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from typing import List |
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import torch |
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from transformers import BertTokenizer |
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from foodybert import FoodyBertForSequenceClassification |
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class PreTrainedPipeline(): |
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def __init__(self, path=""): |
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""" |
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Initialize model |
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""" |
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self.bert_tokenizer = BertTokenizer.from_pretrained("bert-base-uncased") |
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self.model = FoodyBertForSequenceClassification.from_pretrained(".") |
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def __call__(self, inputs: str) -> str: |
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""" |
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Args: |
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inputs (:obj:`str`): |
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a string to get the features of. |
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Return: |
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A :obj:`list` of floats: The features computed by the model. |
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""" |
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input_ids = self.bert_tokenizer.encode(inputs, add_special_tokens=True) |
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X = torch.tensor([input_ids]) |
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with torch.no_grad(): |
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predicted_class_id = self.model(X).logits.argmax().item() |
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labels = ['positive','neutral','negative'] |
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reps = labels[predicted_class_id] |
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return reps |
