-
Notifications
You must be signed in to change notification settings - Fork 1
/
generate_cbg.py
197 lines (172 loc) · 7.17 KB
/
generate_cbg.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
import sys
import pandas as pd
from generation_util import *
import random
import os
from tqdm import tqdm
import argparse
def chatgpt_gen(occu, gend, output_folder):
if gend == "f":
csv_file = f"./biography_dataset/preprocessed_bios/df_f_{occu}_2_para.csv"
else:
csv_file = f"./biography_dataset/preprocessed_bios/df_m_{occu}_2_para.csv"
file_name = csv_file.split('/')[-1].split('.')[0] + '_chatgpt.csv'
if not os.path.exists(csv_file):
raise Exception(f"Occupation {occu} for ChatGPT has not been generated yet!")
if occu == "acting":
real_occupation = "actor"
else:
real_occupation = occu.rstrip("s")
df = pd.read_csv(csv_file)
if "info" not in list(df.columns) or "first_name" not in list(df.columns):
raise Exception("info and name must be in df's columns.")
df["chatgpt_gen"] = -1
for i, row in tqdm(df.iterrows(), ascii=True):
pronoun = "him" if row["gender"] == "m" else "her"
generated_response = generate_response_rec_chatgpt(
{
"occupation": real_occupation,
"name": "{} {}".format(row["first_name"], row["last_name"]),
"pronoun": pronoun,
"info": row["info"],
}
)
generated_response = generated_response.replace("\n", "<return>")
df["chatgpt_gen"][i] = generated_response
df.to_csv(output_folder + '/' + file_name)
return
def model_gen(occu, gend, model_type, output_folder):
if gend == "f":
csv_file = f"./biography_dataset/preprocessed_bios/df_f_{occu}_2_para.csv"
else:
csv_file = f"./biography_dataset/preprocessed_bios/df_m_{occu}_2_para.csv"
file_name = csv_file.split('/')[-1].split('.')[0] + '_{}.csv'.format(model_type)
if not os.path.exists(csv_file):
raise Exception(f"Occupation {occu} for Model {model_type} has not been generated yet!")
if occu == "acting":
real_occupation = "actor"
else:
real_occupation = occu.rstrip("s")
if model_type == "alpaca":
tokenizer = LlamaTokenizer.from_pretrained(
"chavinlo/alpaca-native", model_max_length=1024
)
model = LlamaForCausalLM.from_pretrained("chavinlo/alpaca-native")
model.config.pad_token_id = tokenizer.pad_token_id = 0 # unk
model.config.bos_token_id = 1
model.config.eos_token_id = 2
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.half().to(device)
elif model_type == "vicuna":
tokenizer = LlamaTokenizer.from_pretrained("/local/elaine1wan/vicuna")
model = LlamaForCausalLM.from_pretrained("/local/elaine1wan/vicuna")
model.config.pad_token_id = tokenizer.pad_token_id = 0 # unk
model.config.bos_token_id = 1
model.config.eos_token_id = 2
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.half().to(device)
elif model_type == "stablelm":
tokenizer = AutoTokenizer.from_pretrained("StabilityAI/stablelm-tuned-alpha-7b")
model = AutoModelForCausalLM.from_pretrained(
"StabilityAI/stablelm-tuned-alpha-7b"
)
model.config.pad_token_id = tokenizer.pad_token_id = 0 # unk
model.config.bos_token_id = 1
model.config.eos_token_id = 2
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.half().to(device)
# model.to(device)
elif model_type == "falcoln":
model = "tiiuae/falcon-7b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, trust_remote_code=True)
model.config.pad_token_id = tokenizer.pad_token_id = 0 # unk
model.config.bos_token_id = 1
model.config.eos_token_id = 2
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.half().to(device)
else:
raise NotImplementedError
df = pd.read_csv(csv_file)
if "info" not in list(df.columns) or "first_name" not in list(df.columns):
raise Exception("info and name must be in df's columns.")
df["{}_gen".format(model_type)] = -1
print("Total generations: {}".format(len(df)))
write_amount = 0
for i, row in tqdm(df.iterrows(), ascii=True):
# print(i)
# if i < 3470: continue
pronoun = "him" if row["gender"] == "m" else "her"
if model_type == "alpaca":
generated_response = generate_response_rec_alpaca(
{
"occupation": real_occupation,
"name": "{} {}".format(row["first_name"], row["last_name"]),
"pronoun": pronoun,
"info": row["info"],
},
model,
tokenizer,
device,
)
elif model_type == "vicuna":
generated_response = generate_response_rec_vicuna(
{
"occupation": real_occupation,
"name": "{} {}".format(row["first_name"], row["last_name"]),
"pronoun": pronoun,
"info": row["info"],
},
model,
tokenizer,
device,
)
elif model_type == "stablelm":
generated_response = generate_response_rec_stablelm(
{
"occupation": real_occupation,
"name": "{} {}".format(row["first_name"], row["last_name"]),
"pronoun": pronoun,
"info": row["info"],
},
model,
tokenizer,
device,
)
elif model_type == "falcoln":
generated_response = generate_response_rec_falcon(
{
"occupation": real_occupation,
"name": "{} {}".format(row["first_name"], row["last_name"]),
"pronoun": pronoun,
"info": row["info"],
},
model,
tokenizer,
device,
)
generated_response = generated_response.replace("\n", "<return>")
df["{}_gen".format(model_type)][i] = generated_response
write_amount += 1
print("Number of generated samples: {}".format(write_amount))
df.to_csv(output_folder + '/' + file_name)
return
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--model", default="chatgpt", help="Model type.")
parser.add_argument(
"--n",
default=-1,
help="Number of samples for each occupation for each gender.",
)
parser.add_argument('-of', '--output_folder', default='./generated_letters/chatgpt/cbg')
args = parser.parse_args()
print(args)
for occupation in ['acting', 'chefs', 'artists', 'dancers', 'comedians', 'models', 'musicians', 'podcasters', 'writers', 'sports']:
for gend in ["m", "f"]:
if args.model == "chatgpt":
chatgpt_gen(occupation, gend, args.output_folder)
else:
model_gen(occupation, gend, args.model, args.output_folder)
if __name__ == "__main__":
main()