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| import os |
| import utils |
| import torch |
| import json |
| import argparse |
| import pandas as pd |
| from tqdm import tqdm |
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| os.environ["CUDA_VISIBLE_DEVICES"] = "6,7" |
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| delimiter = '####' |
| system_message_for = f'''The Moral Foundations Theory introduces six foundations of morality:\ |
| 1)Care, 2)Equality, 3) Proportionality, 4)Loyalty, 5)Authority, 6)Purity. \ |
| You are given a sentence delimited with {delimiter} characters. |
| Your task is to generate a one-sentence-long persuasive argument for \ |
| why the given sentence is sexist based on moral foundation theory. |
| Important: Start the argument with ```This sentence is sexist because it violates the moral foundations of```" |
| |
| ''' |
| system_message_against = f'''The Moral Foundations Theory introduces six foundations of morality:\ |
| 1)Care, 2)Equality, 3) Proportionality, 4)Loyalty, 5)Authority, 6)Purity. \ |
| You are given a sentence delimited with {delimiter} characters. |
| Your task is to generate a one-sentence-long persuasive argument for \ |
| why the given sentence is not sexist based on moral foundation theory. |
| Important: Start the argument with ```This sentence is not sexist because it aligns with moral values of``` |
| |
| ''' |
|
|
| def user_message(text): |
| return f'''{delimiter}{text}{delimiter} |
| ''' |
|
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|
|
| if __name__ == "__main__": |
| parser = argparse.ArgumentParser(description="llama2") |
| parser.add_argument('--cuda', type=str, default="-1") |
| parser.add_argument('--idx', type=int, default=-1) |
| args = parser.parse_args() |
| print(args) |
|
|
| if args.idx == "-1": |
| start_index = 0 |
| end_index = utils.MAX_ID |
| else: |
| start_index = args.idx |
| end_index = utils.MAX_ID |
| |
| if args.cuda != "-1": |
| os.environ["CUDA_VISIBLE_DEVICES"] = str(args.cuda) |
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| folder_path = './generations/llama2/' |
| if not os.path.exists('./generations'): |
| os.makedirs('./generations') |
| if not os.path.exists('./generations/llama2'): |
| os.makedirs('./generations/llama2') |
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| data = utils.read_implicit_edos() |
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| model = utils.HFModel( |
| model_path='meta-llama/Llama-2-7b-chat-hf') |
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| for i in range(len(data)): |
| if os.path.exists(folder_path + str(i) + '.json'): |
| continue |
| if i < start_index or i >= end_index: |
| continue |
| t = data['text'][i] |
| response_for = model.generate(system_message_for + user_message(t), temperature=0.7, max_new_tokens=200, pad_token=True) |
| response_against = model.generate(system_message_against + user_message(t), temperature=0.7, max_new_tokens=200, pad_token=True) |
| |
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| |
| response_for = response_for.split(delimiter)[-1].strip() |
| response_against = response_against.split(delimiter)[-1].strip() |
| print(i) |
| for _ in range(5): |
| if response_for.startswith('This sentence is sexist'): |
| break |
| print("re-generating...") |
| response_for = model.generate(system_message_for + user_message(t), temperature=0.7, max_new_tokens=200, pad_token=True) |
| response_for = response_for.split(delimiter)[-1].strip() |
| for _ in range(5): |
| if response_against.startswith('This sentence is not sexist'): |
| break |
| print("re-generating...") |
| response_against = model.generate(system_message_against + user_message(t), temperature=0.7, max_new_tokens=200, pad_token=True) |
| response_against = response_against.split(delimiter)[-1].strip() |
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| with open(folder_path + str(i) + '.json', 'w') as f: |
| json.dump({'id': str(data['id'][i]), 'for': response_for, 'against': response_against}, f) |
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