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from scipy.special import softmax |
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from model import Model |
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import numpy as np |
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class Classifier: |
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def __init__(self): |
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self.model = Model.load_model() |
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self.tokenizer = Model.load_tokenizer() |
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def get_sentiment_label_and_score(self, text: str): |
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result = {} |
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labels = ["Negative", "Neutral", "Positive"] |
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encoded_input = self.tokenizer(text, return_tensors='pt') |
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output = self.model(**encoded_input) |
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scores = output[0][0].detach().numpy() |
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scores = softmax(scores) |
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ranking = np.argsort(scores) |
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ranking = ranking[::-1] |
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result["label"] = str(labels[ranking[0]]) |
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result["score"] = np.round(float(scores[ranking[0]]), 4) |
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return result |
