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import pandas as pd
from sentence_transformers.util import cos_sim
from utils.models import SBert
def p0_originality(df: pd.DataFrame, model_name: str) -> pd.DataFrame:
assert 'prompt' in df.columns
assert 'response' in df.columns
model = SBert(model_name)
def get_cos_sim(prompt: str, response: str) -> float:
prompt_vec = model(prompt)
response_vec = model(response)
score = cos_sim(prompt_vec, response_vec).item()
return score
df['originality'] = df.apply(lambda x: 1 - get_cos_sim(x['prompt'], x['response']), axis=1)
return df
def p1_flexibility(df: pd.DataFrame, model_name: str) -> pd.DataFrame:
assert 'prompt' in df.columns
assert 'response' in df.columns
assert 'id' in df.columns
model = SBert(model_name)
def get_cos_sim(responses: list[str]) -> float:
responses_vec = [model(_) for _ in responses]
count = 0
score = 0
for i in range(len(responses_vec)):
for j in range(1, len(responses_vec)):
if i == j:
continue
score += cos_sim(responses_vec[i], responses_vec[j]).item()
count += 1
return score / count
df_out = df.groupby(by=['id', 'prompt']) \
.agg({'id': 'first', 'prompt': 'first', 'response': get_cos_sim}) \
.rename(columns={'response': 'flexibility'}) \
.reset_index(drop=True)
return df_out
if __name__ == '__main__':
_df_input = pd.read_csv('data/example_3.csv')
_df_0 = p0_originality(_df_input, 'paraphrase-multilingual-MiniLM-L12-v2')
_df_1 = p1_flexibility(_df_input, 'paraphrase-multilingual-MiniLM-L12-v2')
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