code/inference.py [31:40]:
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    with torch.no_grad():
        model_output = model(**encoded_input)

    sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
    return sentence_embeddings

def model_fn(model_dir):
    logger.info('model_fn')
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    logger.info(model_dir)
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inference.py [29:38]:
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    with torch.no_grad():
        model_output = model(**encoded_input)

    sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
    return sentence_embeddings

def model_fn(model_dir):
    logger.info('model_fn')
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    logger.info(model_dir)
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