def __init__(self, dp, charModel, wordModel, caseModel, inputSize,
hiddenSize, layerNum, numTags):
super(LSTM_CRF, self).__init__()
self.dp = dp
self.charModel = TimeDistributed(charModel, self.dp.char2Idx)
self.wordModel = wordModel
self.caseModel = caseModel
self.lstm = nn.LSTM(inputSize,
hiddenSize,
layerNum,
bias=0.5,
batch_first=True,
bidirectional=True)
self.fc = nn.Linear(2 * hiddenSize, numTags),
def __init__(self, dp, charModel, wordModel, caseModel, featureModel,
inputSize, hiddenSize, layerNum, dropout):
super(AdaPULSTMCNN2, self).__init__()
self.dp = dp
self.charModel = TimeDistributed(charModel, self.dp.char2Idx)
self.wordModel = wordModel
self.caseModel = caseModel
self.featureModel = featureModel
self.lstm = nn.LSTM(inputSize,
hiddenSize,
num_layers=layerNum,
batch_first=True,
bidirectional=True)
self.fc = nn.Sequential(nn.Linear(2 * hiddenSize, 200), nn.ReLU(),
nn.Linear(200, 200), nn.ReLU(),
nn.Linear(200, 200), nn.ReLU(),
nn.Linear(200, 2), nn.Softmax(dim=2))
def __init__(self, dp, charModel, wordModel, caseModel, inputSize,
hiddenSize, layerNum):
super(SupervisedModel, self).__init__()
self.dp = dp
self.charModel = TimeDistributed(charModel, self.dp.char2Idx)
self.wordModel = wordModel
self.caseModel = caseModel
self.lstm = nn.LSTM(inputSize,
hiddenSize,
layerNum,
bias=0.5,
batch_first=True,
bidirectional=True)
self.fc = nn.Sequential(nn.Linear(2 * hiddenSize, 200), nn.ReLU(),
nn.Linear(200, 200), nn.ReLU(),
nn.Linear(200, 200), nn.ReLU(),
nn.Linear(200, 2))
self.loss = nn.CrossEntropyLoss()