def __init__(self,config_path):
super().__init__(config_path)
#QLEN = 10
KMAX = 2
NFILTERS = 32
MINGRAM = 1
MAXGRAM = 3
self.simmat = modeling_util.SimmatModule()
self.ngrams = torch.nn.ModuleList()
self.rbf_bank = None
self.CHANNELS = 1
for ng in range(MINGRAM, MAXGRAM+1):
ng = modeling_util.PACRRConvMax2dModule(ng, NFILTERS, k=KMAX, channels=self.CHANNELS)
self.ngrams.append(ng)
qvalue_size = len(self.ngrams) * KMAX
#self.linear1 = torch.nn.Linear(self.BERT_SIZE + QLEN * qvalue_size, 32)
self.linear1 = torch.nn.Linear(self.config['text1_maxlen'] * qvalue_size, 32)
self.linear2 = torch.nn.Linear(32, 32)
self.linear3 = torch.nn.Linear(32, 1)
def __init__(self, args):
super().__init__()
# QLEN = 20
self.args = args
QLEN = self.args.maxlen
KMAX = 1 # Original was 2, which causes unknown bug
NFILTERS = 32
MINGRAM = 1
MAXGRAM = 3
self.simmat = modeling_util.SimmatModule()
self.ngrams = torch.nn.ModuleList()
self.rbf_bank = None
for ng in range(MINGRAM, MAXGRAM + 1):
ng = modeling_util.PACRRConvMax2dModule(ng,
NFILTERS,
k=KMAX,
channels=self.CHANNELS)
self.ngrams.append(ng)
qvalue_size = len(self.ngrams) * KMAX
self.linear1 = torch.nn.Linear(self.BERT_SIZE + QLEN * qvalue_size, 32)
self.linear2 = torch.nn.Linear(32, 32)
self.linear3 = torch.nn.Linear(32, 1)