max_txt = max(len(i[0]), max_txt)
if len(i[0]) != len(i[1]):
unsame += 1
else:
same += 1
ss = normalizeString(i[0])
sl = list(ss)
for sli in sl:
if sli not in all_expect_alphabets:
all_expect_alphabets.append(sli)
all_expect_alphabets.sort()
tokenize_alphabets = dg_alphabets.Diagnosis_alphabets()
for a in all_expect_alphabets:
tokenize_alphabets.addalphabet(a)
config = {
'hidden_size': 64,
'max_position_embeddings': 1350,
'eps': 1e-7,
'input_size': tokenize_alphabets.n_alphabets,
'vocab_size': tokenize_alphabets.n_alphabets,
'hidden_dropout_prob': 0.1,
'num_attention_heads': 8,
'attention_probs_dropout_prob': 0.1,
'intermediate_size': 256,
'num_hidden_layers': 16,
}
'[',
']',
'{',
'}',
'|',
'#0#',
'#1#',
'#2#',
'#3#',
'#4#',
'#5#',
'#6#',
'#7#',
]
tokenize_alphabets = dg_alphabets.Diagnosis_alphabets()
for a in all_expect_alphabets:
tokenize_alphabets.addalphabet(a)
config = {
'hidden_size': 64,
'max_position_embeddings': 1350,
'eps': 1e-12,
'input_size': tokenize_alphabets.n_alphabets,
'vocab_size': tokenize_alphabets.n_alphabets,
'hidden_dropout_prob': 0.1,
'num_attention_heads': 16,
'attention_probs_dropout_prob': 0.1,
'intermediate_size': 256,
'num_hidden_layers': 16,
}
