""" 1. jd_vocab_tokens的中文: 2. 中文标点 3. 全中文(单字) unicode 4. 全中文() 词典大小:46145。其中 中文汉字数:{'total': 25359, '中文单字': 5089, '中文多字': 20270}, 中文标点数: 266 """ from collections import Counter from transformers import AutoTokenizer from data_sample.oov_base import jd_vocab_tokens from utils.text_util import is_chinese, has_chinese from zhon.hanzi import punctuation as zh_punc tokenizer = AutoTokenizer.from_pretrained("tokenizer", trust_remote_code=True) # tokenizer = Tokenizer.from_file("../gpt_neox_chinese/20B_tokenizer_chinese.json") vocab = tokenizer.get_vocab() def zh_iterator(): for idx in range(ord(u'\u4e00'), ord(u'\u9fa5')): yield (chr(idx)) def test_coding_length(vocab, filter=None): all_length = [] for word in vocab: if len(word) > 1: continue if filter is not None and filter(word): continue tokens = tokenizer.encode(word) all_length.append(len(tokens)) # if len(tokens.ids) > 1: if len(tokens.ids) == 1: print(word, tokens.ids) print("编码长度统计:", Counter(all_length)) print("平均编码长度:", sum(all_length)/len(all_length)) def has_zh_char(text): return any(ch in zh_punc for ch in text) def iter_vocab(): f_out = open("vocab.zh.txt", "w", encoding="utf-8") zh_token_count = {"total": 0, "中文单字": 0, "中文多字": 0} zh_symbol_count = 0 for idx in range(len(vocab)): decode_str = tokenizer.decode([idx]) if has_chinese(decode_str): zh_token_count["total"] += 1 if len(decode_str.strip()) > 1: zh_token_count["中文多字"] += 1 else: zh_token_count["中文单字"] += 1 f_out.write("%d\t%s\t中文汉字\n" % (idx, decode_str)) elif has_zh_char(decode_str): zh_symbol_count += 1 f_out.write("%d\t%s\t中文标点\n" % (idx, decode_str)) print("词典大小:%d。其中 中文汉字数:%s, 中文标点数: %d" % (len(vocab), str(zh_token_count), zh_symbol_count)) if __name__ == "__main__": # test_coding_length(jd_vocab_tokens, filter=lambda k: not is_chinese(k)) # test_coding_length(zh_punc) # test_coding_length(zh_iterator()) iter_vocab()