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import pandas as pd
from sentence_transformers.util import cos_sim
from utils.models import SBert
def get_cos_sim(model, prompt: str, response: str) -> float:
prompt_vec = model(prompt)
response_vec = model(response)
score = cos_sim(prompt_vec, response_vec).item()
return score
def batch_cos_sim(df: pd.DataFrame, model_name) -> pd.DataFrame:
# df.columns = ['prompt', 'response']
assert 'prompt' in df.columns
assert 'response' in df.columns
model = SBert(model_name)
df['originality'] = df.apply(lambda x: 1 - get_cos_sim(model, x['prompt'], x['response']), axis=1)
return df
if __name__ == '__main__':
_df = pd.read_csv('data/example_1.csv')
_df_o = batch_cos_sim(_df, 'paraphrase-multilingual-MiniLM-L12-v2')
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