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from typing import Sequence | |
import numpy as np | |
class Vectorizer: | |
def __init__(self, model) -> None: | |
"""Initialize the vectorizer with a pre-trained embedding model. | |
Args: | |
model: The pre-trained embedding model to use for transforming | |
prompts. | |
""" | |
self.model = model | |
def transform(self, prompts: Sequence[str]) -> np.ndarray: | |
"""Transform texts into numerical vectors using the specified | |
model. | |
Args: | |
prompts: The sequence of raw corpus prompts. Returns: | |
Vectorized | |
prompts as a numpy array. | |
""" | |
return self.model.encode(prompts) | |