def __init__(self, model: SentenceTransformer, sentence_embedding_dimension: int, num_labels: int, vocab, document_coef: float=0.4, concatenation_sent_rep: bool=True, concatenation_sent_difference: bool=True, concatenation_sent_multiplication: bool=False):
    super(TripleSoftmaxLoss, self).__init__()
    self.model = model
    self.num_labels = num_labels
    self.hidden = 1000
    self.concatenation_sent_rep = concatenation_sent_rep
    self.concatenation_sent_difference = concatenation_sent_difference
    self.concatenation_sent_multiplication = concatenation_sent_multiplication
    self.document_coef = document_coef
    num_vectors_concatenated = 0
    if concatenation_sent_rep:
        num_vectors_concatenated += 2
    if concatenation_sent_difference:
        num_vectors_concatenated += 2
    logging.info('Softmax loss: #Vectors concatenated: {}'.format(num_vectors_concatenated))
    self.relu = nn.ReLU()
    self.document2hidden = nn.Linear(291868, self.hidden)
    self.hidden2output = nn.Linear(self.hidden, 768)
    self.classifier = nn.Linear((num_vectors_concatenated * sentence_embedding_dimension), num_labels)
def __init__(self, model: SentenceTransformer, sentence_embedding_dimension: int, num_labels: int, vocab, document_coef: float=0.4, concatenation_sent_rep: bool=True, concatenation_sent_difference: bool=True, concatenation_sent_multiplication: bool=False):
    super(TripleSoftmaxLoss, self).__init__()
    self.model = model
    self.num_labels = num_labels
    self.hidden = 1000
    self.concatenation_sent_rep = concatenation_sent_rep
    self.concatenation_sent_difference = concatenation_sent_difference
    self.concatenation_sent_multiplication = concatenation_sent_multiplication
    self.document_coef = document_coef
    num_vectors_concatenated = 0
    if concatenation_sent_rep:
        num_vectors_concatenated += 2
    if concatenation_sent_difference:
        num_vectors_concatenated += 2
    logging.info('Softmax loss: #Vectors concatenated: {}'.format(num_vectors_concatenated))
    self.relu = nn.ReLU()
    self.document2hidden = nn.Linear(291868, self.hidden)
    self.hidden2output = nn.Linear(self.hidden, 768)
    self.classifier = nn.Linear((num_vectors_concatenated * sentence_embedding_dimension), num_labels)