82 lines
1.9 KiB
Python
82 lines
1.9 KiB
Python
import en_vectors_web_lg, random, re, json
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import numpy as np
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def tokenize(ques_list, use_glove):
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token_to_ix = {
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'PAD': 0,
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'UNK': 1,
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}
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spacy_tool = None
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pretrained_emb = []
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if use_glove:
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spacy_tool = en_vectors_web_lg.load()
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pretrained_emb.append(spacy_tool('PAD').vector)
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pretrained_emb.append(spacy_tool('UNK').vector)
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for ques in ques_list:
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words = re.sub(
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r"([.,'!?\"()*#:;])",
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'',
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ques.lower()
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).replace('-', ' ').replace('/', ' ').split()
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for word in words:
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if word not in token_to_ix:
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token_to_ix[word] = len(token_to_ix)
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if use_glove:
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pretrained_emb.append(spacy_tool(word).vector)
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pretrained_emb = np.array(pretrained_emb)
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return token_to_ix, pretrained_emb
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def proc_ques(ques, token_to_ix, max_token):
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ques_ix = np.zeros(max_token, np.int64)
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words = re.sub(
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r"([.,'!?\"()*#:;])",
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'',
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ques.lower()
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).replace('-', ' ').replace('/', ' ').split()
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q_len = 0
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for ix, word in enumerate(words):
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if word in token_to_ix:
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ques_ix[ix] = token_to_ix[word]
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q_len += 1
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else:
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ques_ix[ix] = token_to_ix['UNK']
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if ix + 1 == max_token:
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break
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return ques_ix, q_len, len(words)
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def ans_stat(ans_list):
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ans_to_ix, ix_to_ans = {}, {}
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for i, ans in enumerate(ans_list):
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ans_to_ix[ans] = i
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ix_to_ans[i] = ans
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return ans_to_ix, ix_to_ans
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def shuffle_list(ans_list):
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random.shuffle(ans_list)
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def qlen_to_key(q_len):
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if 1<= q_len <=3:
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return '1-3'
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if 4<= q_len <=8:
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return '4-8'
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if 9<= q_len:
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return '9-15'
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def ans_to_key(ans_idx):
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if 0 <= ans_idx <= 99 :
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return '0-99'
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if 100 <= ans_idx <= 299 :
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return '100-299'
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if 300 <= ans_idx <= 999 :
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return '300-999'
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