pipeline.py

from typing import List

import os

import fasttext.util

class PreTrainedPipeline():

    def __init__(self, path=""):
        """
        Initialize model
        """

        self.model = fasttext.load_model(os.path.join(path, 'debate2vec.bin'))

    def __call__(self, inputs: str) -> List[List[Dict[str, float]]]:
        """
        Args:
            inputs (:obj:`str`):
                a string containing some text
        Return:
            A :obj:`list`:. The object returned should be a list of one list like [[{"label": 0.9939950108528137}]] containing :
                - "label": A string representing what the label/class is. There can be multiple labels.
                - "score": A score between 0 and 1 describing how confident the model is for this label/class.
        """
        preds = self.model.get_nearest_neighbors("dog", k=10)
        result = []
        for distance, word in preds:
            result.append({"label": word, "score": distance})
        return [result]