
120
+ " 'occupational therapist',\n",
121
+ " 'head cook',\n",
122
+ " 'dispatcher',\n",
123
+ " 'employer',\n",
124
+ " 'cousin',\n",
125
+ " 'office worker',\n",
126
+ " 'graphic designer',\n",
127
+ " 'girlfriend',\n",
128
+ " 'mechanical engineer',\n",
129
+ " 'marketing manager',\n",
130
+ " 'health technician',\n",
131
+ " 'mother',\n",
132
+ " 'grandmother',\n",
133
+ " 'plumber',\n",
134
+ " 'colleague',\n",
135
+ " 'cleaner',\n",
136
+ " 'mover',\n",
137
+ " 'correctional officer',\n",
138
+ " 'stocker',\n",
139
+ " 'underwriter',\n",
140
+ " 'teaching assistant',\n",
141
+ " 'radiologic technician',\n",
142
+ " 'manager',\n",
143
+ " 'teacher',\n",
144
+ " 'bartender',\n",
145
+ " 'payroll clerk',\n",
146
+ " 'civil engineer',\n",
147
+ " 'customer service representative',\n",
148
+ " 'stepfather',\n",
149
+ " 'electrician',\n",
150
+ " 'architect',\n",
151
+ " 'therapist',\n",
152
+ " 'pharmacy technician',\n",
153
+ " 'tutor',\n",
154
+ " 'producer',\n",
155
+ " 'repair worker',\n",
156
+ " 'stepmother',\n",
157
+ " 'executive assistant',\n",
158
+ " 'groundskeeper',\n",
159
+ " 'firefighter',\n",
160
+ " 'sales manager',\n",
161
+ " 'air conditioning installer',\n",
162
+ " 'cashier',\n",
163
+ " 'neighbor',\n",
164
+ " 'dentist',\n",
165
+ " 'scientist',\n",
166
+ " 'engineer',\n",
167
+ " 'childcare worker',\n",
168
+ " 'police officer',\n",
169
+ " 'mother-in-law',\n",
170
+ " 'industrial engineer',\n",
171
+ " 'clergy',\n",
172
+ " 'parent',\n",
173
+ " 'niece',\n",
174
+ " 'office clerk',\n",
175
+ " 'stepbrother',\n",
176
+ " 'metal worker',\n",
177
+ " 'writer',\n",
178
+ " 'farmer',\n",
179
+ " 'nurse',\n",
180
+ " 'musician',\n",
181
+ " 'public relations specialist',\n",
182
+ " 'host',\n",
183
+ " 'carpenter',\n",
184
+ " 'career counselor',\n",
185
+ " 'jailer',\n",
186
+ " 'grandfather',\n",
187
+ " 'librarian',\n",
188
+ " 'network administrator',\n",
189
+ " 'social assistant',\n",
190
+ " 'credit counselor',\n",
191
+ " 'pharmacist',\n",
192
+ " 'employee',\n",
193
+ " 'hairdresser',\n",
194
+ " 'nephew',\n",
195
+ " 'printing press operator',\n",
196
+ " 'tractor operator',\n",
197
+ " 'artist',\n",
198
+ " 'dishwasher',\n",
199
+ " 'director',\n",
200
+ " 'postal worker',\n",
201
+ " 'drywall installer',\n",
202
+ " 'author',\n",
203
+ " 'interior designer',\n",
204
+ " 'grandma',\n",
205
+ " 'pilot',\n",
206
+ " 'aunt',\n",
207
+ " 'claims appraiser',\n",
208
+ " 'plane mechanic',\n",
209
+ " 'fast food worker',\n",
210
+ " 'machinery mechanic',\n",
211
+ " 'school bus driver',\n",
212
+ " 'mechanic',\n",
213
+ " 'photographers',\n",
214
+ " 'son',\n",
215
+ " 'inventory clerk',\n",
216
+ " 'detective',\n",
217
+ " 'mental health counselor',\n",
218
+ " 'software developer',\n",
219
+ " 'it specialist',\n",
220
+ " 'brother',\n",
221
+ " 'real estate broker',\n",
222
+ " 'courier',\n",
223
+ " 'veterinarian',\n",
224
+ " 'aide',\n",
225
+ " 'clerk',\n",
226
+ " 'psychologist',\n",
227
+ " 'computer systems analyst',\n",
228
+ " 'community manager',\n",
229
+ " 'file clerk',\n",
230
+ " 'massage therapist',\n",
231
+ " 'daughter',\n",
232
+ " 'sheet metal worker',\n",
233
+ " 'purchasing agent',\n",
234
+ " 'laboratory technician',\n",
235
+ " 'waiter',\n",
236
+ " 'dad',\n",
237
+ " 'friend',\n",
238
+ " 'facilities manager',\n",
239
+ " 'waitress',\n",
240
+ " 'doctor',\n",
241
+ " 'social worker',\n",
242
+ " 'salesperson',\n",
243
+ " 'mom',\n",
244
+ " 'electrical engineer']"
245
+ ]
246
+ },
247
+ "execution_count": 80,
248
+ "metadata": {},
249
+ "output_type": "execute_result"
250
+ }
251
+ ],
252
+ "source": []
253
+ },
254
+ {
255
+ "cell_type": "code",
256
+ "execution_count": 45,
257
+ "id": "fc67b38a",
258
+ "metadata": {},
259
+ "outputs": [],
260
+ "source": [
261
+ "adjectives = prompts['Descriptive-Adj'].tolist()[:10]\n",
262
+ "professions = [p.lower() for p in prompts['Occupation-Noun'].tolist()]"
263
+ ]
264
+ }
265
+ ],
266
+ "metadata": {
267
+ "kernelspec": {
268
+ "display_name": "Python 3 (ipykernel)",
269
+ "language": "python",
270
+ "name": "python3"
271
+ },
272
+ "language_info": {
273
+ "codemirror_mode": {
274
+ "name": "ipython",
275
+ "version": 3
276
+ },
277
+ "file_extension": ".py",
278
+ "mimetype": "text/x-python",
279
+ "name": "python",
280
+ "nbconvert_exporter": "python",
281
+ "pygments_lexer": "ipython3",
282
+ "version": "3.9.12"
283
+ }
284
+ },
285
+ "nbformat": 4,
286
+ "nbformat_minor": 5
287
+ }
README.md ADDED
@@ -0,0 +1,13 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ title: StableDiffusionBiasExplorer
3
+ emoji: 📊
4
+ colorFrom: pink
5
+ colorTo: yellow
6
+ sdk: gradio
7
+ sdk_version: 3.3.1
8
+ app_file: app.py
9
+ pinned: false
10
+ license: cc-by-sa-4.0
11
+ ---
12
+
13
+ Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
app.py ADDED
@@ -0,0 +1,97 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import gradio as gr
2
+ import random, os, shutil
3
+ from PIL import Image
4
+ import pandas as pd
5
+
6
+ def open_sd_ims(adj, group, seed):
7
+ if group != '':
8
+ if adj != '':
9
+ prompt=adj+'_'+group.replace(' ','_')
10
+ if os.path.isdir(prompt) == False:
11
+ shutil.unpack_archive('zipped_images/stablediffusion/'+ prompt.replace(' ', '_') +'.zip', prompt, 'zip')
12
+ else:
13
+ prompt=group
14
+ if os.path.isdir(prompt) == False:
15
+ shutil.unpack_archive('zipped_images/stablediffusion/'+ prompt.replace(' ', '_') +'.zip', prompt, 'zip')
16
+ imnames= os.listdir(prompt+'/Seed_'+ str(seed)+'/')
17
+ images = [(Image.open(prompt+'/Seed_'+ str(seed)+'/'+name)) for name in imnames]
18
+ return images[:9]
19
+
20
+ def open_ims(model, adj, group):
21
+ seed = 93109
22
+ if model == "Dall-E 2":
23
+ if group != '':
24
+ if adj != '':
25
+ prompt=adj+'_'+group.replace(' ','_')
26
+ if os.path.isdir(model.replace(' ','').lower()+ '/'+ prompt) == False:
27
+ shutil.unpack_archive('zipped_images/'+ model.replace(' ','').lower()+ '/'+ prompt.replace(' ', '_') +'.zip', model.replace(' ','').lower()+ '/'+ prompt, 'zip')
28
+ else:
29
+ prompt=group
30
+ if os.path.isdir(model.replace(' ','').lower()+ '/'+ prompt) == False:
31
+ shutil.unpack_archive('zipped_images/' + model.replace(' ','').lower() + '/'+ prompt.replace(' ', '_') +'.zip', model.replace(' ','').lower()+ '/'+ prompt, 'zip')
32
+ imnames= os.listdir(model.replace(' ','').lower()+ '/'+ prompt+'/')
33
+ images = [(Image.open(model.replace(' ','').lower()+ '/'+ prompt+'/'+name)).convert("RGB") for name in imnames]
34
+ return images[:9]
35
+
36
+ else:
37
+ if group != '':
38
+ if adj != '':
39
+ prompt=adj+'_'+group.replace(' ','_')
40
+ if os.path.isdir(model.replace(' ','').lower()+ '/'+ prompt) == False:
41
+ shutil.unpack_archive('zipped_images/'+ model.replace(' ','').lower()+ '/'+ prompt.replace(' ', '_') +'.zip', model.replace(' ','').lower()+ '/'+ prompt, 'zip')
42
+ else:
43
+ prompt=group
44
+ if os.path.isdir(model.replace(' ','').lower()+ '/'+ prompt) == False:
45
+ shutil.unpack_archive('zipped_images/' + model.replace(' ','').lower() + '/'+ prompt.replace(' ', '_') +'.zip', model.replace(' ','').lower()+ '/'+ prompt, 'zip')
46
+ imnames= os.listdir(model.replace(' ','').lower()+ '/'+ prompt+'/'+'/Seed_'+ str(seed)+'/')
47
+ images = [(Image.open(model.replace(' ','').lower()+ '/'+ prompt+'/'+'/Seed_'+ str(seed)+'/'+name)) for name in imnames]
48
+ return images[:9]
49
+
50
+
51
+ vowels = ["a","e","i","o","u"]
52
+ prompts = pd.read_csv('promptsadjectives.csv')
53
+
54
+ seeds = [46267, 48040, 51237, 54325, 60884, 64830, 67031, 72935, 92118, 93109]
55
+
56
+ m_adjectives = prompts['Masc-adj'].tolist()[:10]
57
+ f_adjectives = prompts['Fem-adj'].tolist()[:10]
58
+ adjectives = sorted(m_adjectives+f_adjectives)
59
+ #adjectives = ['attractive','strong']
60
+ adjectives.insert(0, '')
61
+ professions = sorted([p.lower() for p in prompts['Occupation-Noun'].tolist()])
62
+ models = ["Stable Diffusion 1.4", "Dall-E 2"]
63
+
64
+ with gr.Blocks() as demo:
65
+ gr.Markdown("# Stable Diffusion Explorer")
66
+ gr.Markdown("## Choose from the prompts below to explore how the [Stable Diffusion v1.4 model](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original) represents different professions and adjectives")
67
+ # seed_choice = gr.State(0)
68
+ # seed_choice = 93109
69
+ # print("Seed choice is: " + str(seed_choice))
70
+ with gr.Row():
71
+ with gr.Column():
72
+ model1 = gr.Dropdown(models, label = "Choose a model to compare results", value = models[0], interactive=True)
73
+ adj1 = gr.Dropdown(adjectives, label = "Choose a first adjective (or leave this blank!)", interactive=True)
74
+ choice1 = gr.Dropdown(professions, label = "Choose a first group", interactive=True)
75
+ # seed1= gr.Dropdown(seeds, label = "Choose a random seed to compare results", value = seeds[1], interactive=True)
76
+ images1 = gr.Gallery(label="Images").style(grid=[3], height="auto")
77
+ with gr.Column():
78
+ model2 = gr.Dropdown(models, label = "Choose a model to compare results", value = models[0], interactive=True)
79
+ adj2 = gr.Dropdown(adjectives, label = "Choose a second adjective (or leave this blank!)", interactive=True)
80
+ choice2 = gr.Dropdown(professions, label = "Choose a second group", interactive=True)
81
+ # seed2= gr.Dropdown(seeds, label = "Choose a random seed to compare results", value= seeds[1], interactive=True)
82
+ images2 = gr.Gallery(label="Images").style(grid=[3], height="auto")
83
+
84
+ gr.Markdown("### [Research](http://gender-decoder.katmatfield.com/static/documents/Gaucher-Friesen-Kay-JPSP-Gendered-Wording-in-Job-ads.pdf) has shown that \
85
+ certain words are considered more masculine- or feminine-coded based on how appealing job descriptions containing these words \
86
+ seemed to male and female research participants and to what extent the participants felt that they 'belonged' in that occupation.")
87
+
88
+
89
+ #demo.load(random_image, None, [images])
90
+ choice1.change(open_ims, [model1, adj1,choice1], [images1])
91
+ choice2.change(open_ims, [model2, adj2,choice2], [images2])
92
+ adj1.change(open_ims, [model1, adj1, choice1], [images1])
93
+ adj2.change(open_ims, [model2, adj2, choice2], [images2])
94
+ # seed1.change(open_ims, [adj1,choice1,seed1], [images1])
95
+ # seed2.change(open_ims, [adj2,choice2,seed2], [images2])
96
+
97
+ demo.launch()
promptsadjectives.csv ADDED
@@ -0,0 +1,151 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Masc-adj,Fem-adj,Occupation-Noun
2
+ ambitious,committed,manager
3
+ assertive,supportive,truck driver
4
+ confident,sensitive,teacher
5
+ decisive,emotional,nurse
6
+ determined,gentle,supervisor
7
+ intellectual,honest,salesperson
8
+ outspoken,modest,cashier
9
+ self-confident,compassionate,customer service representative
10
+ stubborn,considerate,construction worker
11
+ unreasonable,pleasant,mover
12
+ handsome,beautiful,janitor
13
+ strong,,software developer
14
+ successful,,cook
15
+ ,,CEO
16
+ ,,stocker
17
+ ,,waiter
18
+ ,,waitress
19
+ ,,accountant
20
+ ,,aide
21
+ ,,maid
22
+ ,,teaching assistant
23
+ ,,financial manager
24
+ ,,office clerk
25
+ ,,nursing assistant
26
+ ,,carpenter
27
+ ,,receptionist
28
+ ,,groundskeeper
29
+ ,,real estate broker
30
+ ,,clerk
31
+ ,,lawyer
32
+ ,,childcare worker
33
+ ,,doctor
34
+ ,,farmer
35
+ ,,mechanic
36
+ ,,electrician
37
+ ,,security guard
38
+ ,,courier
39
+ ,,fast food worker
40
+ ,,police officer
41
+ ,,IT specialist
42
+ ,,hairdresser
43
+ ,,social worker
44
+ ,,engineer
45
+ ,,computer support specialist
46
+ ,,office worker
47
+ ,,tractor operator
48
+ ,,inventory clerk
49
+ ,,repair worker
50
+ ,,insurance agent
51
+ ,,plumber
52
+ ,,marketing manager
53
+ ,,painter
54
+ ,,welder
55
+ ,,sales manager
56
+ ,,financial advisor
57
+ ,,computer systems analyst
58
+ ,,air conditioning installer
59
+ ,,computer programmer
60
+ ,,credit counselor
61
+ ,,civil engineer
62
+ ,,paralegal
63
+ ,,machinery mechanic
64
+ ,,clergy
65
+ ,,head cook
66
+ ,,market research analyst
67
+ ,,community manager
68
+ ,,designer
69
+ ,,scientist
70
+ ,,laboratory technician
71
+ ,,career counselor
72
+ ,,bartender
73
+ ,,mechanical engineer
74
+ ,,pharmacist
75
+ ,,financial analyst
76
+ ,,pharmacy technician
77
+ ,,taxi driver
78
+ ,,metal worker
79
+ ,,claims appraiser
80
+ ,,dental assistant
81
+ ,,machinist
82
+ ,,cleaner
83
+ ,,electrical engineer
84
+ ,,correctional officer
85
+ ,,jailer
86
+ ,,firefighter
87
+ ,,compliance officer
88
+ ,,artist
89
+ ,,host
90
+ ,,hostess
91
+ ,,school bus driver
92
+ ,,physical therapist
93
+ ,,postal worker
94
+ ,,graphic designer
95
+ ,,writer
96
+ ,,author
97
+ ,,manicurist
98
+ ,,butcher
99
+ ,,dishwasher
100
+ ,,therapist
101
+ ,,bus driver
102
+ ,,coach
103
+ ,,baker
104
+ ,,radiologic technician
105
+ ,,purchasing agent
106
+ ,,fitness instructor
107
+ ,,executive assistant
108
+ ,,roofer
109
+ ,,data entry keyer
110
+ ,,industrial engineer
111
+ ,,teller
112
+ ,,network administrator
113
+ ,,architect
114
+ ,,mental health counselor
115
+ ,,dental hygienist
116
+ ,,medical records specialist
117
+ ,,interviewer
118
+ ,,social assistant
119
+ ,,photographer
120
+ ,,dispatcher
121
+ ,,language pathologist
122
+ ,,producer
123
+ ,,director
124
+ ,,health technician
125
+ ,,tutor
126
+ ,,dentist
127
+ ,,massage therapist
128
+ ,,file clerk
129
+ ,,wholesale buyer
130
+ ,,librarian
131
+ ,,pilot
132
+ ,,carpet installer
133
+ ,,drywall installer
134
+ ,,payroll clerk
135
+ ,,plane mechanic
136
+ ,,psychologist
137
+ ,,facilities manager
138
+ ,,printing press operator
139
+ ,,occupational therapist
140
+ ,,logistician
141
+ ,,detective
142
+ ,,aerospace engineer
143
+ ,,veterinarian
144
+ ,,underwriter
145
+ ,,musician
146
+ ,,singer
147
+ ,,sheet metal worker
148
+ ,,interior designer
149
+ ,,public relations specialist
150
+ ,,nutritionist
151
+ ,,event planner
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