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- Data/BangDream/config.json +184 -0
- Data/BangDream/models/G_060000.pth +3 -0
- app.py +279 -0
- attentions.py +464 -0
- bert/Erlangshen-DeBERTa-v2-710M-Chinese/config.json +35 -0
- bert/Erlangshen-DeBERTa-v2-710M-Chinese/special_tokens_map.json +1 -0
- bert/Erlangshen-DeBERTa-v2-710M-Chinese/tokenizer_config.json +15 -0
- bert/Erlangshen-DeBERTa-v2-710M-Chinese/vocab.txt +12800 -0
- bert/Erlangshen-MegatronBert-1.3B-Chinese/config.json +1 -0
- bert/Erlangshen-MegatronBert-1.3B-Chinese/vocab.txt +0 -0
- bert/Erlangshen-MegatronBert-3.9B-Chinese/config.json +21 -0
- bert/Erlangshen-MegatronBert-3.9B-Chinese/special_tokens_map.json +7 -0
- bert/Erlangshen-MegatronBert-3.9B-Chinese/tokenizer_config.json +16 -0
- bert/Erlangshen-MegatronBert-3.9B-Chinese/vocab.txt +0 -0
- bert/bert-base-japanese-v3/.gitattributes +34 -0
- bert/bert-base-japanese-v3/README.md +53 -0
- bert/bert-base-japanese-v3/config.json +19 -0
- bert/bert-base-japanese-v3/tokenizer_config.json +10 -0
- bert/bert-base-japanese-v3/vocab.txt +0 -0
- bert/bert-large-japanese-v2/.gitattributes +34 -0
- bert/bert-large-japanese-v2/README.md +53 -0
- bert/bert-large-japanese-v2/config.json +19 -0
- bert/bert-large-japanese-v2/tokenizer_config.json +10 -0
- bert/bert-large-japanese-v2/vocab.txt +0 -0
- bert/bert_models.json +14 -0
- bert/chinese-roberta-wwm-ext-large/.gitattributes +9 -0
- bert/chinese-roberta-wwm-ext-large/README.md +57 -0
- bert/chinese-roberta-wwm-ext-large/added_tokens.json +1 -0
- bert/chinese-roberta-wwm-ext-large/config.json +28 -0
- bert/chinese-roberta-wwm-ext-large/special_tokens_map.json +1 -0
- bert/chinese-roberta-wwm-ext-large/tokenizer.json +0 -0
- bert/chinese-roberta-wwm-ext-large/tokenizer_config.json +1 -0
- bert/chinese-roberta-wwm-ext-large/vocab.txt +0 -0
- bert/deberta-v2-large-japanese-char-wwm/.gitattributes +34 -0
- bert/deberta-v2-large-japanese-char-wwm/README.md +89 -0
- bert/deberta-v2-large-japanese-char-wwm/config.json +37 -0
- bert/deberta-v2-large-japanese-char-wwm/pytorch_model.bin +3 -0
- bert/deberta-v2-large-japanese-char-wwm/special_tokens_map.json +7 -0
- bert/deberta-v2-large-japanese-char-wwm/tokenizer_config.json +19 -0
- bert/deberta-v2-large-japanese-char-wwm/vocab.txt +0 -0
- bert/deberta-v2-large-japanese/.gitattributes +34 -0
- bert/deberta-v2-large-japanese/README.md +111 -0
- bert/deberta-v2-large-japanese/config.json +38 -0
- bert/deberta-v2-large-japanese/special_tokens_map.json +9 -0
- bert/deberta-v2-large-japanese/tokenizer.json +0 -0
- bert/deberta-v2-large-japanese/tokenizer_config.json +15 -0
- bert/deberta-v3-large/.gitattributes +27 -0
- bert/deberta-v3-large/README.md +93 -0
- bert/deberta-v3-large/config.json +22 -0
- bert/deberta-v3-large/generator_config.json +22 -0
Data/BangDream/config.json
ADDED
@@ -0,0 +1,184 @@
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{
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"train": {
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"log_interval": 200,
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"eval_interval": 20000,
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"seed": 42,
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"epochs": 1000,
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"learning_rate": 0.0001,
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"betas": [
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0.8,
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0.99
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],
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"eps": 1e-09,
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"batch_size": 8,
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"bf16_run": false,
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"fp16_run": false,
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"lr_decay": 0.99996,
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"segment_size": 16384,
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"init_lr_ratio": 1,
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"warmup_epochs": 0,
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"c_mel": 45,
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"c_kl": 1.0,
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"c_commit": 100,
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"skip_optimizer": true,
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"freeze_ZH_bert": false,
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"freeze_JP_bert": false,
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"freeze_EN_bert": false,
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"freeze_emo": false
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},
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"data": {
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"training_files": "Data/BangDream/filelists/train.list",
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"validation_files": "Data/BangDream/filelists/val.list",
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"max_wav_value": 32768.0,
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"sampling_rate": 44100,
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"filter_length": 2048,
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"hop_length": 512,
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"win_length": 2048,
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"n_mel_channels": 128,
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"mel_fmin": 0.0,
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"mel_fmax": null,
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"add_blank": true,
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"n_speakers": 75,
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"cleaned_text": true,
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"spk2id": {
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"紗夜": 0,
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"有咲": 1,
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"たえ": 2,
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"りみ": 3,
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"香澄": 4,
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"ロック": 5,
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"パレオ": 6,
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"レイヤ": 7,
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"千聖": 8,
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"イヴ": 9,
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"蘭": 10,
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"巴": 11,
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"ひまり": 12,
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"つぐみ": 13,
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"華戀": 14,
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"晶": 15,
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"光": 16,
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"未知留": 17,
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"香子": 18,
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"雙葉": 19,
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"真晝": 20,
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"艾露": 21,
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"珠緒": 22,
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"艾露露": 23,
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"純那": 24,
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"克洛迪娜": 25,
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"真矢": 26,
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"奈奈": 27,
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"壘": 28,
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"文": 29,
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"一愛": 30,
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"菈樂菲": 31,
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"司": 32,
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"美空": 33,
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"靜羽": 34,
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"悠悠子": 35,
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"八千代": 36,
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"栞": 37,
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"美帆": 38,
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"安德露": 39,
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"瑪莉亞貝菈": 40,
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"克拉迪亞": 41,
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"桃樂西": 42,
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"瑪麗安": 43,
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"花音": 44,
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"はぐみ": 45,
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"こころ": 46,
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"美咲": 47,
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"沙綾": 48,
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"つくし": 49,
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"瑠唯": 50,
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"透子": 51,
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"七深": 52,
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"ましろ": 53,
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"友希那": 54,
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"リサ": 55,
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"あこ": 56,
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"チュチュ": 57,
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"薫": 58,
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"麻弥": 59,
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"彩": 60,
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"日菜": 61,
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"愛音": 62,
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"楽奈": 63,
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"そよ": 64,
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"立希": 65,
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"燐子": 66,
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"モカ": 67,
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"燈": 68,
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"ますき": 69,
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"祥子": 70,
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"睦": 71,
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"海鈴": 72,
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"にゃむ": 73,
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"初華": 74
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}
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},
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"model": {
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"use_spk_conditioned_encoder": true,
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"use_noise_scaled_mas": true,
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"use_mel_posterior_encoder": false,
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"use_duration_discriminator": false,
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"use_wavlm_discriminator": true,
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"inter_channels": 192,
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"hidden_channels": 192,
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"filter_channels": 768,
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"n_heads": 2,
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"n_layers": 6,
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"kernel_size": 3,
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"p_dropout": 0.1,
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"resblock": "1",
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"resblock_kernel_sizes": [
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3,
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7,
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11
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],
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"resblock_dilation_sizes": [
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[
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],
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[
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],
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[
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]
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],
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"upsample_rates": [
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8,
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8,
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2,
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2,
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],
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"upsample_initial_channel": 512,
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"upsample_kernel_sizes": [
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16,
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16,
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],
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"n_layers_q": 3,
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"use_spectral_norm": false,
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"gin_channels": 512,
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"slm": {
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"model": "./slm/wavlm-base-plus",
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"sr": 16000,
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"hidden": 768,
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"nlayers": 13,
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"initial_channel": 64
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}
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},
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"version": "2.4"
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}
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Data/BangDream/models/G_060000.pth
ADDED
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:4fd175e07cf8e52eeae57b4130030a580c0b31edb314b1f3e8e60f1c6439db71
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size 914308886
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app.py
ADDED
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import argparse
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import os
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from pathlib import Path
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import logging
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import re_matching
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logging.getLogger("numba").setLevel(logging.WARNING)
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logging.getLogger("markdown_it").setLevel(logging.WARNING)
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logging.getLogger("urllib3").setLevel(logging.WARNING)
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logging.getLogger("matplotlib").setLevel(logging.WARNING)
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logging.basicConfig(
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level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s"
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)
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17 |
+
logger = logging.getLogger(__name__)
|
18 |
+
|
19 |
+
import librosa
|
20 |
+
import numpy as np
|
21 |
+
import torch
|
22 |
+
import torch.nn as nn
|
23 |
+
from torch.utils.data import Dataset
|
24 |
+
from torch.utils.data import DataLoader, Dataset
|
25 |
+
from tqdm import tqdm
|
26 |
+
from clap_wrapper import get_clap_audio_feature, get_clap_text_feature
|
27 |
+
|
28 |
+
|
29 |
+
import gradio as gr
|
30 |
+
|
31 |
+
import utils
|
32 |
+
from config import config
|
33 |
+
|
34 |
+
import torch
|
35 |
+
import commons
|
36 |
+
from text import cleaned_text_to_sequence, get_bert
|
37 |
+
from text.cleaner import clean_text
|
38 |
+
import utils
|
39 |
+
|
40 |
+
from models import SynthesizerTrn
|
41 |
+
from text.symbols import symbols
|
42 |
+
import sys
|
43 |
+
|
44 |
+
net_g = None
|
45 |
+
'''
|
46 |
+
device = (
|
47 |
+
"cuda:0"
|
48 |
+
if torch.cuda.is_available()
|
49 |
+
else (
|
50 |
+
"mps"
|
51 |
+
if sys.platform == "darwin" and torch.backends.mps.is_available()
|
52 |
+
else "cpu"
|
53 |
+
)
|
54 |
+
)
|
55 |
+
'''
|
56 |
+
device = "cpu"
|
57 |
+
BandList = {
|
58 |
+
"PoppinParty":["香澄","有咲","たえ","りみ","沙綾"],
|
59 |
+
"Afterglow":["蘭","モカ","ひまり","巴","つぐみ"],
|
60 |
+
"HelloHappyWorld":["こころ","美咲","薫","花音","はぐみ"],
|
61 |
+
"PastelPalettes":["彩","日菜","千聖","イヴ","麻弥"],
|
62 |
+
"Roselia":["友希那","紗夜","リサ","燐子","あこ"],
|
63 |
+
"RaiseASuilen":["レイヤ","ロック","ますき","チュチュ","パレオ"],
|
64 |
+
"Morfonica":["ましろ","瑠唯","つくし","七深","透子"],
|
65 |
+
"MyGo":["燈","愛音","そよ","立希","楽奈"],
|
66 |
+
"AveMujica":["祥子","睦","海鈴","にゃむ","初華"],
|
67 |
+
"圣翔音乐学园":["華戀","光","香子","雙葉","真晝","純那","克洛迪娜","真矢","奈奈"],
|
68 |
+
"凛明馆女子学校":["珠緒","壘","文","悠悠子","一愛"],
|
69 |
+
"弗隆提亚艺术学校":["艾露","艾露露","菈樂菲","司","靜羽"],
|
70 |
+
"西克菲尔特音乐学院":["晶","未知留","八千代","栞","美帆"]
|
71 |
+
}
|
72 |
+
|
73 |
+
def get_net_g(model_path: str, device: str, hps):
|
74 |
+
# 当前版本模型 net_g
|
75 |
+
net_g = SynthesizerTrn(
|
76 |
+
len(symbols),
|
77 |
+
hps.data.filter_length // 2 + 1,
|
78 |
+
hps.train.segment_size // hps.data.hop_length,
|
79 |
+
n_speakers=hps.data.n_speakers,
|
80 |
+
**hps.model,
|
81 |
+
).to(device)
|
82 |
+
_ = net_g.eval()
|
83 |
+
_ = utils.load_checkpoint(model_path, net_g, None, skip_optimizer=True)
|
84 |
+
return net_g
|
85 |
+
|
86 |
+
|
87 |
+
def get_text(text, language_str, hps, device, style_text=None, style_weight=0.7):
|
88 |
+
style_text = None if style_text == "" else style_text
|
89 |
+
# 在此处实现当前版本的get_text
|
90 |
+
norm_text, phone, tone, word2ph = clean_text(text, language_str)
|
91 |
+
phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
|
92 |
+
if hps.data.add_blank:
|
93 |
+
phone = commons.intersperse(phone, 0)
|
94 |
+
tone = commons.intersperse(tone, 0)
|
95 |
+
language = commons.intersperse(language, 0)
|
96 |
+
for i in range(len(word2ph)):
|
97 |
+
word2ph[i] = word2ph[i] * 2
|
98 |
+
word2ph[0] += 1
|
99 |
+
bert = get_bert(norm_text, word2ph, language_str, device, style_text, style_weight)
|
100 |
+
del word2ph
|
101 |
+
|
102 |
+
assert bert.shape[-1] == len(
|
103 |
+
phone
|
104 |
+
), f"Bert seq len {bert.shape[-1]} != {len(phone)}"
|
105 |
+
|
106 |
+
phone = torch.LongTensor(phone)
|
107 |
+
tone = torch.LongTensor(tone)
|
108 |
+
language = torch.LongTensor(language)
|
109 |
+
return bert, phone, tone, language
|
110 |
+
|
111 |
+
def infer(
|
112 |
+
text,
|
113 |
+
sdp_ratio,
|
114 |
+
noise_scale,
|
115 |
+
noise_scale_w,
|
116 |
+
length_scale,
|
117 |
+
sid,
|
118 |
+
emotion,
|
119 |
+
reference_audio=None,
|
120 |
+
skip_start=False,
|
121 |
+
skip_end=False,
|
122 |
+
style_text=None,
|
123 |
+
style_weight=0.7,
|
124 |
+
):
|
125 |
+
language = "JP"
|
126 |
+
if isinstance(reference_audio, np.ndarray):
|
127 |
+
emo = get_clap_audio_feature(reference_audio, device)
|
128 |
+
else:
|
129 |
+
emo = get_clap_text_feature(emotion, device)
|
130 |
+
emo = torch.squeeze(emo, dim=1)
|
131 |
+
|
132 |
+
bert, phones, tones, lang_ids = get_text(
|
133 |
+
text,
|
134 |
+
language,
|
135 |
+
hps,
|
136 |
+
device,
|
137 |
+
style_text=style_text,
|
138 |
+
style_weight=style_weight,
|
139 |
+
)
|
140 |
+
if skip_start:
|
141 |
+
phones = phones[3:]
|
142 |
+
tones = tones[3:]
|
143 |
+
lang_ids = lang_ids[3:]
|
144 |
+
bert = bert[:, 3:]
|
145 |
+
if skip_end:
|
146 |
+
phones = phones[:-2]
|
147 |
+
tones = tones[:-2]
|
148 |
+
lang_ids = lang_ids[:-2]
|
149 |
+
bert = bert[:, :-2]
|
150 |
+
with torch.no_grad():
|
151 |
+
x_tst = phones.to(device).unsqueeze(0)
|
152 |
+
tones = tones.to(device).unsqueeze(0)
|
153 |
+
lang_ids = lang_ids.to(device).unsqueeze(0)
|
154 |
+
bert = bert.to(device).unsqueeze(0)
|
155 |
+
x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
|
156 |
+
emo = emo.to(device).unsqueeze(0)
|
157 |
+
del phones
|
158 |
+
speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
|
159 |
+
print(text)
|
160 |
+
audio = (
|
161 |
+
net_g.infer(
|
162 |
+
x_tst,
|
163 |
+
x_tst_lengths,
|
164 |
+
speakers,
|
165 |
+
tones,
|
166 |
+
lang_ids,
|
167 |
+
bert,
|
168 |
+
emo,
|
169 |
+
sdp_ratio=sdp_ratio,
|
170 |
+
noise_scale=noise_scale,
|
171 |
+
noise_scale_w=noise_scale_w,
|
172 |
+
length_scale=length_scale,
|
173 |
+
)[0][0, 0]
|
174 |
+
.data.cpu()
|
175 |
+
.float()
|
176 |
+
.numpy()
|
177 |
+
)
|
178 |
+
del (
|
179 |
+
x_tst,
|
180 |
+
tones,
|
181 |
+
lang_ids,
|
182 |
+
bert,
|
183 |
+
x_tst_lengths,
|
184 |
+
speakers,
|
185 |
+
emo,
|
186 |
+
) # , emo
|
187 |
+
if torch.cuda.is_available():
|
188 |
+
torch.cuda.empty_cache()
|
189 |
+
return (hps.data.sampling_rate,gr.processing_utils.convert_to_16_bit_wav(audio))
|
190 |
+
|
191 |
+
def loadmodel(model):
|
192 |
+
_ = net_g.eval()
|
193 |
+
_ = utils.load_checkpoint(model, net_g, None, skip_optimizer=True)
|
194 |
+
return "success"
|
195 |
+
|
196 |
+
if __name__ == "__main__":
|
197 |
+
modelPaths = []
|
198 |
+
for dirpath, dirnames, filenames in os.walk('Data/BangDream/models/'):
|
199 |
+
for filename in filenames:
|
200 |
+
modelPaths.append(os.path.join(dirpath, filename))
|
201 |
+
hps = utils.get_hparams_from_file('Data/BangDream//config.json')
|
202 |
+
net_g = get_net_g(
|
203 |
+
model_path=modelPaths[-1], device=device, hps=hps
|
204 |
+
)
|
205 |
+
speaker_ids = hps.data.spk2id
|
206 |
+
speakers = list(speaker_ids.keys())
|
207 |
+
with gr.Blocks() as app:
|
208 |
+
for band in BandList:
|
209 |
+
with gr.TabItem(band):
|
210 |
+
for name in BandList[band]:
|
211 |
+
with gr.TabItem(name):
|
212 |
+
with gr.Row():
|
213 |
+
with gr.Column():
|
214 |
+
with gr.Row():
|
215 |
+
gr.Markdown(
|
216 |
+
'<div align="center">'
|
217 |
+
f'<img style="width:auto;height:400px;" src="https://mahiruoshi-bangdream-bert-vits2.hf.space/file/image/{name}.png">'
|
218 |
+
'</div>'
|
219 |
+
)
|
220 |
+
length_scale = gr.Slider(
|
221 |
+
minimum=0.1, maximum=2, value=1, step=0.01, label="语速调节"
|
222 |
+
)
|
223 |
+
emotion = gr.Textbox(
|
224 |
+
label="情感标注文本t",
|
225 |
+
value = 'なんではるひかげやったの?!!'
|
226 |
+
)
|
227 |
+
style_weight = gr.Slider(
|
228 |
+
minimum=0.1, maximum=2, value=1, step=0.01, label="感情比重"
|
229 |
+
)
|
230 |
+
with gr.Accordion(label="参数设定", open=False):
|
231 |
+
sdp_ratio = gr.Slider(
|
232 |
+
minimum=0, maximum=1, value=0.2, step=0.01, label="SDP/DP混合比"
|
233 |
+
)
|
234 |
+
noise_scale = gr.Slider(
|
235 |
+
minimum=0.1, maximum=2, value=0.6, step=0.01, label="感情调节"
|
236 |
+
)
|
237 |
+
noise_scale_w = gr.Slider(
|
238 |
+
minimum=0.1, maximum=2, value=0.8, step=0.01, label="音素长度"
|
239 |
+
)
|
240 |
+
speaker = gr.Dropdown(
|
241 |
+
choices=speakers, value=name, label="说话人"
|
242 |
+
)
|
243 |
+
skip_start = gr.Checkbox(label="跳过开头")
|
244 |
+
skip_end = gr.Checkbox(label="跳过结尾")
|
245 |
+
with gr.Accordion(label="切换模型", open=False):
|
246 |
+
modelstrs = gr.Dropdown(label = "模型", choices = modelPaths, value = modelPaths[0], type = "value")
|
247 |
+
btnMod = gr.Button("载入模型")
|
248 |
+
statusa = gr.TextArea()
|
249 |
+
btnMod.click(loadmodel, inputs=[modelstrs], outputs = [statusa])
|
250 |
+
with gr.Column():
|
251 |
+
text = gr.TextArea(
|
252 |
+
label="输入纯日语",
|
253 |
+
placeholder="输入纯日语",
|
254 |
+
value="なんではるひかげやったの?!!",
|
255 |
+
)
|
256 |
+
reference_audio = gr.Audio(label="情感参考音频)", type="filepath")
|
257 |
+
btn = gr.Button("点击生成", variant="primary")
|
258 |
+
audio_output = gr.Audio(label="Output Audio")
|
259 |
+
btn.click(
|
260 |
+
infer,
|
261 |
+
inputs=[
|
262 |
+
text,
|
263 |
+
sdp_ratio,
|
264 |
+
noise_scale,
|
265 |
+
noise_scale_w,
|
266 |
+
length_scale,
|
267 |
+
speaker,
|
268 |
+
emotion,
|
269 |
+
reference_audio,
|
270 |
+
skip_start,
|
271 |
+
skip_end,
|
272 |
+
emotion,
|
273 |
+
style_weight,
|
274 |
+
],
|
275 |
+
outputs=[audio_output],
|
276 |
+
)
|
277 |
+
|
278 |
+
print("推理页面已开启!")
|
279 |
+
app.launch(share=True)
|
attentions.py
ADDED
@@ -0,0 +1,464 @@
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|
1 |
+
import math
|
2 |
+
import torch
|
3 |
+
from torch import nn
|
4 |
+
from torch.nn import functional as F
|
5 |
+
|
6 |
+
import commons
|
7 |
+
import logging
|
8 |
+
|
9 |
+
logger = logging.getLogger(__name__)
|
10 |
+
|
11 |
+
|
12 |
+
class LayerNorm(nn.Module):
|
13 |
+
def __init__(self, channels, eps=1e-5):
|
14 |
+
super().__init__()
|
15 |
+
self.channels = channels
|
16 |
+
self.eps = eps
|
17 |
+
|
18 |
+
self.gamma = nn.Parameter(torch.ones(channels))
|
19 |
+
self.beta = nn.Parameter(torch.zeros(channels))
|
20 |
+
|
21 |
+
def forward(self, x):
|
22 |
+
x = x.transpose(1, -1)
|
23 |
+
x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
|
24 |
+
return x.transpose(1, -1)
|
25 |
+
|
26 |
+
|
27 |
+
@torch.jit.script
|
28 |
+
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
|
29 |
+
n_channels_int = n_channels[0]
|
30 |
+
in_act = input_a + input_b
|
31 |
+
t_act = torch.tanh(in_act[:, :n_channels_int, :])
|
32 |
+
s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
|
33 |
+
acts = t_act * s_act
|
34 |
+
return acts
|
35 |
+
|
36 |
+
|
37 |
+
class Encoder(nn.Module):
|
38 |
+
def __init__(
|
39 |
+
self,
|
40 |
+
hidden_channels,
|
41 |
+
filter_channels,
|
42 |
+
n_heads,
|
43 |
+
n_layers,
|
44 |
+
kernel_size=1,
|
45 |
+
p_dropout=0.0,
|
46 |
+
window_size=4,
|
47 |
+
isflow=True,
|
48 |
+
**kwargs
|
49 |
+
):
|
50 |
+
super().__init__()
|
51 |
+
self.hidden_channels = hidden_channels
|
52 |
+
self.filter_channels = filter_channels
|
53 |
+
self.n_heads = n_heads
|
54 |
+
self.n_layers = n_layers
|
55 |
+
self.kernel_size = kernel_size
|
56 |
+
self.p_dropout = p_dropout
|
57 |
+
self.window_size = window_size
|
58 |
+
# if isflow:
|
59 |
+
# cond_layer = torch.nn.Conv1d(256, 2*hidden_channels*n_layers, 1)
|
60 |
+
# self.cond_pre = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, 1)
|
61 |
+
# self.cond_layer = weight_norm(cond_layer, name='weight')
|
62 |
+
# self.gin_channels = 256
|
63 |
+
self.cond_layer_idx = self.n_layers
|
64 |
+
if "gin_channels" in kwargs:
|
65 |
+
self.gin_channels = kwargs["gin_channels"]
|
66 |
+
if self.gin_channels != 0:
|
67 |
+
self.spk_emb_linear = nn.Linear(self.gin_channels, self.hidden_channels)
|
68 |
+
# vits2 says 3rd block, so idx is 2 by default
|
69 |
+
self.cond_layer_idx = (
|
70 |
+
kwargs["cond_layer_idx"] if "cond_layer_idx" in kwargs else 2
|
71 |
+
)
|
72 |
+
logging.debug(self.gin_channels, self.cond_layer_idx)
|
73 |
+
assert (
|
74 |
+
self.cond_layer_idx < self.n_layers
|
75 |
+
), "cond_layer_idx should be less than n_layers"
|
76 |
+
self.drop = nn.Dropout(p_dropout)
|
77 |
+
self.attn_layers = nn.ModuleList()
|
78 |
+
self.norm_layers_1 = nn.ModuleList()
|
79 |
+
self.ffn_layers = nn.ModuleList()
|
80 |
+
self.norm_layers_2 = nn.ModuleList()
|
81 |
+
for i in range(self.n_layers):
|
82 |
+
self.attn_layers.append(
|
83 |
+
MultiHeadAttention(
|
84 |
+
hidden_channels,
|
85 |
+
hidden_channels,
|
86 |
+
n_heads,
|
87 |
+
p_dropout=p_dropout,
|
88 |
+
window_size=window_size,
|
89 |
+
)
|
90 |
+
)
|
91 |
+
self.norm_layers_1.append(LayerNorm(hidden_channels))
|
92 |
+
self.ffn_layers.append(
|
93 |
+
FFN(
|
94 |
+
hidden_channels,
|
95 |
+
hidden_channels,
|
96 |
+
filter_channels,
|
97 |
+
kernel_size,
|
98 |
+
p_dropout=p_dropout,
|
99 |
+
)
|
100 |
+
)
|
101 |
+
self.norm_layers_2.append(LayerNorm(hidden_channels))
|
102 |
+
|
103 |
+
def forward(self, x, x_mask, g=None):
|
104 |
+
attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
|
105 |
+
x = x * x_mask
|
106 |
+
for i in range(self.n_layers):
|
107 |
+
if i == self.cond_layer_idx and g is not None:
|
108 |
+
g = self.spk_emb_linear(g.transpose(1, 2))
|
109 |
+
g = g.transpose(1, 2)
|
110 |
+
x = x + g
|
111 |
+
x = x * x_mask
|
112 |
+
y = self.attn_layers[i](x, x, attn_mask)
|
113 |
+
y = self.drop(y)
|
114 |
+
x = self.norm_layers_1[i](x + y)
|
115 |
+
|
116 |
+
y = self.ffn_layers[i](x, x_mask)
|
117 |
+
y = self.drop(y)
|
118 |
+
x = self.norm_layers_2[i](x + y)
|
119 |
+
x = x * x_mask
|
120 |
+
return x
|
121 |
+
|
122 |
+
|
123 |
+
class Decoder(nn.Module):
|
124 |
+
def __init__(
|
125 |
+
self,
|
126 |
+
hidden_channels,
|
127 |
+
filter_channels,
|
128 |
+
n_heads,
|
129 |
+
n_layers,
|
130 |
+
kernel_size=1,
|
131 |
+
p_dropout=0.0,
|
132 |
+
proximal_bias=False,
|
133 |
+
proximal_init=True,
|
134 |
+
**kwargs
|
135 |
+
):
|
136 |
+
super().__init__()
|
137 |
+
self.hidden_channels = hidden_channels
|
138 |
+
self.filter_channels = filter_channels
|
139 |
+
self.n_heads = n_heads
|
140 |
+
self.n_layers = n_layers
|
141 |
+
self.kernel_size = kernel_size
|
142 |
+
self.p_dropout = p_dropout
|
143 |
+
self.proximal_bias = proximal_bias
|
144 |
+
self.proximal_init = proximal_init
|
145 |
+
|
146 |
+
self.drop = nn.Dropout(p_dropout)
|
147 |
+
self.self_attn_layers = nn.ModuleList()
|
148 |
+
self.norm_layers_0 = nn.ModuleList()
|
149 |
+
self.encdec_attn_layers = nn.ModuleList()
|
150 |
+
self.norm_layers_1 = nn.ModuleList()
|
151 |
+
self.ffn_layers = nn.ModuleList()
|
152 |
+
self.norm_layers_2 = nn.ModuleList()
|
153 |
+
for i in range(self.n_layers):
|
154 |
+
self.self_attn_layers.append(
|
155 |
+
MultiHeadAttention(
|
156 |
+
hidden_channels,
|
157 |
+
hidden_channels,
|
158 |
+
n_heads,
|
159 |
+
p_dropout=p_dropout,
|
160 |
+
proximal_bias=proximal_bias,
|
161 |
+
proximal_init=proximal_init,
|
162 |
+
)
|
163 |
+
)
|
164 |
+
self.norm_layers_0.append(LayerNorm(hidden_channels))
|
165 |
+
self.encdec_attn_layers.append(
|
166 |
+
MultiHeadAttention(
|
167 |
+
hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout
|
168 |
+
)
|
169 |
+
)
|
170 |
+
self.norm_layers_1.append(LayerNorm(hidden_channels))
|
171 |
+
self.ffn_layers.append(
|
172 |
+
FFN(
|
173 |
+
hidden_channels,
|
174 |
+
hidden_channels,
|
175 |
+
filter_channels,
|
176 |
+
kernel_size,
|
177 |
+
p_dropout=p_dropout,
|
178 |
+
causal=True,
|
179 |
+
)
|
180 |
+
)
|
181 |
+
self.norm_layers_2.append(LayerNorm(hidden_channels))
|
182 |
+
|
183 |
+
def forward(self, x, x_mask, h, h_mask):
|
184 |
+
"""
|
185 |
+
x: decoder input
|
186 |
+
h: encoder output
|
187 |
+
"""
|
188 |
+
self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(
|
189 |
+
device=x.device, dtype=x.dtype
|
190 |
+
)
|
191 |
+
encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
|
192 |
+
x = x * x_mask
|
193 |
+
for i in range(self.n_layers):
|
194 |
+
y = self.self_attn_layers[i](x, x, self_attn_mask)
|
195 |
+
y = self.drop(y)
|
196 |
+
x = self.norm_layers_0[i](x + y)
|
197 |
+
|
198 |
+
y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
|
199 |
+
y = self.drop(y)
|
200 |
+
x = self.norm_layers_1[i](x + y)
|
201 |
+
|
202 |
+
y = self.ffn_layers[i](x, x_mask)
|
203 |
+
y = self.drop(y)
|
204 |
+
x = self.norm_layers_2[i](x + y)
|
205 |
+
x = x * x_mask
|
206 |
+
return x
|
207 |
+
|
208 |
+
|
209 |
+
class MultiHeadAttention(nn.Module):
|
210 |
+
def __init__(
|
211 |
+
self,
|
212 |
+
channels,
|
213 |
+
out_channels,
|
214 |
+
n_heads,
|
215 |
+
p_dropout=0.0,
|
216 |
+
window_size=None,
|
217 |
+
heads_share=True,
|
218 |
+
block_length=None,
|
219 |
+
proximal_bias=False,
|
220 |
+
proximal_init=False,
|
221 |
+
):
|
222 |
+
super().__init__()
|
223 |
+
assert channels % n_heads == 0
|
224 |
+
|
225 |
+
self.channels = channels
|
226 |
+
self.out_channels = out_channels
|
227 |
+
self.n_heads = n_heads
|
228 |
+
self.p_dropout = p_dropout
|
229 |
+
self.window_size = window_size
|
230 |
+
self.heads_share = heads_share
|
231 |
+
self.block_length = block_length
|
232 |
+
self.proximal_bias = proximal_bias
|
233 |
+
self.proximal_init = proximal_init
|
234 |
+
self.attn = None
|
235 |
+
|
236 |
+
self.k_channels = channels // n_heads
|
237 |
+
self.conv_q = nn.Conv1d(channels, channels, 1)
|
238 |
+
self.conv_k = nn.Conv1d(channels, channels, 1)
|
239 |
+
self.conv_v = nn.Conv1d(channels, channels, 1)
|
240 |
+
self.conv_o = nn.Conv1d(channels, out_channels, 1)
|
241 |
+
self.drop = nn.Dropout(p_dropout)
|
242 |
+
|
243 |
+
if window_size is not None:
|
244 |
+
n_heads_rel = 1 if heads_share else n_heads
|
245 |
+
rel_stddev = self.k_channels**-0.5
|
246 |
+
self.emb_rel_k = nn.Parameter(
|
247 |
+
torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
|
248 |
+
* rel_stddev
|
249 |
+
)
|
250 |
+
self.emb_rel_v = nn.Parameter(
|
251 |
+
torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
|
252 |
+
* rel_stddev
|
253 |
+
)
|
254 |
+
|
255 |
+
nn.init.xavier_uniform_(self.conv_q.weight)
|
256 |
+
nn.init.xavier_uniform_(self.conv_k.weight)
|
257 |
+
nn.init.xavier_uniform_(self.conv_v.weight)
|
258 |
+
if proximal_init:
|
259 |
+
with torch.no_grad():
|
260 |
+
self.conv_k.weight.copy_(self.conv_q.weight)
|
261 |
+
self.conv_k.bias.copy_(self.conv_q.bias)
|
262 |
+
|
263 |
+
def forward(self, x, c, attn_mask=None):
|
264 |
+
q = self.conv_q(x)
|
265 |
+
k = self.conv_k(c)
|
266 |
+
v = self.conv_v(c)
|
267 |
+
|
268 |
+
x, self.attn = self.attention(q, k, v, mask=attn_mask)
|
269 |
+
|
270 |
+
x = self.conv_o(x)
|
271 |
+
return x
|
272 |
+
|
273 |
+
def attention(self, query, key, value, mask=None):
|
274 |
+
# reshape [b, d, t] -> [b, n_h, t, d_k]
|
275 |
+
b, d, t_s, t_t = (*key.size(), query.size(2))
|
276 |
+
query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
|
277 |
+
key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
|
278 |
+
value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
|
279 |
+
|
280 |
+
scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
|
281 |
+
if self.window_size is not None:
|
282 |
+
assert (
|
283 |
+
t_s == t_t
|
284 |
+
), "Relative attention is only available for self-attention."
|
285 |
+
key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
|
286 |
+
rel_logits = self._matmul_with_relative_keys(
|
287 |
+
query / math.sqrt(self.k_channels), key_relative_embeddings
|
288 |
+
)
|
289 |
+
scores_local = self._relative_position_to_absolute_position(rel_logits)
|
290 |
+
scores = scores + scores_local
|
291 |
+
if self.proximal_bias:
|
292 |
+
assert t_s == t_t, "Proximal bias is only available for self-attention."
|
293 |
+
scores = scores + self._attention_bias_proximal(t_s).to(
|
294 |
+
device=scores.device, dtype=scores.dtype
|
295 |
+
)
|
296 |
+
if mask is not None:
|
297 |
+
scores = scores.masked_fill(mask == 0, -1e4)
|
298 |
+
if self.block_length is not None:
|
299 |
+
assert (
|
300 |
+
t_s == t_t
|
301 |
+
), "Local attention is only available for self-attention."
|
302 |
+
block_mask = (
|
303 |
+
torch.ones_like(scores)
|
304 |
+
.triu(-self.block_length)
|
305 |
+
.tril(self.block_length)
|
306 |
+
)
|
307 |
+
scores = scores.masked_fill(block_mask == 0, -1e4)
|
308 |
+
p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
|
309 |
+
p_attn = self.drop(p_attn)
|
310 |
+
output = torch.matmul(p_attn, value)
|
311 |
+
if self.window_size is not None:
|
312 |
+
relative_weights = self._absolute_position_to_relative_position(p_attn)
|
313 |
+
value_relative_embeddings = self._get_relative_embeddings(
|
314 |
+
self.emb_rel_v, t_s
|
315 |
+
)
|
316 |
+
output = output + self._matmul_with_relative_values(
|
317 |
+
relative_weights, value_relative_embeddings
|
318 |
+
)
|
319 |
+
output = (
|
320 |
+
output.transpose(2, 3).contiguous().view(b, d, t_t)
|
321 |
+
) # [b, n_h, t_t, d_k] -> [b, d, t_t]
|
322 |
+
return output, p_attn
|
323 |
+
|
324 |
+
def _matmul_with_relative_values(self, x, y):
|
325 |
+
"""
|
326 |
+
x: [b, h, l, m]
|
327 |
+
y: [h or 1, m, d]
|
328 |
+
ret: [b, h, l, d]
|
329 |
+
"""
|
330 |
+
ret = torch.matmul(x, y.unsqueeze(0))
|
331 |
+
return ret
|
332 |
+
|
333 |
+
def _matmul_with_relative_keys(self, x, y):
|
334 |
+
"""
|
335 |
+
x: [b, h, l, d]
|
336 |
+
y: [h or 1, m, d]
|
337 |
+
ret: [b, h, l, m]
|
338 |
+
"""
|
339 |
+
ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
|
340 |
+
return ret
|
341 |
+
|
342 |
+
def _get_relative_embeddings(self, relative_embeddings, length):
|
343 |
+
2 * self.window_size + 1
|
344 |
+
# Pad first before slice to avoid using cond ops.
|
345 |
+
pad_length = max(length - (self.window_size + 1), 0)
|
346 |
+
slice_start_position = max((self.window_size + 1) - length, 0)
|
347 |
+
slice_end_position = slice_start_position + 2 * length - 1
|
348 |
+
if pad_length > 0:
|
349 |
+
padded_relative_embeddings = F.pad(
|
350 |
+
relative_embeddings,
|
351 |
+
commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
|
352 |
+
)
|
353 |
+
else:
|
354 |
+
padded_relative_embeddings = relative_embeddings
|
355 |
+
used_relative_embeddings = padded_relative_embeddings[
|
356 |
+
:, slice_start_position:slice_end_position
|
357 |
+
]
|
358 |
+
return used_relative_embeddings
|
359 |
+
|
360 |
+
def _relative_position_to_absolute_position(self, x):
|
361 |
+
"""
|
362 |
+
x: [b, h, l, 2*l-1]
|
363 |
+
ret: [b, h, l, l]
|
364 |
+
"""
|
365 |
+
batch, heads, length, _ = x.size()
|
366 |
+
# Concat columns of pad to shift from relative to absolute indexing.
|
367 |
+
x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
|
368 |
+
|
369 |
+
# Concat extra elements so to add up to shape (len+1, 2*len-1).
|
370 |
+
x_flat = x.view([batch, heads, length * 2 * length])
|
371 |
+
x_flat = F.pad(
|
372 |
+
x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
|
373 |
+
)
|
374 |
+
|
375 |
+
# Reshape and slice out the padded elements.
|
376 |
+
x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
|
377 |
+
:, :, :length, length - 1 :
|
378 |
+
]
|
379 |
+
return x_final
|
380 |
+
|
381 |
+
def _absolute_position_to_relative_position(self, x):
|
382 |
+
"""
|
383 |
+
x: [b, h, l, l]
|
384 |
+
ret: [b, h, l, 2*l-1]
|
385 |
+
"""
|
386 |
+
batch, heads, length, _ = x.size()
|
387 |
+
# pad along column
|
388 |
+
x = F.pad(
|
389 |
+
x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
|
390 |
+
)
|
391 |
+
x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
|
392 |
+
# add 0's in the beginning that will skew the elements after reshape
|
393 |
+
x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
|
394 |
+
x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
|
395 |
+
return x_final
|
396 |
+
|
397 |
+
def _attention_bias_proximal(self, length):
|
398 |
+
"""Bias for self-attention to encourage attention to close positions.
|
399 |
+
Args:
|
400 |
+
length: an integer scalar.
|
401 |
+
Returns:
|
402 |
+
a Tensor with shape [1, 1, length, length]
|
403 |
+
"""
|
404 |
+
r = torch.arange(length, dtype=torch.float32)
|
405 |
+
diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
|
406 |
+
return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
|
407 |
+
|
408 |
+
|
409 |
+
class FFN(nn.Module):
|
410 |
+
def __init__(
|
411 |
+
self,
|
412 |
+
in_channels,
|
413 |
+
out_channels,
|
414 |
+
filter_channels,
|
415 |
+
kernel_size,
|
416 |
+
p_dropout=0.0,
|
417 |
+
activation=None,
|
418 |
+
causal=False,
|
419 |
+
):
|
420 |
+
super().__init__()
|
421 |
+
self.in_channels = in_channels
|
422 |
+
self.out_channels = out_channels
|
423 |
+
self.filter_channels = filter_channels
|
424 |
+
self.kernel_size = kernel_size
|
425 |
+
self.p_dropout = p_dropout
|
426 |
+
self.activation = activation
|
427 |
+
self.causal = causal
|
428 |
+
|
429 |
+
if causal:
|
430 |
+
self.padding = self._causal_padding
|
431 |
+
else:
|
432 |
+
self.padding = self._same_padding
|
433 |
+
|
434 |
+
self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
|
435 |
+
self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
|
436 |
+
self.drop = nn.Dropout(p_dropout)
|
437 |
+
|
438 |
+
def forward(self, x, x_mask):
|
439 |
+
x = self.conv_1(self.padding(x * x_mask))
|
440 |
+
if self.activation == "gelu":
|
441 |
+
x = x * torch.sigmoid(1.702 * x)
|
442 |
+
else:
|
443 |
+
x = torch.relu(x)
|
444 |
+
x = self.drop(x)
|
445 |
+
x = self.conv_2(self.padding(x * x_mask))
|
446 |
+
return x * x_mask
|
447 |
+
|
448 |
+
def _causal_padding(self, x):
|
449 |
+
if self.kernel_size == 1:
|
450 |
+
return x
|
451 |
+
pad_l = self.kernel_size - 1
|
452 |
+
pad_r = 0
|
453 |
+
padding = [[0, 0], [0, 0], [pad_l, pad_r]]
|
454 |
+
x = F.pad(x, commons.convert_pad_shape(padding))
|
455 |
+
return x
|
456 |
+
|
457 |
+
def _same_padding(self, x):
|
458 |
+
if self.kernel_size == 1:
|
459 |
+
return x
|
460 |
+
pad_l = (self.kernel_size - 1) // 2
|
461 |
+
pad_r = self.kernel_size // 2
|
462 |
+
padding = [[0, 0], [0, 0], [pad_l, pad_r]]
|
463 |
+
x = F.pad(x, commons.convert_pad_shape(padding))
|
464 |
+
return x
|
bert/Erlangshen-DeBERTa-v2-710M-Chinese/config.json
ADDED
@@ -0,0 +1,35 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"model_type": "deberta-v2",
|
3 |
+
"architectures": [
|
4 |
+
"DebertaV2ForMaskedLM"
|
5 |
+
],
|
6 |
+
"attention_probs_dropout_prob": 0.1,
|
7 |
+
"attention_head_size": 64,
|
8 |
+
"hidden_act": "gelu",
|
9 |
+
"hidden_dropout_prob": 0.1,
|
10 |
+
"hidden_size": 1536,
|
11 |
+
"initializer_range": 0.02,
|
12 |
+
"intermediate_size": 6144,
|
13 |
+
"max_position_embeddings": 512,
|
14 |
+
"relative_attention": true,
|
15 |
+
"position_buckets": 256,
|
16 |
+
"norm_rel_ebd": "layer_norm",
|
17 |
+
"share_att_key": true,
|
18 |
+
"pos_att_type": [
|
19 |
+
"p2c",
|
20 |
+
"c2p"
|
21 |
+
],
|
22 |
+
"conv_kernel_size": 3,
|
23 |
+
"pooler_dropout": 0,
|
24 |
+
"pooler_hidden_act": "gelu",
|
25 |
+
"pooler_hidden_size": 1536,
|
26 |
+
"conv_act": "gelu",
|
27 |
+
"layer_norm_eps": 1e-7,
|
28 |
+
"max_relative_positions": -1,
|
29 |
+
"position_biased_input": false,
|
30 |
+
"num_attention_heads": 24,
|
31 |
+
"num_hidden_layers": 24,
|
32 |
+
"type_vocab_size": 0,
|
33 |
+
"num_labels": 119,
|
34 |
+
"vocab_size": 12800
|
35 |
+
}
|
bert/Erlangshen-DeBERTa-v2-710M-Chinese/special_tokens_map.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}
|
bert/Erlangshen-DeBERTa-v2-710M-Chinese/tokenizer_config.json
ADDED
@@ -0,0 +1,15 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"do_lower_case": true,
|
3 |
+
"do_basic_tokenize": true,
|
4 |
+
"never_split": null,
|
5 |
+
"unk_token": "[UNK]",
|
6 |
+
"sep_token": "[SEP]",
|
7 |
+
"pad_token": "[PAD]",
|
8 |
+
"cls_token": "[CLS]",
|
9 |
+
"mask_token": "[MASK]",
|
10 |
+
"tokenize_chinese_chars": true,
|
11 |
+
"strip_accents": null,
|
12 |
+
"special_tokens_map_file": null,
|
13 |
+
"name_or_path": "/cognitive_comp/gaoxinyu/pretrained_model/bert-1.3B",
|
14 |
+
"tokenizer_class": "BertTokenizer"
|
15 |
+
}
|
bert/Erlangshen-DeBERTa-v2-710M-Chinese/vocab.txt
ADDED
@@ -0,0 +1,12800 @@
|
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|
|
|
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|
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1 |
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[unused81]
|
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[unused82]
|
88 |
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[unused83]
|
89 |
+
[unused84]
|
90 |
+
[unused85]
|
91 |
+
[unused86]
|
92 |
+
[unused87]
|
93 |
+
[unused88]
|
94 |
+
[unused89]
|
95 |
+
[unused90]
|
96 |
+
[unused91]
|
97 |
+
[unused92]
|
98 |
+
[unused93]
|
99 |
+
[unused94]
|
100 |
+
[unused95]
|
101 |
+
[unused96]
|
102 |
+
[unused97]
|
103 |
+
[unused98]
|
104 |
+
[unused99]
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冴
|
698 |
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况
|
699 |
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冶
|
700 |
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冷
|
701 |
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冻
|
702 |
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冼
|
703 |
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冽
|
704 |
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净
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705 |
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凃
|
706 |
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凄
|
707 |
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准
|
708 |
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凇
|
709 |
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凉
|
710 |
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凊
|
711 |
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凋
|
712 |
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凌
|
713 |
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减
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714 |
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凑
|
715 |
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凖
|
716 |
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凛
|
717 |
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凝
|
718 |
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几
|
719 |
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凡
|
720 |
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凤
|
721 |
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処
|
722 |
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凪
|
723 |
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凫
|
724 |
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凭
|
725 |
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凯
|
726 |
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凰
|
727 |
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凳
|
728 |
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凶
|
729 |
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凸
|
730 |
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凹
|
731 |
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出
|
732 |
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击
|
733 |
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凼
|
734 |
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函
|
735 |
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凿
|
736 |
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刀
|
737 |
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刁
|
738 |
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刂
|
739 |
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刃
|
740 |
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分
|
741 |
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切
|
742 |
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刈
|
743 |
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刊
|
744 |
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刋
|
745 |
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刍
|
746 |
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刎
|
747 |
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刑
|
748 |
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划
|
749 |
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刖
|
750 |
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列
|
751 |
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刘
|
752 |
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则
|
753 |
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刚
|
754 |
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创
|
755 |
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初
|
756 |
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删
|
757 |
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判
|
758 |
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刨
|
759 |
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利
|
760 |
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别
|
761 |
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刬
|
762 |
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刭
|
763 |
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刮
|
764 |
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到
|
765 |
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刳
|
766 |
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制
|
767 |
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刷
|
768 |
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券
|
769 |
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刹
|
770 |
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刺
|
771 |
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刻
|
772 |
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刽
|
773 |
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刿
|
774 |
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剀
|
775 |
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剁
|
776 |
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剂
|
777 |
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剃
|
778 |
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剅
|
779 |
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削
|
780 |
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剌
|
781 |
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前
|
782 |
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剐
|
783 |
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剑
|
784 |
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剔
|
785 |
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剖
|
786 |
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剜
|
787 |
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剞
|
788 |
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剡
|
789 |
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剣
|
790 |
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剥
|
791 |
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剧
|
792 |
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剩
|
793 |
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剪
|
794 |
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副
|
795 |
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割
|
796 |
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剽
|
797 |
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剿
|
798 |
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劂
|
799 |
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劄
|
800 |
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劈
|
801 |
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劏
|
802 |
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劓
|
803 |
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力
|
804 |
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劝
|
805 |
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办
|
806 |
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功
|
807 |
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加
|
808 |
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务
|
809 |
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劢
|
810 |
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劣
|
811 |
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动
|
812 |
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助
|
813 |
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努
|
814 |
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劫
|
815 |
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劬
|
816 |
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劭
|
817 |
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励
|
818 |
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劲
|
819 |
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劳
|
820 |
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劵
|
821 |
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効
|
822 |
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劻
|
823 |
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劼
|
824 |
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劾
|
825 |
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势
|
826 |
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勃
|
827 |
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勅
|
828 |
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勇
|
829 |
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勉
|
830 |
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勋
|
831 |
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勍
|
832 |
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勐
|
833 |
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勑
|
834 |
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勒
|
835 |
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勔
|
836 |
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勖
|
837 |
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勘
|
838 |
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募
|
839 |
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勠
|
840 |
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勤
|
841 |
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勰
|
842 |
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勲
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843 |
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勳
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844 |
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勷
|
845 |
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勺
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846 |
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勾
|
847 |
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勿
|
848 |
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匀
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849 |
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匂
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850 |
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匄
|
851 |
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包
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852 |
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匆
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853 |
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匈
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854 |
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匋
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855 |
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匍
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856 |
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匏
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857 |
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匐
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858 |
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匕
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859 |
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化
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860 |
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北
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861 |
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匙
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862 |
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匚
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863 |
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匜
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864 |
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匝
|
865 |
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匠
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866 |
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匡
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867 |
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匣
|
868 |
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匦
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869 |
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匪
|
870 |
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匮
|
871 |
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匹
|
872 |
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区
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873 |
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医
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874 |
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匾
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875 |
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匿
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876 |
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區
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877 |
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十
|
878 |
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千
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879 |
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卅
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880 |
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升
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881 |
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午
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882 |
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卉
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883 |
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半
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884 |
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卌
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885 |
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卍
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886 |
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华
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887 |
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协
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888 |
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卐
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889 |
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卑
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890 |
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卒
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891 |
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卓
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892 |
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单
|
893 |
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卖
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894 |
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南
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895 |
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単
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896 |
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博
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897 |
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卜
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898 |
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卝
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899 |
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卞
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900 |
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卟
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901 |
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占
|
902 |
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卡
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903 |
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卢
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904 |
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卣
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905 |
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卤
|
906 |
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卦
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907 |
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卧
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908 |
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卨
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909 |
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卫
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910 |
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卬
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911 |
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卮
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912 |
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卯
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913 |
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印
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914 |
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危
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915 |
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卲
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916 |
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即
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917 |
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却
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918 |
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卵
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919 |
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卷
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920 |
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卸
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921 |
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卺
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922 |
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卽
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923 |
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卿
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924 |
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厂
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925 |
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厄
|
926 |
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厅
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927 |
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历
|
928 |
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厉
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929 |
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压
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930 |
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厌
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931 |
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厍
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932 |
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厓
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933 |
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厔
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934 |
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厕
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935 |
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厘
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936 |
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厚
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937 |
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厝
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938 |
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原
|
939 |
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厢
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940 |
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厣
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941 |
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厥
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942 |
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厦
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943 |
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厨
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944 |
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厩
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945 |
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厮
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946 |
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厶
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947 |
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去
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948 |
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县
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949 |
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叁
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950 |
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参
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951 |
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叆
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952 |
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又
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953 |
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叉
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954 |
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及
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955 |
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友
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956 |
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双
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957 |
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反
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958 |
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収
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959 |
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发
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960 |
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叒
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961 |
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叔
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962 |
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叕
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963 |
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取
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964 |
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受
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965 |
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变
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966 |
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叙
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967 |
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叛
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968 |
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叟
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969 |
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叠
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970 |
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叡
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971 |
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口
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972 |
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古
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973 |
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句
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974 |
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另
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975 |
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叨
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976 |
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叩
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977 |
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只
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978 |
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叫
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979 |
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召
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980 |
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叭
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981 |
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叮
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982 |
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可
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983 |
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台
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984 |
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叱
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985 |
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史
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986 |
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右
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987 |
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叵
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988 |
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叶
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989 |
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号
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990 |
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司
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991 |
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叹
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992 |
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叻
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993 |
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叼
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994 |
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叽
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995 |
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吁
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996 |
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吃
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997 |
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各
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998 |
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吅
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999 |
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吆
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1000 |
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吇
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1001 |
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合
|
1002 |
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吉
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1003 |
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吊
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1004 |
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吋
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1005 |
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同
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1006 |
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名
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1007 |
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后
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1008 |
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吏
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1009 |
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吐
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1010 |
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向
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1011 |
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吒
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1012 |
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吓
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1013 |
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吔
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1014 |
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吕
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1015 |
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吖
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1016 |
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吗
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1017 |
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吙
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1018 |
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吚
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1019 |
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君
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1020 |
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吝
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1021 |
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吞
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1022 |
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吟
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1023 |
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吠
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1024 |
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吡
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1025 |
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吥
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1026 |
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否
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1027 |
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吧
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1028 |
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吨
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1029 |
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吩
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1030 |
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含
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1031 |
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听
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1032 |
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吭
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1033 |
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吮
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1034 |
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启
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1035 |
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吱
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1036 |
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吲
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吴
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1038 |
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吵
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1039 |
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吸
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1040 |
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吹
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1041 |
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吻
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1042 |
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吼
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吽
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1044 |
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吾
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1045 |
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吿
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呀
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呃
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呆
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呈
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呉
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告
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呋
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呎
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呐
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呑
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呒
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呓
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呔
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呕
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呖
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呗
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员
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1063 |
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呙
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呛
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1065 |
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呜
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1066 |
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呢
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呣
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呤
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1069 |
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呦
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1070 |
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周
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1071 |
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呪
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呬
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呯
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1074 |
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呱
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1075 |
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呲
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1076 |
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味
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呴
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1078 |
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呵
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呶
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呷
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1081 |
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呸
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呻
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呼
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命
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呾
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咀
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咁
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咂
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咄
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咆
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咋
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和
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咎
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咏
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咐
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咒
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咔
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咕
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咖
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咗
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咘
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咙
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咚
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咛
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咝
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咢
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咣
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咤
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咥
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咧
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咨
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1123 |
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咽
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1126 |
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咾
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哀
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哂
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哐
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哑
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1172 |
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1173 |
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1174 |
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1176 |
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1177 |
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1178 |
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1180 |
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1181 |
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啁
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啊
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1231 |
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1235 |
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喺
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1258 |
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喻
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1259 |
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喽
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1260 |
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喾
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1261 |
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1262 |
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嗅
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1263 |
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嗉
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1264 |
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嗌
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1265 |
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嗍
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1266 |
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嗑
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1267 |
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嗒
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1268 |
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嗓
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1269 |
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嗔
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1270 |
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嗖
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1271 |
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嗜
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1272 |
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嗝
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1273 |
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嗞
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1274 |
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嗟
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1275 |
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嗡
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1276 |
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嗣
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1277 |
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嗤
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1278 |
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嗥
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1279 |
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嗦
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1280 |
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嗨
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1281 |
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嗪
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1282 |
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嗫
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1283 |
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嗬
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1284 |
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嗮
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1285 |
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嗯
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1286 |
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嗰
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1287 |
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嗲
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1288 |
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嗳
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1289 |
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嗵
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1290 |
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嗷
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1291 |
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嗽
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1292 |
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嗾
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1293 |
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嘀
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1294 |
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嘁
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1295 |
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嘅
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1296 |
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嘈
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1297 |
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嘉
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1298 |
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嘌
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1299 |
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嘎
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1300 |
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嘏
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1301 |
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嘘
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1302 |
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嘚
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1303 |
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嘛
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1304 |
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嘞
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1305 |
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嘟
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1306 |
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嘢
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1307 |
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嘣
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1308 |
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嘤
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1309 |
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嘦
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1310 |
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嘧
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1311 |
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嘬
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1312 |
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嘭
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1313 |
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嘱
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1314 |
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嘲
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嘴
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嘶
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1317 |
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嘹
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1318 |
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嘻
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嘿
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1320 |
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噁
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1321 |
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噉
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1322 |
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噌
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1323 |
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噎
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1324 |
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噐
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1325 |
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噔
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1326 |
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噗
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1327 |
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噘
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1328 |
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噙
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1329 |
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噜
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1330 |
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噢
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1331 |
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噤
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1332 |
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器
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1333 |
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噩
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1334 |
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噪
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1335 |
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噫
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1336 |
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噬
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1337 |
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噱
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1338 |
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噶
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1339 |
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噻
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1340 |
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噼
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嚅
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1342 |
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嚈
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嚎
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1344 |
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嚏
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1345 |
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嚐
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1346 |
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嚒
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1347 |
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嚓
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1348 |
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嚟
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1349 |
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嚣
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1350 |
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嚧
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1351 |
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嚩
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1352 |
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嚭
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1353 |
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嚯
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1354 |
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嚷
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1355 |
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嚼
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1356 |
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囊
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囍
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1358 |
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囔
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1359 |
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囖
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1360 |
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囗
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1361 |
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囘
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1362 |
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囚
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四
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囝
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1365 |
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回
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1366 |
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囟
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1367 |
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因
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1368 |
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囡
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1369 |
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团
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1370 |
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団
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囤
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1372 |
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囧
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囬
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园
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囯
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1377 |
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困
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1378 |
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囱
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1379 |
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囲
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1380 |
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図
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1381 |
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围
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1382 |
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囵
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1383 |
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囷
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囹
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固
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国
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图
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囿
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圃
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圄
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圆
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圈
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在
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圬
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圮
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圯
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地
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圳
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圹
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场
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圻
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圾
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址
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坂
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均
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坊
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坎
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坐
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坑
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块
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坚
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坛
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垃
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垄
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1452 |
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垅
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垆
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1454 |
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型
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1455 |
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垌
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1457 |
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垒
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1458 |
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垓
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1459 |
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垕
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1460 |
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垚
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1461 |
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1462 |
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1463 |
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垟
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1464 |
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1465 |
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1466 |
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1467 |
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1468 |
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1469 |
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1470 |
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垸
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埂
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堽
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嬗
|
1793 |
+
嬛
|
1794 |
+
嬜
|
1795 |
+
嬢
|
1796 |
+
嬲
|
1797 |
+
嬴
|
1798 |
+
嬷
|
1799 |
+
嬾
|
1800 |
+
嬿
|
1801 |
+
孀
|
1802 |
+
子
|
1803 |
+
孑
|
1804 |
+
孒
|
1805 |
+
孓
|
1806 |
+
孔
|
1807 |
+
孕
|
1808 |
+
孖
|
1809 |
+
字
|
1810 |
+
存
|
1811 |
+
孙
|
1812 |
+
孚
|
1813 |
+
孛
|
1814 |
+
孜
|
1815 |
+
孝
|
1816 |
+
孟
|
1817 |
+
孢
|
1818 |
+
季
|
1819 |
+
孤
|
1820 |
+
孥
|
1821 |
+
学
|
1822 |
+
孩
|
1823 |
+
孪
|
1824 |
+
孬
|
1825 |
+
孰
|
1826 |
+
孱
|
1827 |
+
孳
|
1828 |
+
孵
|
1829 |
+
孺
|
1830 |
+
孽
|
1831 |
+
宀
|
1832 |
+
宁
|
1833 |
+
它
|
1834 |
+
宄
|
1835 |
+
宅
|
1836 |
+
宇
|
1837 |
+
守
|
1838 |
+
安
|
1839 |
+
宋
|
1840 |
+
完
|
1841 |
+
宍
|
1842 |
+
宏
|
1843 |
+
宓
|
1844 |
+
宕
|
1845 |
+
宗
|
1846 |
+
官
|
1847 |
+
宙
|
1848 |
+
定
|
1849 |
+
宛
|
1850 |
+
宜
|
1851 |
+
宝
|
1852 |
+
实
|
1853 |
+
実
|
1854 |
+
宠
|
1855 |
+
审
|
1856 |
+
客
|
1857 |
+
宣
|
1858 |
+
室
|
1859 |
+
宥
|
1860 |
+
宦
|
1861 |
+
宪
|
1862 |
+
宫
|
1863 |
+
宬
|
1864 |
+
宰
|
1865 |
+
害
|
1866 |
+
宴
|
1867 |
+
宵
|
1868 |
+
家
|
1869 |
+
宸
|
1870 |
+
容
|
1871 |
+
宽
|
1872 |
+
宾
|
1873 |
+
宿
|
1874 |
+
寀
|
1875 |
+
寂
|
1876 |
+
寃
|
1877 |
+
寄
|
1878 |
+
寅
|
1879 |
+
密
|
1880 |
+
寇
|
1881 |
+
富
|
1882 |
+
寐
|
1883 |
+
寒
|
1884 |
+
寓
|
1885 |
+
寔
|
1886 |
+
寘
|
1887 |
+
寛
|
1888 |
+
寝
|
1889 |
+
寞
|
1890 |
+
察
|
1891 |
+
寡
|
1892 |
+
寤
|
1893 |
+
寥
|
1894 |
+
寨
|
1895 |
+
寮
|
1896 |
+
寯
|
1897 |
+
寰
|
1898 |
+
寳
|
1899 |
+
寸
|
1900 |
+
对
|
1901 |
+
寺
|
1902 |
+
寻
|
1903 |
+
导
|
1904 |
+
対
|
1905 |
+
寿
|
1906 |
+
封
|
1907 |
+
専
|
1908 |
+
射
|
1909 |
+
尅
|
1910 |
+
将
|
1911 |
+
尉
|
1912 |
+
尊
|
1913 |
+
對
|
1914 |
+
小
|
1915 |
+
尐
|
1916 |
+
少
|
1917 |
+
尒
|
1918 |
+
尓
|
1919 |
+
尔
|
1920 |
+
尕
|
1921 |
+
尖
|
1922 |
+
尘
|
1923 |
+
尙
|
1924 |
+
尚
|
1925 |
+
尛
|
1926 |
+
尜
|
1927 |
+
尝
|
1928 |
+
尢
|
1929 |
+
尤
|
1930 |
+
尧
|
1931 |
+
尨
|
1932 |
+
尪
|
1933 |
+
尬
|
1934 |
+
就
|
1935 |
+
尴
|
1936 |
+
尸
|
1937 |
+
尹
|
1938 |
+
尺
|
1939 |
+
尻
|
1940 |
+
尼
|
1941 |
+
尽
|
1942 |
+
尾
|
1943 |
+
尿
|
1944 |
+
局
|
1945 |
+
屁
|
1946 |
+
层
|
1947 |
+
屃
|
1948 |
+
屄
|
1949 |
+
居
|
1950 |
+
屈
|
1951 |
+
屉
|
1952 |
+
届
|
1953 |
+
屋
|
1954 |
+
屌
|
1955 |
+
屍
|
1956 |
+
屎
|
1957 |
+
屏
|
1958 |
+
屐
|
1959 |
+
屑
|
1960 |
+
展
|
1961 |
+
屙
|
1962 |
+
属
|
1963 |
+
屠
|
1964 |
+
屡
|
1965 |
+
屣
|
1966 |
+
履
|
1967 |
+
屦
|
1968 |
+
屮
|
1969 |
+
屯
|
1970 |
+
山
|
1971 |
+
屹
|
1972 |
+
屺
|
1973 |
+
屾
|
1974 |
+
屿
|
1975 |
+
岀
|
1976 |
+
岁
|
1977 |
+
岂
|
1978 |
+
岈
|
1979 |
+
岌
|
1980 |
+
岐
|
1981 |
+
岑
|
1982 |
+
岔
|
1983 |
+
岕
|
1984 |
+
岖
|
1985 |
+
岗
|
1986 |
+
岘
|
1987 |
+
岙
|
1988 |
+
岚
|
1989 |
+
岛
|
1990 |
+
岜
|
1991 |
+
岞
|
1992 |
+
岢
|
1993 |
+
岣
|
1994 |
+
岩
|
1995 |
+
岫
|
1996 |
+
岬
|
1997 |
+
岭
|
1998 |
+
岱
|
1999 |
+
岳
|
2000 |
+
岵
|
2001 |
+
岷
|
2002 |
+
岸
|
2003 |
+
岺
|
2004 |
+
岽
|
2005 |
+
岿
|
2006 |
+
峁
|
2007 |
+
峄
|
2008 |
+
峇
|
2009 |
+
峋
|
2010 |
+
峒
|
2011 |
+
峕
|
2012 |
+
峙
|
2013 |
+
峠
|
2014 |
+
峡
|
2015 |
+
峣
|
2016 |
+
峤
|
2017 |
+
峥
|
2018 |
+
峦
|
2019 |
+
峨
|
2020 |
+
峩
|
2021 |
+
峪
|
2022 |
+
峭
|
2023 |
+
峯
|
2024 |
+
峰
|
2025 |
+
峻
|
2026 |
+
崀
|
2027 |
+
崁
|
2028 |
+
崂
|
2029 |
+
崃
|
2030 |
+
崄
|
2031 |
+
崆
|
2032 |
+
崇
|
2033 |
+
崎
|
2034 |
+
崐
|
2035 |
+
崑
|
2036 |
+
崔
|
2037 |
+
崖
|
2038 |
+
崚
|
2039 |
+
崛
|
2040 |
+
崞
|
2041 |
+
崟
|
2042 |
+
崤
|
2043 |
+
崦
|
2044 |
+
崧
|
2045 |
+
崩
|
2046 |
+
崭
|
2047 |
+
崮
|
2048 |
+
崴
|
2049 |
+
崽
|
2050 |
+
崾
|
2051 |
+
嵇
|
2052 |
+
嵊
|
2053 |
+
嵋
|
2054 |
+
嵌
|
2055 |
+
嵎
|
2056 |
+
嵖
|
2057 |
+
嵗
|
2058 |
+
嵘
|
2059 |
+
嵚
|
2060 |
+
嵛
|
2061 |
+
嵝
|
2062 |
+
嵩
|
2063 |
+
嵬
|
2064 |
+
嵯
|
2065 |
+
嵴
|
2066 |
+
嶂
|
2067 |
+
嶋
|
2068 |
+
嶙
|
2069 |
+
嶝
|
2070 |
+
嶲
|
2071 |
+
嶷
|
2072 |
+
巂
|
2073 |
+
巅
|
2074 |
+
巇
|
2075 |
+
巉
|
2076 |
+
巍
|
2077 |
+
巎
|
2078 |
+
巘
|
2079 |
+
巜
|
2080 |
+
川
|
2081 |
+
州
|
2082 |
+
巡
|
2083 |
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巢
|
2084 |
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巣
|
2085 |
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工
|
2086 |
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左
|
2087 |
+
巧
|
2088 |
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巨
|
2089 |
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巩
|
2090 |
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巫
|
2091 |
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差
|
2092 |
+
巯
|
2093 |
+
己
|
2094 |
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已
|
2095 |
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巳
|
2096 |
+
巴
|
2097 |
+
巷
|
2098 |
+
巻
|
2099 |
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巽
|
2100 |
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巾
|
2101 |
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巿
|
2102 |
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币
|
2103 |
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市
|
2104 |
+
布
|
2105 |
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帅
|
2106 |
+
帆
|
2107 |
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师
|
2108 |
+
希
|
2109 |
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帏
|
2110 |
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帐
|
2111 |
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帑
|
2112 |
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帔
|
2113 |
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帕
|
2114 |
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帖
|
2115 |
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帘
|
2116 |
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帙
|
2117 |
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帚
|
2118 |
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帛
|
2119 |
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帜
|
2120 |
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帝
|
2121 |
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带
|
2122 |
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帧
|
2123 |
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席
|
2124 |
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帮
|
2125 |
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帯
|
2126 |
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帰
|
2127 |
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帷
|
2128 |
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常
|
2129 |
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帻
|
2130 |
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帼
|
2131 |
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帽
|
2132 |
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幂
|
2133 |
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幄
|
2134 |
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幅
|
2135 |
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幌
|
2136 |
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幔
|
2137 |
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幕
|
2138 |
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幛
|
2139 |
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幞
|
2140 |
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幡
|
2141 |
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幢
|
2142 |
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干
|
2143 |
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平
|
2144 |
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年
|
2145 |
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幵
|
2146 |
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并
|
2147 |
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幷
|
2148 |
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幸
|
2149 |
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幺
|
2150 |
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幻
|
2151 |
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幼
|
2152 |
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幽
|
2153 |
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广
|
2154 |
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庀
|
2155 |
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庁
|
2156 |
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広
|
2157 |
+
庄
|
2158 |
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庆
|
2159 |
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庇
|
2160 |
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床
|
2161 |
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庋
|
2162 |
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序
|
2163 |
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庐
|
2164 |
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庑
|
2165 |
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库
|
2166 |
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应
|
2167 |
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底
|
2168 |
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庖
|
2169 |
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店
|
2170 |
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庙
|
2171 |
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庚
|
2172 |
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府
|
2173 |
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庞
|
2174 |
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废
|
2175 |
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庠
|
2176 |
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庤
|
2177 |
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庥
|
2178 |
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度
|
2179 |
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座
|
2180 |
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庭
|
2181 |
+
庵
|
2182 |
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庶
|
2183 |
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康
|
2184 |
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庸
|
2185 |
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庹
|
2186 |
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庾
|
2187 |
+
廆
|
2188 |
+
廉
|
2189 |
+
廊
|
2190 |
+
廋
|
2191 |
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廌
|
2192 |
+
廑
|
2193 |
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廒
|
2194 |
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廓
|
2195 |
+
廕
|
2196 |
+
廖
|
2197 |
+
廙
|
2198 |
+
廛
|
2199 |
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廞
|
2200 |
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廨
|
2201 |
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廪
|
2202 |
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廯
|
2203 |
+
延
|
2204 |
+
廷
|
2205 |
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建
|
2206 |
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廻
|
2207 |
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廼
|
2208 |
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廾
|
2209 |
+
廿
|
2210 |
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开
|
2211 |
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弁
|
2212 |
+
异
|
2213 |
+
弃
|
2214 |
+
弄
|
2215 |
+
弇
|
2216 |
+
弈
|
2217 |
+
弊
|
2218 |
+
弋
|
2219 |
+
式
|
2220 |
+
弐
|
2221 |
+
弑
|
2222 |
+
弓
|
2223 |
+
引
|
2224 |
+
弗
|
2225 |
+
弘
|
2226 |
+
弛
|
2227 |
+
弟
|
2228 |
+
张
|
2229 |
+
弢
|
2230 |
+
弥
|
2231 |
+
弦
|
2232 |
+
弧
|
2233 |
+
弩
|
2234 |
+
弭
|
2235 |
+
弯
|
2236 |
+
弱
|
2237 |
+
弹
|
2238 |
+
强
|
2239 |
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弼
|
2240 |
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弾
|
2241 |
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彀
|
2242 |
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归
|
2243 |
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当
|
2244 |
+
录
|
2245 |
+
彖
|
2246 |
+
彗
|
2247 |
+
彘
|
2248 |
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彝
|
2249 |
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彟
|
2250 |
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彡
|
2251 |
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形
|
2252 |
+
彤
|
2253 |
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彦
|
2254 |
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彧
|
2255 |
+
彩
|
2256 |
+
彪
|
2257 |
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彬
|
2258 |
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彭
|
2259 |
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彰
|
2260 |
+
影
|
2261 |
+
彳
|
2262 |
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彵
|
2263 |
+
彷
|
2264 |
+
役
|
2265 |
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彻
|
2266 |
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彼
|
2267 |
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往
|
2268 |
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征
|
2269 |
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徂
|
2270 |
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径
|
2271 |
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待
|
2272 |
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徇
|
2273 |
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很
|
2274 |
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徉
|
2275 |
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徊
|
2276 |
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律
|
2277 |
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徐
|
2278 |
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徒
|
2279 |
+
従
|
2280 |
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徕
|
2281 |
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得
|
2282 |
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徘
|
2283 |
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徙
|
2284 |
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徜
|
2285 |
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御
|
2286 |
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徧
|
2287 |
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徨
|
2288 |
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循
|
2289 |
+
徭
|
2290 |
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微
|
2291 |
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徳
|
2292 |
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徴
|
2293 |
+
徵
|
2294 |
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德
|
2295 |
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徼
|
2296 |
+
徽
|
2297 |
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心
|
2298 |
+
忄
|
2299 |
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必
|
2300 |
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忆
|
2301 |
+
忉
|
2302 |
+
忌
|
2303 |
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忍
|
2304 |
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忏
|
2305 |
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忐
|
2306 |
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忑
|
2307 |
+
忒
|
2308 |
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忖
|
2309 |
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志
|
2310 |
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忘
|
2311 |
+
忙
|
2312 |
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応
|
2313 |
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忝
|
2314 |
+
忞
|
2315 |
+
忠
|
2316 |
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忡
|
2317 |
+
忤
|
2318 |
+
忧
|
2319 |
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忪
|
2320 |
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快
|
2321 |
+
忭
|
2322 |
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忱
|
2323 |
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念
|
2324 |
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忸
|
2325 |
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忻
|
2326 |
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忽
|
2327 |
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忾
|
2328 |
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忿
|
2329 |
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怀
|
2330 |
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态
|
2331 |
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怂
|
2332 |
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怃
|
2333 |
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怄
|
2334 |
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怅
|
2335 |
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怆
|
2336 |
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怍
|
2337 |
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怎
|
2338 |
+
怏
|
2339 |
+
怒
|
2340 |
+
怔
|
2341 |
+
怕
|
2342 |
+
怖
|
2343 |
+
怙
|
2344 |
+
怛
|
2345 |
+
怜
|
2346 |
+
思
|
2347 |
+
怠
|
2348 |
+
怡
|
2349 |
+
急
|
2350 |
+
怦
|
2351 |
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性
|
2352 |
+
怨
|
2353 |
+
怩
|
2354 |
+
怪
|
2355 |
+
怫
|
2356 |
+
怯
|
2357 |
+
怱
|
2358 |
+
怳
|
2359 |
+
怵
|
2360 |
+
怹
|
2361 |
+
总
|
2362 |
+
怼
|
2363 |
+
怿
|
2364 |
+
恁
|
2365 |
+
恂
|
2366 |
+
恃
|
2367 |
+
恋
|
2368 |
+
恍
|
2369 |
+
恏
|
2370 |
+
恐
|
2371 |
+
恒
|
2372 |
+
恕
|
2373 |
+
恙
|
2374 |
+
恚
|
2375 |
+
恠
|
2376 |
+
恢
|
2377 |
+
恣
|
2378 |
+
恤
|
2379 |
+
恨
|
2380 |
+
恩
|
2381 |
+
恪
|
2382 |
+
恫
|
2383 |
+
恬
|
2384 |
+
恭
|
2385 |
+
息
|
2386 |
+
恰
|
2387 |
+
恳
|
2388 |
+
恵
|
2389 |
+
恶
|
2390 |
+
恸
|
2391 |
+
恹
|
2392 |
+
恺
|
2393 |
+
恻
|
2394 |
+
恼
|
2395 |
+
恽
|
2396 |
+
恿
|
2397 |
+
悃
|
2398 |
+
悄
|
2399 |
+
悉
|
2400 |
+
悌
|
2401 |
+
悍
|
2402 |
+
悒
|
2403 |
+
悔
|
2404 |
+
悖
|
2405 |
+
悚
|
2406 |
+
悛
|
2407 |
+
悝
|
2408 |
+
悟
|
2409 |
+
悠
|
2410 |
+
患
|
2411 |
+
悦
|
2412 |
+
您
|
2413 |
+
悩
|
2414 |
+
悪
|
2415 |
+
悫
|
2416 |
+
悬
|
2417 |
+
悭
|
2418 |
+
悯
|
2419 |
+
悰
|
2420 |
+
悱
|
2421 |
+
悲
|
2422 |
+
悳
|
2423 |
+
悴
|
2424 |
+
悸
|
2425 |
+
悻
|
2426 |
+
悼
|
2427 |
+
情
|
2428 |
+
惆
|
2429 |
+
惇
|
2430 |
+
惊
|
2431 |
+
惋
|
2432 |
+
惑
|
2433 |
+
惔
|
2434 |
+
惕
|
2435 |
+
惘
|
2436 |
+
惚
|
2437 |
+
惛
|
2438 |
+
惜
|
2439 |
+
惝
|
2440 |
+
惟
|
2441 |
+
惠
|
2442 |
+
惢
|
2443 |
+
惣
|
2444 |
+
惦
|
2445 |
+
惧
|
2446 |
+
惨
|
2447 |
+
惩
|
2448 |
+
惪
|
2449 |
+
惫
|
2450 |
+
惬
|
2451 |
+
惭
|
2452 |
+
惮
|
2453 |
+
惯
|
2454 |
+
惰
|
2455 |
+
想
|
2456 |
+
惴
|
2457 |
+
惶
|
2458 |
+
惹
|
2459 |
+
惺
|
2460 |
+
愀
|
2461 |
+
愁
|
2462 |
+
愆
|
2463 |
+
愈
|
2464 |
+
愉
|
2465 |
+
愍
|
2466 |
+
愎
|
2467 |
+
意
|
2468 |
+
愔
|
2469 |
+
愕
|
2470 |
+
愚
|
2471 |
+
感
|
2472 |
+
愠
|
2473 |
+
愣
|
2474 |
+
愤
|
2475 |
+
愦
|
2476 |
+
愧
|
2477 |
+
愫
|
2478 |
+
愬
|
2479 |
+
愰
|
2480 |
+
愽
|
2481 |
+
愿
|
2482 |
+
慆
|
2483 |
+
慈
|
2484 |
+
慊
|
2485 |
+
慌
|
2486 |
+
慎
|
2487 |
+
慑
|
2488 |
+
慕
|
2489 |
+
慜
|
2490 |
+
慝
|
2491 |
+
慢
|
2492 |
+
慥
|
2493 |
+
慧
|
2494 |
+
慨
|
2495 |
+
慰
|
2496 |
+
慵
|
2497 |
+
慷
|
2498 |
+
慾
|
2499 |
+
憋
|
2500 |
+
憍
|
2501 |
+
憎
|
2502 |
+
憔
|
2503 |
+
憙
|
2504 |
+
憧
|
2505 |
+
憨
|
2506 |
+
憩
|
2507 |
+
憬
|
2508 |
+
憷
|
2509 |
+
憺
|
2510 |
+
憾
|
2511 |
+
懂
|
2512 |
+
懈
|
2513 |
+
懊
|
2514 |
+
懋
|
2515 |
+
懐
|
2516 |
+
懑
|
2517 |
+
懒
|
2518 |
+
懔
|
2519 |
+
懦
|
2520 |
+
懮
|
2521 |
+
懵
|
2522 |
+
懽
|
2523 |
+
懿
|
2524 |
+
戆
|
2525 |
+
戈
|
2526 |
+
戊
|
2527 |
+
戋
|
2528 |
+
戌
|
2529 |
+
戍
|
2530 |
+
戎
|
2531 |
+
戏
|
2532 |
+
成
|
2533 |
+
我
|
2534 |
+
戒
|
2535 |
+
戓
|
2536 |
+
戕
|
2537 |
+
或
|
2538 |
+
戗
|
2539 |
+
战
|
2540 |
+
戚
|
2541 |
+
戛
|
2542 |
+
戟
|
2543 |
+
戡
|
2544 |
+
戢
|
2545 |
+
戥
|
2546 |
+
戦
|
2547 |
+
截
|
2548 |
+
戬
|
2549 |
+
戮
|
2550 |
+
戯
|
2551 |
+
戳
|
2552 |
+
戴
|
2553 |
+
户
|
2554 |
+
戻
|
2555 |
+
戽
|
2556 |
+
戾
|
2557 |
+
房
|
2558 |
+
所
|
2559 |
+
扁
|
2560 |
+
扃
|
2561 |
+
扆
|
2562 |
+
扇
|
2563 |
+
扈
|
2564 |
+
扉
|
2565 |
+
手
|
2566 |
+
扌
|
2567 |
+
才
|
2568 |
+
扎
|
2569 |
+
扑
|
2570 |
+
扒
|
2571 |
+
打
|
2572 |
+
扔
|
2573 |
+
托
|
2574 |
+
扛
|
2575 |
+
扞
|
2576 |
+
扣
|
2577 |
+
扥
|
2578 |
+
扦
|
2579 |
+
执
|
2580 |
+
扩
|
2581 |
+
扪
|
2582 |
+
扫
|
2583 |
+
扬
|
2584 |
+
扭
|
2585 |
+
扮
|
2586 |
+
扯
|
2587 |
+
扰
|
2588 |
+
扳
|
2589 |
+
扶
|
2590 |
+
批
|
2591 |
+
扼
|
2592 |
+
扽
|
2593 |
+
找
|
2594 |
+
承
|
2595 |
+
技
|
2596 |
+
抃
|
2597 |
+
抄
|
2598 |
+
抉
|
2599 |
+
把
|
2600 |
+
抑
|
2601 |
+
抒
|
2602 |
+
抓
|
2603 |
+
抔
|
2604 |
+
投
|
2605 |
+
抖
|
2606 |
+
抗
|
2607 |
+
折
|
2608 |
+
抚
|
2609 |
+
抛
|
2610 |
+
抜
|
2611 |
+
抟
|
2612 |
+
抠
|
2613 |
+
抡
|
2614 |
+
抢
|
2615 |
+
护
|
2616 |
+
报
|
2617 |
+
抧
|
2618 |
+
抨
|
2619 |
+
披
|
2620 |
+
抬
|
2621 |
+
抱
|
2622 |
+
抳
|
2623 |
+
抵
|
2624 |
+
抹
|
2625 |
+
抺
|
2626 |
+
抻
|
2627 |
+
押
|
2628 |
+
抽
|
2629 |
+
抿
|
2630 |
+
拂
|
2631 |
+
拄
|
2632 |
+
担
|
2633 |
+
拆
|
2634 |
+
拇
|
2635 |
+
拈
|
2636 |
+
拉
|
2637 |
+
拊
|
2638 |
+
拌
|
2639 |
+
拍
|
2640 |
+
拎
|
2641 |
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拏
|
2642 |
+
拐
|
2643 |
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拒
|
2644 |
+
拓
|
2645 |
+
拔
|
2646 |
+
拖
|
2647 |
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拗
|
2648 |
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拘
|
2649 |
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拙
|
2650 |
+
招
|
2651 |
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拜
|
2652 |
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拟
|
2653 |
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拢
|
2654 |
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拣
|
2655 |
+
拥
|
2656 |
+
拦
|
2657 |
+
拧
|
2658 |
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拨
|
2659 |
+
择
|
2660 |
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括
|
2661 |
+
拭
|
2662 |
+
拮
|
2663 |
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拯
|
2664 |
+
拱
|
2665 |
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拳
|
2666 |
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拴
|
2667 |
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拶
|
2668 |
+
拷
|
2669 |
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拼
|
2670 |
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拽
|
2671 |
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拾
|
2672 |
+
拿
|
2673 |
+
挀
|
2674 |
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持
|
2675 |
+
挂
|
2676 |
+
指
|
2677 |
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挈
|
2678 |
+
按
|
2679 |
+
挎
|
2680 |
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挑
|
2681 |
+
挒
|
2682 |
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挖
|
2683 |
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挚
|
2684 |
+
挛
|
2685 |
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挝
|
2686 |
+
挞
|
2687 |
+
挟
|
2688 |
+
挠
|
2689 |
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挡
|
2690 |
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挢
|
2691 |
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挣
|
2692 |
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挤
|
2693 |
+
挥
|
2694 |
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挨
|
2695 |
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挪
|
2696 |
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挫
|
2697 |
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振
|
2698 |
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挲
|
2699 |
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挹
|
2700 |
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挺
|
2701 |
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挻
|
2702 |
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挼
|
2703 |
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挽
|
2704 |
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捂
|
2705 |
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捃
|
2706 |
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捅
|
2707 |
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捆
|
2708 |
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捉
|
2709 |
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捋
|
2710 |
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捌
|
2711 |
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捍
|
2712 |
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捎
|
2713 |
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捏
|
2714 |
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捐
|
2715 |
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捕
|
2716 |
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捜
|
2717 |
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捞
|
2718 |
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损
|
2719 |
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捡
|
2720 |
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换
|
2721 |
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捣
|
2722 |
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捧
|
2723 |
+
捩
|
2724 |
+
捭
|
2725 |
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据
|
2726 |
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捯
|
2727 |
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捱
|
2728 |
+
捶
|
2729 |
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捷
|
2730 |
+
捺
|
2731 |
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捻
|
2732 |
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捽
|
2733 |
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掀
|
2734 |
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掂
|
2735 |
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掇
|
2736 |
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授
|
2737 |
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掉
|
2738 |
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掊
|
2739 |
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掌
|
2740 |
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掎
|
2741 |
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掏
|
2742 |
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掐
|
2743 |
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排
|
2744 |
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掖
|
2745 |
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掘
|
2746 |
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掞
|
2747 |
+
掠
|
2748 |
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探
|
2749 |
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掣
|
2750 |
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掤
|
2751 |
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接
|
2752 |
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控
|
2753 |
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推
|
2754 |
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掩
|
2755 |
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措
|
2756 |
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掬
|
2757 |
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掭
|
2758 |
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掮
|
2759 |
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掰
|
2760 |
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掲
|
2761 |
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掳
|
2762 |
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掴
|
2763 |
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掷
|
2764 |
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掸
|
2765 |
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掺
|
2766 |
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掼
|
2767 |
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掾
|
2768 |
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揄
|
2769 |
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揆
|
2770 |
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揉
|
2771 |
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揍
|
2772 |
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描
|
2773 |
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提
|
2774 |
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插
|
2775 |
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揖
|
2776 |
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揠
|
2777 |
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握
|
2778 |
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揣
|
2779 |
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揩
|
2780 |
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揪
|
2781 |
+
揭
|
2782 |
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揲
|
2783 |
+
援
|
2784 |
+
揵
|
2785 |
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揶
|
2786 |
+
揸
|
2787 |
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揺
|
2788 |
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揽
|
2789 |
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揾
|
2790 |
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揿
|
2791 |
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搀
|
2792 |
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搁
|
2793 |
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搂
|
2794 |
+
搅
|
2795 |
+
搋
|
2796 |
+
搏
|
2797 |
+
搐
|
2798 |
+
搓
|
2799 |
+
搔
|
2800 |
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搜
|
2801 |
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搞
|
2802 |
+
搠
|
2803 |
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搡
|
2804 |
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搢
|
2805 |
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搥
|
2806 |
+
搦
|
2807 |
+
搧
|
2808 |
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搨
|
2809 |
+
搪
|
2810 |
+
搬
|
2811 |
+
搭
|
2812 |
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搴
|
2813 |
+
搵
|
2814 |
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携
|
2815 |
+
搽
|
2816 |
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搿
|
2817 |
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摁
|
2818 |
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摄
|
2819 |
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摅
|
2820 |
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摆
|
2821 |
+
摇
|
2822 |
+
摈
|
2823 |
+
摊
|
2824 |
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摒
|
2825 |
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摔
|
2826 |
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摘
|
2827 |
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摛
|
2828 |
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摞
|
2829 |
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摧
|
2830 |
+
摩
|
2831 |
+
摭
|
2832 |
+
摸
|
2833 |
+
摹
|
2834 |
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摺
|
2835 |
+
摽
|
2836 |
+
撂
|
2837 |
+
撃
|
2838 |
+
撄
|
2839 |
+
撅
|
2840 |
+
撇
|
2841 |
+
撑
|
2842 |
+
撒
|
2843 |
+
撕
|
2844 |
+
撘
|
2845 |
+
撙
|
2846 |
+
撝
|
2847 |
+
撞
|
2848 |
+
撤
|
2849 |
+
撩
|
2850 |
+
撬
|
2851 |
+
播
|
2852 |
+
撮
|
2853 |
+
撰
|
2854 |
+
撵
|
2855 |
+
撷
|
2856 |
+
撸
|
2857 |
+
撺
|
2858 |
+
撼
|
2859 |
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擀
|
2860 |
+
擂
|
2861 |
+
擅
|
2862 |
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操
|
2863 |
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擎
|
2864 |
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擒
|
2865 |
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擗
|
2866 |
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擘
|
2867 |
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擞
|
2868 |
+
擢
|
2869 |
+
擤
|
2870 |
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擦
|
2871 |
+
擫
|
2872 |
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擿
|
2873 |
+
攀
|
2874 |
+
攒
|
2875 |
+
攘
|
2876 |
+
攞
|
2877 |
+
攥
|
2878 |
+
攫
|
2879 |
+
支
|
2880 |
+
攲
|
2881 |
+
攴
|
2882 |
+
攵
|
2883 |
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收
|
2884 |
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攸
|
2885 |
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改
|
2886 |
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攻
|
2887 |
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攽
|
2888 |
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放
|
2889 |
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政
|
2890 |
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故
|
2891 |
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效
|
2892 |
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敉
|
2893 |
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敌
|
2894 |
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敎
|
2895 |
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敏
|
2896 |
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救
|
2897 |
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敔
|
2898 |
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敕
|
2899 |
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敖
|
2900 |
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教
|
2901 |
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敚
|
2902 |
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敛
|
2903 |
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敝
|
2904 |
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敞
|
2905 |
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敢
|
2906 |
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散
|
2907 |
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敦
|
2908 |
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敫
|
2909 |
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敬
|
2910 |
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数
|
2911 |
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敲
|
2912 |
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整
|
2913 |
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敷
|
2914 |
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敻
|
2915 |
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文
|
2916 |
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斉
|
2917 |
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斋
|
2918 |
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斌
|
2919 |
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斎
|
2920 |
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斐
|
2921 |
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斑
|
2922 |
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斓
|
2923 |
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斗
|
2924 |
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料
|
2925 |
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斛
|
2926 |
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斜
|
2927 |
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斝
|
2928 |
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斟
|
2929 |
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斡
|
2930 |
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斤
|
2931 |
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斥
|
2932 |
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斧
|
2933 |
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斩
|
2934 |
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斫
|
2935 |
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断
|
2936 |
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斯
|
2937 |
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新
|
2938 |
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斲
|
2939 |
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斶
|
2940 |
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方
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2941 |
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於
|
2942 |
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施
|
2943 |
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旁
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2944 |
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旃
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2945 |
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旄
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2946 |
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旅
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2947 |
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旆
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2948 |
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旋
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2949 |
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旌
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2950 |
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旎
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2951 |
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族
|
2952 |
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旒
|
2953 |
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旖
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2954 |
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旗
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2955 |
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旛
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2956 |
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无
|
2957 |
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既
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2958 |
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旣
|
2959 |
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日
|
2960 |
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旦
|
2961 |
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旧
|
2962 |
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旨
|
2963 |
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早
|
2964 |
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旬
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2965 |
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旭
|
2966 |
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旮
|
2967 |
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旯
|
2968 |
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旰
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2969 |
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旱
|
2970 |
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旳
|
2971 |
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旴
|
2972 |
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时
|
2973 |
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旷
|
2974 |
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旸
|
2975 |
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旺
|
2976 |
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旻
|
2977 |
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旼
|
2978 |
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昀
|
2979 |
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昂
|
2980 |
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昃
|
2981 |
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昆
|
2982 |
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昇
|
2983 |
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昉
|
2984 |
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昊
|
2985 |
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昌
|
2986 |
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明
|
2987 |
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昏
|
2988 |
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易
|
2989 |
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昔
|
2990 |
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昕
|
2991 |
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昙
|
2992 |
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昚
|
2993 |
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昝
|
2994 |
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昞
|
2995 |
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星
|
2996 |
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映
|
2997 |
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春
|
2998 |
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昧
|
2999 |
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昨
|
3000 |
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昪
|
3001 |
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昫
|
3002 |
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昭
|
3003 |
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是
|
3004 |
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昰
|
3005 |
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昱
|
3006 |
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昳
|
3007 |
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昴
|
3008 |
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昵
|
3009 |
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昶
|
3010 |
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昺
|
3011 |
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昼
|
3012 |
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显
|
3013 |
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晁
|
3014 |
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時
|
3015 |
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晃
|
3016 |
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晄
|
3017 |
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晋
|
3018 |
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晌
|
3019 |
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晏
|
3020 |
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晒
|
3021 |
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晓
|
3022 |
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晔
|
3023 |
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晕
|
3024 |
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晖
|
3025 |
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晗
|
3026 |
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晙
|
3027 |
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晚
|
3028 |
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晞
|
3029 |
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晟
|
3030 |
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晡
|
3031 |
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晢
|
3032 |
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晤
|
3033 |
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晦
|
3034 |
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晧
|
3035 |
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晨
|
3036 |
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晩
|
3037 |
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晬
|
3038 |
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普
|
3039 |
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景
|
3040 |
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晰
|
3041 |
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晳
|
3042 |
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晴
|
3043 |
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晶
|
3044 |
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晷
|
3045 |
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晸
|
3046 |
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智
|
3047 |
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晻
|
3048 |
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晾
|
3049 |
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暁
|
3050 |
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暂
|
3051 |
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暄
|
3052 |
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暇
|
3053 |
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暌
|
3054 |
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暍
|
3055 |
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暎
|
3056 |
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暐
|
3057 |
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暑
|
3058 |
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暕
|
3059 |
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暖
|
3060 |
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暗
|
3061 |
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暝
|
3062 |
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暠
|
3063 |
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暧
|
3064 |
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暨
|
3065 |
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暮
|
3066 |
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暴
|
3067 |
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暸
|
3068 |
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暹
|
3069 |
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暻
|
3070 |
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暾
|
3071 |
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曈
|
3072 |
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曌
|
3073 |
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曕
|
3074 |
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曙
|
3075 |
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曛
|
3076 |
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曜
|
3077 |
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曝
|
3078 |
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曦
|
3079 |
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曩
|
3080 |
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曰
|
3081 |
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曱
|
3082 |
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曲
|
3083 |
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曳
|
3084 |
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更
|
3085 |
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曷
|
3086 |
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曹
|
3087 |
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曺
|
3088 |
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曼
|
3089 |
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曽
|
3090 |
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曾
|
3091 |
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替
|
3092 |
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最
|
3093 |
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會
|
3094 |
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朅
|
3095 |
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月
|
3096 |
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有
|
3097 |
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朊
|
3098 |
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朋
|
3099 |
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服
|
3100 |
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朏
|
3101 |
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朐
|
3102 |
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朓
|
3103 |
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朔
|
3104 |
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朕
|
3105 |
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朗
|
3106 |
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望
|
3107 |
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朝
|
3108 |
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期
|
3109 |
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朥
|
3110 |
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朦
|
3111 |
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木
|
3112 |
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未
|
3113 |
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末
|
3114 |
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本
|
3115 |
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札
|
3116 |
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术
|
3117 |
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朱
|
3118 |
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朲
|
3119 |
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朴
|
3120 |
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朵
|
3121 |
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机
|
3122 |
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朽
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3123 |
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朿
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3124 |
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杀
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3125 |
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杂
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3126 |
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权
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3127 |
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杆
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3128 |
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杈
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3129 |
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杉
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3130 |
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杌
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3131 |
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李
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3132 |
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杏
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3133 |
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材
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3134 |
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村
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3135 |
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杓
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3136 |
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杖
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3137 |
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杜
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3138 |
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杞
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3139 |
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束
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3140 |
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杠
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3141 |
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条
|
3142 |
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来
|
3143 |
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杧
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3144 |
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杨
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3145 |
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杪
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3146 |
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杭
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3147 |
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杮
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3148 |
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杯
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3149 |
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杰
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3150 |
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杲
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3151 |
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杳
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3152 |
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杵
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3153 |
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杷
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3154 |
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杼
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3155 |
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松
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3156 |
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板
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3157 |
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极
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3158 |
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构
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3159 |
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枇
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3160 |
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枉
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3161 |
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枋
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3162 |
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枏
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3163 |
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析
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3164 |
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枓
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3165 |
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枕
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3166 |
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林
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3167 |
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枘
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3168 |
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枚
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3169 |
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果
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3170 |
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枝
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3171 |
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枞
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3172 |
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枟
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3173 |
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枠
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3174 |
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枢
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3175 |
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枣
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3176 |
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枥
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3177 |
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枧
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3178 |
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枨
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3179 |
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枪
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3180 |
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枫
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3181 |
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枭
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3182 |
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枯
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3183 |
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枰
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3184 |
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枱
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3185 |
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枳
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3186 |
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枵
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3187 |
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架
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3188 |
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枷
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3189 |
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枸
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3190 |
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枹
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3191 |
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柁
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3192 |
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柃
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3193 |
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柄
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3194 |
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柊
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3195 |
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柏
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3196 |
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某
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3197 |
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柑
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3198 |
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柒
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3199 |
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染
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3200 |
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柔
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3201 |
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柘
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3202 |
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柙
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3203 |
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柚
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3204 |
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柜
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3205 |
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柝
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3206 |
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柞
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3207 |
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柟
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3208 |
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柠
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3209 |
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柢
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3210 |
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查
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3211 |
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柩
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3212 |
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柬
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3213 |
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柯
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3214 |
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柰
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3215 |
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柱
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3216 |
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柳
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3217 |
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柴
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3218 |
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柷
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3219 |
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査
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3220 |
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柽
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3221 |
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柾
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3222 |
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柿
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3223 |
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栀
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3224 |
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栃
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3225 |
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栄
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3226 |
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栅
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3227 |
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标
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3228 |
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栈
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3229 |
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栉
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3230 |
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栊
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3231 |
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栋
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3232 |
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栌
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3233 |
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栎
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3234 |
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栏
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3235 |
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树
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3236 |
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栒
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3237 |
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栓
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3238 |
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栖
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3239 |
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栗
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3240 |
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栝
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3241 |
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栞
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3242 |
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栟
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3243 |
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校
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3244 |
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栢
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3245 |
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栩
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3246 |
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株
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3247 |
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栱
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3248 |
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栲
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3249 |
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栳
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3250 |
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栴
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3251 |
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样
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3252 |
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核
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3253 |
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根
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3254 |
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栻
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3255 |
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格
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3256 |
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栽
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3257 |
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栾
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3258 |
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栿
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3259 |
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桀
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3260 |
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桁
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3261 |
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桂
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3262 |
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桃
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3263 |
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桄
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3264 |
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桅
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3265 |
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框
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3266 |
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案
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3267 |
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桉
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3268 |
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桌
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3269 |
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桎
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3270 |
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桐
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3271 |
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桑
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3272 |
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桓
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3273 |
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桔
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3274 |
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桕
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3275 |
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桖
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3276 |
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桜
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3277 |
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桠
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3278 |
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桡
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3279 |
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桢
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3280 |
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档
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3281 |
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桤
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3282 |
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桥
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3283 |
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桦
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3284 |
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桧
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3285 |
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桨
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3286 |
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桩
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3287 |
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桫
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3288 |
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桴
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3289 |
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桶
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3290 |
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桷
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3291 |
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梁
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3292 |
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梃
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3293 |
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梅
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3294 |
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梆
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3295 |
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梏
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3296 |
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梓
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3297 |
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梗
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3298 |
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梢
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3299 |
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梣
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3300 |
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梦
|
3301 |
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梧
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3302 |
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梨
|
3303 |
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梭
|
3304 |
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梯
|
3305 |
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械
|
3306 |
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梳
|
3307 |
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梵
|
3308 |
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梶
|
3309 |
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梼
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3310 |
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梿
|
3311 |
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检
|
3312 |
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棂
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3313 |
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棉
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3314 |
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棋
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3315 |
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棍
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3316 |
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棐
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3317 |
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棒
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3318 |
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棕
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3319 |
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棘
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3320 |
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棚
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3321 |
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棠
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3322 |
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棣
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3323 |
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棨
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3324 |
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棪
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3325 |
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棫
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3326 |
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森
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3327 |
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棰
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3328 |
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棱
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3329 |
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棵
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3330 |
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棹
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3331 |
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棺
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3332 |
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棻
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3333 |
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棼
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3334 |
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椀
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3335 |
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椁
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3336 |
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椅
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3337 |
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椇
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3338 |
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椋
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3339 |
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植
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3340 |
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椎
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3341 |
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椐
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3342 |
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椒
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3343 |
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椛
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3344 |
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検
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3345 |
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椟
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3346 |
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椠
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3347 |
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椤
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3348 |
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椪
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3349 |
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椭
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3350 |
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椰
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3351 |
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椴
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3352 |
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椹
|
3353 |
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椽
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3354 |
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椿
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3355 |
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楂
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3356 |
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楔
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3357 |
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楗
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3358 |
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楙
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3359 |
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楚
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3360 |
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楛
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3361 |
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楝
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3362 |
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楞
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3363 |
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楠
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3364 |
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楢
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3365 |
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楣
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3366 |
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楤
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3367 |
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楦
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3368 |
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楩
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3369 |
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楪
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3370 |
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楫
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3371 |
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業
|
3372 |
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楮
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3373 |
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楯
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3374 |
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楶
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3375 |
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楷
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3376 |
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楸
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3377 |
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楹
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3378 |
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楼
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3379 |
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楽
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3380 |
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榀
|
3381 |
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概
|
3382 |
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榃
|
3383 |
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榄
|
3384 |
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榆
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3385 |
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榇
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3386 |
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榈
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3387 |
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榉
|
3388 |
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榊
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3389 |
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榎
|
3390 |
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榔
|
3391 |
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榕
|
3392 |
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榖
|
3393 |
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榘
|
3394 |
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榛
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3395 |
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榜
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3396 |
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榧
|
3397 |
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榨
|
3398 |
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榫
|
3399 |
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榭
|
3400 |
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榱
|
3401 |
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榴
|
3402 |
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榷
|
3403 |
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榻
|
3404 |
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榼
|
3405 |
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槁
|
3406 |
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槃
|
3407 |
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槅
|
3408 |
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槊
|
3409 |
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槌
|
3410 |
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槎
|
3411 |
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槐
|
3412 |
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槑
|
3413 |
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槔
|
3414 |
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様
|
3415 |
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槙
|
3416 |
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槚
|
3417 |
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槛
|
3418 |
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槜
|
3419 |
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槟
|
3420 |
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槠
|
3421 |
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槭
|
3422 |
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槱
|
3423 |
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槲
|
3424 |
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槵
|
3425 |
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槻
|
3426 |
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槽
|
3427 |
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槿
|
3428 |
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樊
|
3429 |
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樋
|
3430 |
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樗
|
3431 |
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樘
|
3432 |
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樛
|
3433 |
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樟
|
3434 |
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模
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3435 |
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樨
|
3436 |
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権
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3437 |
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横
|
3438 |
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樫
|
3439 |
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樯
|
3440 |
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樱
|
3441 |
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樵
|
3442 |
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樽
|
3443 |
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樾
|
3444 |
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橄
|
3445 |
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橇
|
3446 |
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橐
|
3447 |
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橘
|
3448 |
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橙
|
3449 |
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橚
|
3450 |
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橛
|
3451 |
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橡
|
3452 |
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橥
|
3453 |
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橦
|
3454 |
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橱
|
3455 |
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橹
|
3456 |
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橼
|
3457 |
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檀
|
3458 |
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檄
|
3459 |
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檇
|
3460 |
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檎
|
3461 |
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檐
|
3462 |
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檗
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3463 |
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檞
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3464 |
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檠
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3465 |
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檩
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3466 |
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檫
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3467 |
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檬
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3468 |
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檵
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3469 |
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櫂
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3470 |
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櫆
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3471 |
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欠
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3472 |
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次
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3473 |
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欢
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3474 |
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欣
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3475 |
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欤
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3476 |
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欧
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3477 |
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欲
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3478 |
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欷
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3479 |
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欸
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3480 |
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欹
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3481 |
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欺
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3482 |
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欻
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3483 |
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款
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3484 |
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歃
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3485 |
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歆
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3486 |
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歇
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3487 |
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歉
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3488 |
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歌
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3489 |
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歔
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3490 |
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歘
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3491 |
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歙
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3492 |
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止
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3493 |
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正
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3494 |
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此
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3495 |
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步
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3496 |
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武
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3497 |
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歧
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3498 |
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歩
|
3499 |
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歪
|
3500 |
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歳
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3501 |
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歴
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3502 |
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歹
|
3503 |
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歺
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3504 |
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死
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3505 |
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歼
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3506 |
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殁
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3507 |
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殂
|
3508 |
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殃
|
3509 |
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殄
|
3510 |
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殆
|
3511 |
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殇
|
3512 |
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殉
|
3513 |
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殊
|
3514 |
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残
|
3515 |
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殍
|
3516 |
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殑
|
3517 |
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殒
|
3518 |
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殓
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3519 |
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殖
|
3520 |
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殚
|
3521 |
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殛
|
3522 |
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殡
|
3523 |
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殢
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3524 |
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殪
|
3525 |
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殳
|
3526 |
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殴
|
3527 |
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段
|
3528 |
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殷
|
3529 |
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殿
|
3530 |
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毁
|
3531 |
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毂
|
3532 |
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毅
|
3533 |
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毋
|
3534 |
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毌
|
3535 |
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母
|
3536 |
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毎
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3537 |
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每
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3538 |
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毐
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3539 |
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毑
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3540 |
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毒
|
3541 |
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毓
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3542 |
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比
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3543 |
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毕
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3544 |
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毖
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3545 |
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毗
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3546 |
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毘
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3547 |
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毙
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3548 |
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毛
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3549 |
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毡
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3550 |
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毫
|
3551 |
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毯
|
3552 |
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毳
|
3553 |
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毵
|
3554 |
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毽
|
3555 |
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氀
|
3556 |
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氂
|
3557 |
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氅
|
3558 |
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氆
|
3559 |
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氇
|
3560 |
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氍
|
3561 |
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氏
|
3562 |
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氐
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3563 |
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民
|
3564 |
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氓
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3565 |
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气
|
3566 |
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氖
|
3567 |
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気
|
3568 |
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氘
|
3569 |
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氙
|
3570 |
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氚
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3571 |
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氛
|
3572 |
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氟
|
3573 |
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氡
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3574 |
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氢
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3575 |
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氤
|
3576 |
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氦
|
3577 |
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氧
|
3578 |
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氨
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3579 |
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氩
|
3580 |
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氪
|
3581 |
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氮
|
3582 |
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氯
|
3583 |
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氰
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3584 |
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氲
|
3585 |
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水
|
3586 |
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氵
|
3587 |
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氷
|
3588 |
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永
|
3589 |
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氹
|
3590 |
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氺
|
3591 |
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氽
|
3592 |
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氿
|
3593 |
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汀
|
3594 |
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汁
|
3595 |
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求
|
3596 |
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汆
|
3597 |
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汇
|
3598 |
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汉
|
3599 |
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汊
|
3600 |
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汐
|
3601 |
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汕
|
3602 |
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汖
|
3603 |
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汗
|
3604 |
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汛
|
3605 |
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汜
|
3606 |
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汝
|
3607 |
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汞
|
3608 |
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江
|
3609 |
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池
|
3610 |
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污
|
3611 |
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汤
|
3612 |
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汧
|
3613 |
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汨
|
3614 |
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汩
|
3615 |
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汪
|
3616 |
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汭
|
3617 |
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汰
|
3618 |
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汲
|
3619 |
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汴
|
3620 |
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汶
|
3621 |
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汸
|
3622 |
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汹
|
3623 |
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汽
|
3624 |
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汾
|
3625 |
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沁
|
3626 |
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沂
|
3627 |
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沃
|
3628 |
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沄
|
3629 |
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沅
|
3630 |
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沆
|
3631 |
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沇
|
3632 |
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沈
|
3633 |
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沉
|
3634 |
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沌
|
3635 |
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沏
|
3636 |
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沐
|
3637 |
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沒
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3638 |
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沓
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3639 |
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沔
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3640 |
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沕
|
3641 |
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沙
|
3642 |
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沚
|
3643 |
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沛
|
3644 |
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沟
|
3645 |
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没
|
3646 |
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沢
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3647 |
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沣
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3648 |
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沤
|
3649 |
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沥
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3650 |
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沦
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3651 |
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沧
|
3652 |
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沨
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3653 |
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沩
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3654 |
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沪
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3655 |
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沫
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3656 |
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沬
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3657 |
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沭
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3658 |
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沮
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3659 |
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沱
|
3660 |
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河
|
3661 |
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沴
|
3662 |
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沵
|
3663 |
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沸
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3664 |
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油
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3665 |
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治
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3666 |
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沼
|
3667 |
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沽
|
3668 |
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沾
|
3669 |
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沿
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3670 |
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泃
|
3671 |
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泄
|
3672 |
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泅
|
3673 |
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泇
|
3674 |
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泉
|
3675 |
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泊
|
3676 |
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泌
|
3677 |
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泐
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3678 |
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泓
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3679 |
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泔
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3680 |
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法
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3681 |
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泖
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3682 |
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泗
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3683 |
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泚
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3684 |
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泛
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3685 |
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泞
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3686 |
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泠
|
3687 |
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泡
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3688 |
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波
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3689 |
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泣
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3690 |
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泥
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3691 |
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注
|
3692 |
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泩
|
3693 |
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泪
|
3694 |
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泫
|
3695 |
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泮
|
3696 |
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泯
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3697 |
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泰
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3698 |
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泱
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3699 |
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泳
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3700 |
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泵
|
3701 |
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泷
|
3702 |
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泸
|
3703 |
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泺
|
3704 |
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泻
|
3705 |
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泼
|
3706 |
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泽
|
3707 |
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泾
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3708 |
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洁
|
3709 |
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洄
|
3710 |
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洇
|
3711 |
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洈
|
3712 |
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洊
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3713 |
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洋
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3714 |
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洌
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3715 |
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洎
|
3716 |
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洑
|
3717 |
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洒
|
3718 |
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洗
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3719 |
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洙
|
3720 |
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洛
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3721 |
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洞
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3722 |
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洢
|
3723 |
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洣
|
3724 |
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津
|
3725 |
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洧
|
3726 |
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洨
|
3727 |
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洪
|
3728 |
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洫
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3729 |
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洮
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3730 |
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洱
|
3731 |
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洲
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3732 |
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洳
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3733 |
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洵
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3734 |
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洸
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3735 |
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洹
|
3736 |
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洺
|
3737 |
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活
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3738 |
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洼
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3739 |
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洽
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3740 |
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派
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3741 |
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流
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3742 |
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浃
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3743 |
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浄
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3744 |
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浅
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3745 |
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浆
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3746 |
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浇
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3747 |
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浈
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3748 |
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浉
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3749 |
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浊
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3750 |
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测
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3751 |
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浍
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3752 |
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济
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3753 |
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浏
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3754 |
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浐
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3755 |
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浑
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3756 |
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浒
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3757 |
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浓
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3758 |
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浔
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3759 |
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浙
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3760 |
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浚
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3761 |
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浛
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3762 |
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浜
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3763 |
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浞
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3764 |
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浠
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3765 |
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浡
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3766 |
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浣
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3767 |
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浥
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3768 |
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浦
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3769 |
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浩
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3770 |
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浪
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3771 |
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浬
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3772 |
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浮
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3773 |
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浯
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3774 |
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浴
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3775 |
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海
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3776 |
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浸
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3777 |
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浼
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3778 |
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涂
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3779 |
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涅
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3780 |
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消
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3781 |
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涉
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3782 |
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涌
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3783 |
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涎
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3784 |
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涐
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3785 |
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涑
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3786 |
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涓
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3787 |
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涔
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3788 |
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涕
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3789 |
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涘
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3790 |
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涙
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3791 |
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涛
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3792 |
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涝
|
3793 |
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涞
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3794 |
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涟
|
3795 |
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涠
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3796 |
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涡
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3797 |
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涢
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3798 |
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涣
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3799 |
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涤
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3800 |
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润
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3801 |
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涧
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3802 |
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涨
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3803 |
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涩
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3804 |
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涪
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3805 |
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涫
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3806 |
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涮
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3807 |
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涯
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3808 |
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液
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3809 |
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涴
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3810 |
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涵
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3811 |
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涸
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3812 |
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涿
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3813 |
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淀
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3814 |
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淄
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3815 |
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淅
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3816 |
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淆
|
3817 |
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淇
|
3818 |
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淋
|
3819 |
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淌
|
3820 |
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淏
|
3821 |
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淑
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3822 |
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淖
|
3823 |
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淘
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3824 |
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淙
|
3825 |
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淛
|
3826 |
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淝
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3827 |
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淞
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3828 |
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淠
|
3829 |
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淡
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3830 |
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淤
|
3831 |
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淦
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3832 |
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淩
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3833 |
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淫
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3834 |
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淬
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3835 |
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淮
|
3836 |
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淯
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3837 |
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深
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3838 |
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淳
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3839 |
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混
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3840 |
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淸
|
3841 |
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淹
|
3842 |
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添
|
3843 |
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淼
|
3844 |
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渀
|
3845 |
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渃
|
3846 |
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清
|
3847 |
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済
|
3848 |
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渉
|
3849 |
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渊
|
3850 |
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渋
|
3851 |
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渌
|
3852 |
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渍
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3853 |
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渎
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3854 |
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渐
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3855 |
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渑
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3856 |
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渔
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3857 |
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渕
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3858 |
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渖
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3859 |
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渗
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3860 |
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渚
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3861 |
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渝
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3862 |
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渟
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3863 |
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渠
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3864 |
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渡
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3865 |
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渣
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3866 |
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渤
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3867 |
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渥
|
3868 |
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温
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3869 |
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渫
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3870 |
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渭
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3871 |
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港
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3872 |
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渲
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3873 |
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渴
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3874 |
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游
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3875 |
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渺
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3876 |
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渼
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3877 |
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湃
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3878 |
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湄
|
3879 |
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湉
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3880 |
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湋
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3881 |
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湍
|
3882 |
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湎
|
3883 |
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湑
|
3884 |
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湓
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3885 |
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湔
|
3886 |
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湖
|
3887 |
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湘
|
3888 |
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湛
|
3889 |
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湜
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3890 |
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湟
|
3891 |
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湣
|
3892 |
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湫
|
3893 |
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湮
|
3894 |
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湲
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3895 |
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湳
|
3896 |
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湴
|
3897 |
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湾
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3898 |
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湿
|
3899 |
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満
|
3900 |
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溁
|
3901 |
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溃
|
3902 |
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溅
|
3903 |
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溆
|
3904 |
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溇
|
3905 |
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溉
|
3906 |
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溍
|
3907 |
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溏
|
3908 |
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源
|
3909 |
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溘
|
3910 |
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溜
|
3911 |
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溞
|
3912 |
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溟
|
3913 |
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溢
|
3914 |
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溥
|
3915 |
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溦
|
3916 |
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溧
|
3917 |
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溪
|
3918 |
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溯
|
3919 |
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溱
|
3920 |
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溲
|
3921 |
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溴
|
3922 |
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溶
|
3923 |
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溷
|
3924 |
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溺
|
3925 |
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溽
|
3926 |
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滁
|
3927 |
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滂
|
3928 |
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滃
|
3929 |
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滆
|
3930 |
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滇
|
3931 |
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滈
|
3932 |
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滉
|
3933 |
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滋
|
3934 |
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滍
|
3935 |
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滏
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3936 |
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滑
|
3937 |
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滓
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3938 |
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滔
|
3939 |
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滕
|
3940 |
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滗
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3941 |
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滘
|
3942 |
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滙
|
3943 |
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滚
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3944 |
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滝
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3945 |
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滞
|
3946 |
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滟
|
3947 |
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滠
|
3948 |
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满
|
3949 |
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滢
|
3950 |
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滤
|
3951 |
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滥
|
3952 |
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滦
|
3953 |
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滨
|
3954 |
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滩
|
3955 |
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滴
|
3956 |
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滹
|
3957 |
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漀
|
3958 |
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漂
|
3959 |
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漆
|
3960 |
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漈
|
3961 |
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漉
|
3962 |
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漏
|
3963 |
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漓
|
3964 |
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演
|
3965 |
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漕
|
3966 |
+
漠
|
3967 |
+
漩
|
3968 |
+
漪
|
3969 |
+
漫
|
3970 |
+
漭
|
3971 |
+
漯
|
3972 |
+
漱
|
3973 |
+
漳
|
3974 |
+
漶
|
3975 |
+
漷
|
3976 |
+
漾
|
3977 |
+
潆
|
3978 |
+
潇
|
3979 |
+
潋
|
3980 |
+
潍
|
3981 |
+
潏
|
3982 |
+
潘
|
3983 |
+
潜
|
3984 |
+
潞
|
3985 |
+
潟
|
3986 |
+
潢
|
3987 |
+
潦
|
3988 |
+
潭
|
3989 |
+
潮
|
3990 |
+
潲
|
3991 |
+
潴
|
3992 |
+
潸
|
3993 |
+
潺
|
3994 |
+
潼
|
3995 |
+
潽
|
3996 |
+
潾
|
3997 |
+
澂
|
3998 |
+
澄
|
3999 |
+
澈
|
4000 |
+
澉
|
4001 |
+
澌
|
4002 |
+
澍
|
4003 |
+
澎
|
4004 |
+
澐
|
4005 |
+
澔
|
4006 |
+
澜
|
4007 |
+
澡
|
4008 |
+
澥
|
4009 |
+
澧
|
4010 |
+
澪
|
4011 |
+
澳
|
4012 |
+
澶
|
4013 |
+
澹
|
4014 |
+
激
|
4015 |
+
濂
|
4016 |
+
濆
|
4017 |
+
濉
|
4018 |
+
濊
|
4019 |
+
濑
|
4020 |
+
濒
|
4021 |
+
濙
|
4022 |
+
濛
|
4023 |
+
濞
|
4024 |
+
濠
|
4025 |
+
濡
|
4026 |
+
濩
|
4027 |
+
濬
|
4028 |
+
濮
|
4029 |
+
濯
|
4030 |
+
瀀
|
4031 |
+
瀍
|
4032 |
+
瀑
|
4033 |
+
瀚
|
4034 |
+
瀛
|
4035 |
+
瀞
|
4036 |
+
瀣
|
4037 |
+
瀬
|
4038 |
+
瀹
|
4039 |
+
瀼
|
4040 |
+
灌
|
4041 |
+
灏
|
4042 |
+
灞
|
4043 |
+
火
|
4044 |
+
灬
|
4045 |
+
灭
|
4046 |
+
灯
|
4047 |
+
灰
|
4048 |
+
灵
|
4049 |
+
灶
|
4050 |
+
灸
|
4051 |
+
灼
|
4052 |
+
灾
|
4053 |
+
灿
|
4054 |
+
炀
|
4055 |
+
炁
|
4056 |
+
炅
|
4057 |
+
炆
|
4058 |
+
炉
|
4059 |
+
炊
|
4060 |
+
炎
|
4061 |
+
炒
|
4062 |
+
炔
|
4063 |
+
炕
|
4064 |
+
炖
|
4065 |
+
炘
|
4066 |
+
炙
|
4067 |
+
炜
|
4068 |
+
炝
|
4069 |
+
炟
|
4070 |
+
炤
|
4071 |
+
炩
|
4072 |
+
炫
|
4073 |
+
炬
|
4074 |
+
炭
|
4075 |
+
炮
|
4076 |
+
炯
|
4077 |
+
炱
|
4078 |
+
炳
|
4079 |
+
炷
|
4080 |
+
炸
|
4081 |
+
点
|
4082 |
+
為
|
4083 |
+
炻
|
4084 |
+
炼
|
4085 |
+
炽
|
4086 |
+
烀
|
4087 |
+
烁
|
4088 |
+
烂
|
4089 |
+
烃
|
4090 |
+
烈
|
4091 |
+
烊
|
4092 |
+
烎
|
4093 |
+
烔
|
4094 |
+
烘
|
4095 |
+
烙
|
4096 |
+
烛
|
4097 |
+
烜
|
4098 |
+
烝
|
4099 |
+
烟
|
4100 |
+
烤
|
4101 |
+
烦
|
4102 |
+
烧
|
4103 |
+
烨
|
4104 |
+
烩
|
4105 |
+
烫
|
4106 |
+
烬
|
4107 |
+
热
|
4108 |
+
烯
|
4109 |
+
烷
|
4110 |
+
烹
|
4111 |
+
烺
|
4112 |
+
烽
|
4113 |
+
焉
|
4114 |
+
焊
|
4115 |
+
焌
|
4116 |
+
焐
|
4117 |
+
焓
|
4118 |
+
焕
|
4119 |
+
焖
|
4120 |
+
焗
|
4121 |
+
焘
|
4122 |
+
焙
|
4123 |
+
焚
|
4124 |
+
焜
|
4125 |
+
焞
|
4126 |
+
焦
|
4127 |
+
焮
|
4128 |
+
焯
|
4129 |
+
焰
|
4130 |
+
焱
|
4131 |
+
然
|
4132 |
+
焼
|
4133 |
+
煅
|
4134 |
+
煇
|
4135 |
+
煊
|
4136 |
+
煌
|
4137 |
+
煎
|
4138 |
+
煐
|
4139 |
+
煕
|
4140 |
+
煖
|
4141 |
+
煚
|
4142 |
+
煜
|
4143 |
+
煞
|
4144 |
+
煤
|
4145 |
+
煦
|
4146 |
+
照
|
4147 |
+
煨
|
4148 |
+
煮
|
4149 |
+
煲
|
4150 |
+
煳
|
4151 |
+
煴
|
4152 |
+
煸
|
4153 |
+
煺
|
4154 |
+
煽
|
4155 |
+
熄
|
4156 |
+
熇
|
4157 |
+
熊
|
4158 |
+
熏
|
4159 |
+
熔
|
4160 |
+
熘
|
4161 |
+
熙
|
4162 |
+
熜
|
4163 |
+
熟
|
4164 |
+
熠
|
4165 |
+
熥
|
4166 |
+
熨
|
4167 |
+
熬
|
4168 |
+
熳
|
4169 |
+
熵
|
4170 |
+
熹
|
4171 |
+
熺
|
4172 |
+
燃
|
4173 |
+
燊
|
4174 |
+
燋
|
4175 |
+
燎
|
4176 |
+
燏
|
4177 |
+
燔
|
4178 |
+
燕
|
4179 |
+
燚
|
4180 |
+
燠
|
4181 |
+
燥
|
4182 |
+
燧
|
4183 |
+
燮
|
4184 |
+
燹
|
4185 |
+
燻
|
4186 |
+
燿
|
4187 |
+
爀
|
4188 |
+
爆
|
4189 |
+
爇
|
4190 |
+
爨
|
4191 |
+
爪
|
4192 |
+
爬
|
4193 |
+
爰
|
4194 |
+
爱
|
4195 |
+
爲
|
4196 |
+
爵
|
4197 |
+
父
|
4198 |
+
爷
|
4199 |
+
爸
|
4200 |
+
爹
|
4201 |
+
爻
|
4202 |
+
爽
|
4203 |
+
爿
|
4204 |
+
牀
|
4205 |
+
牁
|
4206 |
+
牂
|
4207 |
+
片
|
4208 |
+
版
|
4209 |
+
牋
|
4210 |
+
牌
|
4211 |
+
牍
|
4212 |
+
牐
|
4213 |
+
牒
|
4214 |
+
牖
|
4215 |
+
牙
|
4216 |
+
牛
|
4217 |
+
牝
|
4218 |
+
牟
|
4219 |
+
牠
|
4220 |
+
牡
|
4221 |
+
牢
|
4222 |
+
牤
|
4223 |
+
牦
|
4224 |
+
牧
|
4225 |
+
物
|
4226 |
+
牯
|
4227 |
+
牲
|
4228 |
+
牵
|
4229 |
+
特
|
4230 |
+
牺
|
4231 |
+
牻
|
4232 |
+
牾
|
4233 |
+
犀
|
4234 |
+
犁
|
4235 |
+
犄
|
4236 |
+
犇
|
4237 |
+
犊
|
4238 |
+
犍
|
4239 |
+
犏
|
4240 |
+
犒
|
4241 |
+
犟
|
4242 |
+
犨
|
4243 |
+
犬
|
4244 |
+
犭
|
4245 |
+
犯
|
4246 |
+
犰
|
4247 |
+
犴
|
4248 |
+
状
|
4249 |
+
犷
|
4250 |
+
犸
|
4251 |
+
犹
|
4252 |
+
犼
|
4253 |
+
犽
|
4254 |
+
狁
|
4255 |
+
狂
|
4256 |
+
狃
|
4257 |
+
狄
|
4258 |
+
狈
|
4259 |
+
狌
|
4260 |
+
狍
|
4261 |
+
狎
|
4262 |
+
狐
|
4263 |
+
狒
|
4264 |
+
狗
|
4265 |
+
狙
|
4266 |
+
狛
|
4267 |
+
狝
|
4268 |
+
狞
|
4269 |
+
狠
|
4270 |
+
狡
|
4271 |
+
狨
|
4272 |
+
狩
|
4273 |
+
独
|
4274 |
+
狭
|
4275 |
+
狮
|
4276 |
+
狯
|
4277 |
+
狰
|
4278 |
+
狱
|
4279 |
+
狲
|
4280 |
+
狳
|
4281 |
+
狴
|
4282 |
+
狷
|
4283 |
+
狸
|
4284 |
+
狻
|
4285 |
+
狼
|
4286 |
+
猀
|
4287 |
+
猁
|
4288 |
+
猃
|
4289 |
+
猄
|
4290 |
+
猇
|
4291 |
+
猊
|
4292 |
+
猋
|
4293 |
+
猎
|
4294 |
+
猓
|
4295 |
+
猕
|
4296 |
+
猖
|
4297 |
+
猗
|
4298 |
+
猛
|
4299 |
+
猜
|
4300 |
+
猝
|
4301 |
+
猞
|
4302 |
+
猟
|
4303 |
+
猡
|
4304 |
+
猢
|
4305 |
+
猥
|
4306 |
+
猩
|
4307 |
+
猪
|
4308 |
+
猫
|
4309 |
+
猬
|
4310 |
+
献
|
4311 |
+
猰
|
4312 |
+
猱
|
4313 |
+
猴
|
4314 |
+
猷
|
4315 |
+
猹
|
4316 |
+
猾
|
4317 |
+
猿
|
4318 |
+
獍
|
4319 |
+
獐
|
4320 |
+
獒
|
4321 |
+
獗
|
4322 |
+
獠
|
4323 |
+
獣
|
4324 |
+
獬
|
4325 |
+
獭
|
4326 |
+
獴
|
4327 |
+
獾
|
4328 |
+
玁
|
4329 |
+
玄
|
4330 |
+
率
|
4331 |
+
玉
|
4332 |
+
王
|
4333 |
+
玎
|
4334 |
+
玏
|
4335 |
+
玑
|
4336 |
+
玕
|
4337 |
+
玖
|
4338 |
+
玗
|
4339 |
+
玘
|
4340 |
+
玙
|
4341 |
+
玚
|
4342 |
+
玛
|
4343 |
+
玟
|
4344 |
+
玠
|
4345 |
+
玡
|
4346 |
+
玢
|
4347 |
+
玥
|
4348 |
+
玦
|
4349 |
+
玧
|
4350 |
+
玩
|
4351 |
+
玫
|
4352 |
+
玭
|
4353 |
+
玮
|
4354 |
+
环
|
4355 |
+
现
|
4356 |
+
玲
|
4357 |
+
玳
|
4358 |
+
玷
|
4359 |
+
玹
|
4360 |
+
玺
|
4361 |
+
玻
|
4362 |
+
珀
|
4363 |
+
珂
|
4364 |
+
珅
|
4365 |
+
珈
|
4366 |
+
珉
|
4367 |
+
珊
|
4368 |
+
珍
|
4369 |
+
珏
|
4370 |
+
珐
|
4371 |
+
珑
|
4372 |
+
珖
|
4373 |
+
珙
|
4374 |
+
珝
|
4375 |
+
珞
|
4376 |
+
珠
|
4377 |
+
珣
|
4378 |
+
珥
|
4379 |
+
珦
|
4380 |
+
珧
|
4381 |
+
珩
|
4382 |
+
珪
|
4383 |
+
班
|
4384 |
+
珰
|
4385 |
+
珲
|
4386 |
+
珵
|
4387 |
+
珹
|
4388 |
+
珺
|
4389 |
+
珽
|
4390 |
+
琀
|
4391 |
+
球
|
4392 |
+
琅
|
4393 |
+
理
|
4394 |
+
琇
|
4395 |
+
琉
|
4396 |
+
琊
|
4397 |
+
琋
|
4398 |
+
琍
|
4399 |
+
琎
|
4400 |
+
琏
|
4401 |
+
琐
|
4402 |
+
琚
|
4403 |
+
琛
|
4404 |
+
琢
|
4405 |
+
琤
|
4406 |
+
琥
|
4407 |
+
琦
|
4408 |
+
琨
|
4409 |
+
琪
|
4410 |
+
琬
|
4411 |
+
琮
|
4412 |
+
琯
|
4413 |
+
琰
|
4414 |
+
琲
|
4415 |
+
琳
|
4416 |
+
琴
|
4417 |
+
琵
|
4418 |
+
琶
|
4419 |
+
琹
|
4420 |
+
琼
|
4421 |
+
瑀
|
4422 |
+
瑁
|
4423 |
+
瑄
|
4424 |
+
瑆
|
4425 |
+
瑊
|
4426 |
+
瑒
|
4427 |
+
瑕
|
4428 |
+
瑗
|
4429 |
+
瑙
|
4430 |
+
瑚
|
4431 |
+
瑛
|
4432 |
+
瑜
|
4433 |
+
瑞
|
4434 |
+
瑟
|
4435 |
+
瑠
|
4436 |
+
瑢
|
4437 |
+
瑧
|
4438 |
+
瑨
|
4439 |
+
瑭
|
4440 |
+
瑰
|
4441 |
+
瑱
|
4442 |
+
瑶
|
4443 |
+
瑷
|
4444 |
+
瑸
|
4445 |
+
瑺
|
4446 |
+
瑾
|
4447 |
+
璀
|
4448 |
+
璁
|
4449 |
+
璂
|
4450 |
+
璃
|
4451 |
+
璆
|
4452 |
+
璇
|
4453 |
+
璈
|
4454 |
+
璋
|
4455 |
+
璎
|
4456 |
+
璐
|
4457 |
+
璘
|
4458 |
+
璜
|
4459 |
+
璞
|
4460 |
+
璟
|
4461 |
+
璠
|
4462 |
+
璧
|
4463 |
+
璨
|
4464 |
+
璩
|
4465 |
+
璪
|
4466 |
+
璮
|
4467 |
+
璲
|
4468 |
+
璺
|
4469 |
+
璿
|
4470 |
+
瓌
|
4471 |
+
瓒
|
4472 |
+
瓘
|
4473 |
+
瓛
|
4474 |
+
瓜
|
4475 |
+
瓞
|
4476 |
+
瓟
|
4477 |
+
瓠
|
4478 |
+
瓢
|
4479 |
+
瓣
|
4480 |
+
瓤
|
4481 |
+
瓦
|
4482 |
+
瓮
|
4483 |
+
瓯
|
4484 |
+
瓴
|
4485 |
+
瓶
|
4486 |
+
瓷
|
4487 |
+
瓿
|
4488 |
+
甃
|
4489 |
+
甄
|
4490 |
+
甍
|
4491 |
+
甏
|
4492 |
+
甑
|
4493 |
+
甓
|
4494 |
+
甗
|
4495 |
+
甘
|
4496 |
+
甙
|
4497 |
+
甚
|
4498 |
+
甜
|
4499 |
+
生
|
4500 |
+
甡
|
4501 |
+
產
|
4502 |
+
甥
|
4503 |
+
用
|
4504 |
+
甩
|
4505 |
+
甪
|
4506 |
+
甫
|
4507 |
+
甬
|
4508 |
+
甭
|
4509 |
+
甯
|
4510 |
+
田
|
4511 |
+
由
|
4512 |
+
甲
|
4513 |
+
申
|
4514 |
+
电
|
4515 |
+
男
|
4516 |
+
甸
|
4517 |
+
町
|
4518 |
+
画
|
4519 |
+
甾
|
4520 |
+
畀
|
4521 |
+
畅
|
4522 |
+
畈
|
4523 |
+
畊
|
4524 |
+
畋
|
4525 |
+
界
|
4526 |
+
畎
|
4527 |
+
畏
|
4528 |
+
畑
|
4529 |
+
畔
|
4530 |
+
留
|
4531 |
+
畚
|
4532 |
+
畛
|
4533 |
+
畜
|
4534 |
+
畠
|
4535 |
+
畤
|
4536 |
+
略
|
4537 |
+
畦
|
4538 |
+
番
|
4539 |
+
畯
|
4540 |
+
畲
|
4541 |
+
畴
|
4542 |
+
畸
|
4543 |
+
畹
|
4544 |
+
畿
|
4545 |
+
疃
|
4546 |
+
疆
|
4547 |
+
疋
|
4548 |
+
疍
|
4549 |
+
疎
|
4550 |
+
疏
|
4551 |
+
疑
|
4552 |
+
疒
|
4553 |
+
疔
|
4554 |
+
疖
|
4555 |
+
疗
|
4556 |
+
疙
|
4557 |
+
疚
|
4558 |
+
疝
|
4559 |
+
疟
|
4560 |
+
疠
|
4561 |
+
疡
|
4562 |
+
疣
|
4563 |
+
疤
|
4564 |
+
疥
|
4565 |
+
疫
|
4566 |
+
疬
|
4567 |
+
疮
|
4568 |
+
疯
|
4569 |
+
疰
|
4570 |
+
疱
|
4571 |
+
疲
|
4572 |
+
疳
|
4573 |
+
疴
|
4574 |
+
疵
|
4575 |
+
疸
|
4576 |
+
疹
|
4577 |
+
疼
|
4578 |
+
疽
|
4579 |
+
疾
|
4580 |
+
痂
|
4581 |
+
痄
|
4582 |
+
病
|
4583 |
+
症
|
4584 |
+
痈
|
4585 |
+
痉
|
4586 |
+
痊
|
4587 |
+
痍
|
4588 |
+
痒
|
4589 |
+
痔
|
4590 |
+
痕
|
4591 |
+
痖
|
4592 |
+
痘
|
4593 |
+
痛
|
4594 |
+
痞
|
4595 |
+
痢
|
4596 |
+
痣
|
4597 |
+
痤
|
4598 |
+
痦
|
4599 |
+
痧
|
4600 |
+
痨
|
4601 |
+
痩
|
4602 |
+
痪
|
4603 |
+
痫
|
4604 |
+
痰
|
4605 |
+
痱
|
4606 |
+
痴
|
4607 |
+
痹
|
4608 |
+
痼
|
4609 |
+
痿
|
4610 |
+
瘀
|
4611 |
+
瘁
|
4612 |
+
瘅
|
4613 |
+
瘆
|
4614 |
+
瘊
|
4615 |
+
瘌
|
4616 |
+
瘐
|
4617 |
+
瘕
|
4618 |
+
瘖
|
4619 |
+
瘗
|
4620 |
+
瘘
|
4621 |
+
瘙
|
4622 |
+
瘛
|
4623 |
+
瘟
|
4624 |
+
瘠
|
4625 |
+
瘢
|
4626 |
+
瘤
|
4627 |
+
瘥
|
4628 |
+
瘦
|
4629 |
+
瘩
|
4630 |
+
瘪
|
4631 |
+
瘫
|
4632 |
+
瘰
|
4633 |
+
瘳
|
4634 |
+
瘴
|
4635 |
+
瘵
|
4636 |
+
瘸
|
4637 |
+
瘼
|
4638 |
+
瘾
|
4639 |
+
瘿
|
4640 |
+
癀
|
4641 |
+
癃
|
4642 |
+
癌
|
4643 |
+
癍
|
4644 |
+
癎
|
4645 |
+
癒
|
4646 |
+
癔
|
4647 |
+
癖
|
4648 |
+
癜
|
4649 |
+
癞
|
4650 |
+
癣
|
4651 |
+
癫
|
4652 |
+
癯
|
4653 |
+
癸
|
4654 |
+
発
|
4655 |
+
登
|
4656 |
+
發
|
4657 |
+
白
|
4658 |
+
百
|
4659 |
+
癿
|
4660 |
+
皂
|
4661 |
+
的
|
4662 |
+
皆
|
4663 |
+
皇
|
4664 |
+
皈
|
4665 |
+
皋
|
4666 |
+
皎
|
4667 |
+
皐
|
4668 |
+
皑
|
4669 |
+
皒
|
4670 |
+
皓
|
4671 |
+
皕
|
4672 |
+
皖
|
4673 |
+
皙
|
4674 |
+
皛
|
4675 |
+
皝
|
4676 |
+
皞
|
4677 |
+
皤
|
4678 |
+
皦
|
4679 |
+
皮
|
4680 |
+
皱
|
4681 |
+
皲
|
4682 |
+
皴
|
4683 |
+
皿
|
4684 |
+
盂
|
4685 |
+
盃
|
4686 |
+
盅
|
4687 |
+
盆
|
4688 |
+
盈
|
4689 |
+
盉
|
4690 |
+
益
|
4691 |
+
盌
|
4692 |
+
盍
|
4693 |
+
盎
|
4694 |
+
盏
|
4695 |
+
盐
|
4696 |
+
监
|
4697 |
+
盒
|
4698 |
+
盔
|
4699 |
+
盖
|
4700 |
+
盗
|
4701 |
+
盘
|
4702 |
+
盛
|
4703 |
+
盝
|
4704 |
+
盟
|
4705 |
+
盥
|
4706 |
+
盦
|
4707 |
+
盨
|
4708 |
+
盩
|
4709 |
+
目
|
4710 |
+
盯
|
4711 |
+
盱
|
4712 |
+
盲
|
4713 |
+
直
|
4714 |
+
相
|
4715 |
+
盹
|
4716 |
+
盼
|
4717 |
+
盾
|
4718 |
+
眀
|
4719 |
+
省
|
4720 |
+
眄
|
4721 |
+
眇
|
4722 |
+
眈
|
4723 |
+
眉
|
4724 |
+
看
|
4725 |
+
県
|
4726 |
+
眙
|
4727 |
+
眚
|
4728 |
+
眛
|
4729 |
+
眞
|
4730 |
+
真
|
4731 |
+
眠
|
4732 |
+
眦
|
4733 |
+
眨
|
4734 |
+
眩
|
4735 |
+
眬
|
4736 |
+
眭
|
4737 |
+
眯
|
4738 |
+
眵
|
4739 |
+
眶
|
4740 |
+
眷
|
4741 |
+
眸
|
4742 |
+
眺
|
4743 |
+
眼
|
4744 |
+
着
|
4745 |
+
睁
|
4746 |
+
睇
|
4747 |
+
睐
|
4748 |
+
睑
|
4749 |
+
睒
|
4750 |
+
睚
|
4751 |
+
睛
|
4752 |
+
睡
|
4753 |
+
睢
|
4754 |
+
督
|
4755 |
+
睥
|
4756 |
+
睦
|
4757 |
+
睨
|
4758 |
+
睪
|
4759 |
+
睫
|
4760 |
+
睬
|
4761 |
+
睱
|
4762 |
+
睹
|
4763 |
+
睺
|
4764 |
+
睽
|
4765 |
+
睾
|
4766 |
+
睿
|
4767 |
+
瞀
|
4768 |
+
瞄
|
4769 |
+
瞅
|
4770 |
+
瞋
|
4771 |
+
瞌
|
4772 |
+
瞎
|
4773 |
+
瞑
|
4774 |
+
瞒
|
4775 |
+
瞟
|
4776 |
+
瞠
|
4777 |
+
瞢
|
4778 |
+
瞥
|
4779 |
+
瞧
|
4780 |
+
瞩
|
4781 |
+
瞪
|
4782 |
+
瞬
|
4783 |
+
瞭
|
4784 |
+
瞰
|
4785 |
+
瞳
|
4786 |
+
瞻
|
4787 |
+
瞽
|
4788 |
+
瞿
|
4789 |
+
矍
|
4790 |
+
矗
|
4791 |
+
矛
|
4792 |
+
矜
|
4793 |
+
矞
|
4794 |
+
矢
|
4795 |
+
矣
|
4796 |
+
知
|
4797 |
+
矧
|
4798 |
+
矩
|
4799 |
+
矫
|
4800 |
+
矬
|
4801 |
+
短
|
4802 |
+
矮
|
4803 |
+
石
|
4804 |
+
矶
|
4805 |
+
矸
|
4806 |
+
矽
|
4807 |
+
矾
|
4808 |
+
矿
|
4809 |
+
砀
|
4810 |
+
码
|
4811 |
+
砂
|
4812 |
+
砉
|
4813 |
+
砌
|
4814 |
+
砍
|
4815 |
+
砑
|
4816 |
+
砒
|
4817 |
+
研
|
4818 |
+
砕
|
4819 |
+
砖
|
4820 |
+
砗
|
4821 |
+
砚
|
4822 |
+
砜
|
4823 |
+
砝
|
4824 |
+
砟
|
4825 |
+
砢
|
4826 |
+
砣
|
4827 |
+
砥
|
4828 |
+
砦
|
4829 |
+
砧
|
4830 |
+
砩
|
4831 |
+
砬
|
4832 |
+
砭
|
4833 |
+
砰
|
4834 |
+
砳
|
4835 |
+
破
|
4836 |
+
砵
|
4837 |
+
砷
|
4838 |
+
砸
|
4839 |
+
砹
|
4840 |
+
砺
|
4841 |
+
砻
|
4842 |
+
砼
|
4843 |
+
砾
|
4844 |
+
础
|
4845 |
+
硅
|
4846 |
+
硇
|
4847 |
+
硌
|
4848 |
+
硎
|
4849 |
+
硏
|
4850 |
+
硐
|
4851 |
+
硒
|
4852 |
+
硔
|
4853 |
+
硕
|
4854 |
+
硖
|
4855 |
+
硗
|
4856 |
+
硙
|
4857 |
+
硚
|
4858 |
+
硝
|
4859 |
+
硪
|
4860 |
+
硫
|
4861 |
+
硬
|
4862 |
+
确
|
4863 |
+
硷
|
4864 |
+
硼
|
4865 |
+
碁
|
4866 |
+
碇
|
4867 |
+
碉
|
4868 |
+
碌
|
4869 |
+
碍
|
4870 |
+
碎
|
4871 |
+
碏
|
4872 |
+
碑
|
4873 |
+
碓
|
4874 |
+
碗
|
4875 |
+
碘
|
4876 |
+
碚
|
4877 |
+
碛
|
4878 |
+
碜
|
4879 |
+
碟
|
4880 |
+
碡
|
4881 |
+
碣
|
4882 |
+
碥
|
4883 |
+
碧
|
4884 |
+
碰
|
4885 |
+
碱
|
4886 |
+
碲
|
4887 |
+
碳
|
4888 |
+
碴
|
4889 |
+
碶
|
4890 |
+
碹
|
4891 |
+
碾
|
4892 |
+
磁
|
4893 |
+
磅
|
4894 |
+
磉
|
4895 |
+
磊
|
4896 |
+
磋
|
4897 |
+
磐
|
4898 |
+
磔
|
4899 |
+
磕
|
4900 |
+
磙
|
4901 |
+
磜
|
4902 |
+
磡
|
4903 |
+
磦
|
4904 |
+
磨
|
4905 |
+
磬
|
4906 |
+
磲
|
4907 |
+
磴
|
4908 |
+
磷
|
4909 |
+
磺
|
4910 |
+
磻
|
4911 |
+
磾
|
4912 |
+
礁
|
4913 |
+
礅
|
4914 |
+
礐
|
4915 |
+
礓
|
4916 |
+
礞
|
4917 |
+
礤
|
4918 |
+
礴
|
4919 |
+
示
|
4920 |
+
礻
|
4921 |
+
礼
|
4922 |
+
礽
|
4923 |
+
社
|
4924 |
+
祀
|
4925 |
+
祁
|
4926 |
+
祂
|
4927 |
+
祆
|
4928 |
+
祇
|
4929 |
+
祈
|
4930 |
+
祉
|
4931 |
+
祊
|
4932 |
+
祎
|
4933 |
+
祏
|
4934 |
+
祐
|
4935 |
+
祓
|
4936 |
+
祔
|
4937 |
+
祖
|
4938 |
+
祗
|
4939 |
+
祘
|
4940 |
+
祚
|
4941 |
+
祛
|
4942 |
+
祜
|
4943 |
+
祝
|
4944 |
+
神
|
4945 |
+
祟
|
4946 |
+
祠
|
4947 |
+
祢
|
4948 |
+
祥
|
4949 |
+
祧
|
4950 |
+
票
|
4951 |
+
祭
|
4952 |
+
祯
|
4953 |
+
祲
|
4954 |
+
祷
|
4955 |
+
祸
|
4956 |
+
祹
|
4957 |
+
祺
|
4958 |
+
祼
|
4959 |
+
祾
|
4960 |
+
禀
|
4961 |
+
禁
|
4962 |
+
禄
|
4963 |
+
禅
|
4964 |
+
禇
|
4965 |
+
禊
|
4966 |
+
禋
|
4967 |
+
福
|
4968 |
+
禑
|
4969 |
+
禔
|
4970 |
+
禖
|
4971 |
+
禘
|
4972 |
+
禚
|
4973 |
+
禛
|
4974 |
+
禟
|
4975 |
+
禤
|
4976 |
+
禧
|
4977 |
+
禩
|
4978 |
+
禳
|
4979 |
+
禵
|
4980 |
+
禹
|
4981 |
+
禺
|
4982 |
+
离
|
4983 |
+
禽
|
4984 |
+
禾
|
4985 |
+
秀
|
4986 |
+
私
|
4987 |
+
秂
|
4988 |
+
秃
|
4989 |
+
秆
|
4990 |
+
秉
|
4991 |
+
秋
|
4992 |
+
种
|
4993 |
+
科
|
4994 |
+
秒
|
4995 |
+
秕
|
4996 |
+
秘
|
4997 |
+
租
|
4998 |
+
秣
|
4999 |
+
秤
|
5000 |
+
秦
|
5001 |
+
秧
|
5002 |
+
秩
|
5003 |
+
秫
|
5004 |
+
秬
|
5005 |
+
秭
|
5006 |
+
积
|
5007 |
+
称
|
5008 |
+
秸
|
5009 |
+
移
|
5010 |
+
秽
|
5011 |
+
秾
|
5012 |
+
稀
|
5013 |
+
稃
|
5014 |
+
程
|
5015 |
+
稍
|
5016 |
+
税
|
5017 |
+
稔
|
5018 |
+
稗
|
5019 |
+
稙
|
5020 |
+
稚
|
5021 |
+
稞
|
5022 |
+
稠
|
5023 |
+
稣
|
5024 |
+
稲
|
5025 |
+
稳
|
5026 |
+
稷
|
5027 |
+
稹
|
5028 |
+
稻
|
5029 |
+
稼
|
5030 |
+
稽
|
5031 |
+
稿
|
5032 |
+
穂
|
5033 |
+
穆
|
5034 |
+
穉
|
5035 |
+
穏
|
5036 |
+
穑
|
5037 |
+
穗
|
5038 |
+
穣
|
5039 |
+
穰
|
5040 |
+
穴
|
5041 |
+
究
|
5042 |
+
穷
|
5043 |
+
穹
|
5044 |
+
空
|
5045 |
+
穿
|
5046 |
+
突
|
5047 |
+
窃
|
5048 |
+
窄
|
5049 |
+
窅
|
5050 |
+
窆
|
5051 |
+
窈
|
5052 |
+
窊
|
5053 |
+
窋
|
5054 |
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窍
|
5055 |
+
窑
|
5056 |
+
窒
|
5057 |
+
窓
|
5058 |
+
窕
|
5059 |
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窖
|
5060 |
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窗
|
5061 |
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窘
|
5062 |
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窜
|
5063 |
+
窝
|
5064 |
+
窟
|
5065 |
+
窠
|
5066 |
+
窣
|
5067 |
+
窥
|
5068 |
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窦
|
5069 |
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窨
|
5070 |
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窭
|
5071 |
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窰
|
5072 |
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窳
|
5073 |
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窸
|
5074 |
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窿
|
5075 |
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立
|
5076 |
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竑
|
5077 |
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竖
|
5078 |
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站
|
5079 |
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竜
|
5080 |
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竝
|
5081 |
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竞
|
5082 |
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竟
|
5083 |
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章
|
5084 |
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竣
|
5085 |
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童
|
5086 |
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竦
|
5087 |
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竭
|
5088 |
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端
|
5089 |
+
竹
|
5090 |
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竺
|
5091 |
+
竽
|
5092 |
+
竿
|
5093 |
+
笃
|
5094 |
+
笄
|
5095 |
+
笆
|
5096 |
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笈
|
5097 |
+
笊
|
5098 |
+
笋
|
5099 |
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笏
|
5100 |
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笑
|
5101 |
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笔
|
5102 |
+
笕
|
5103 |
+
笙
|
5104 |
+
笛
|
5105 |
+
笞
|
5106 |
+
笠
|
5107 |
+
笤
|
5108 |
+
笥
|
5109 |
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符
|
5110 |
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笨
|
5111 |
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笪
|
5112 |
+
笫
|
5113 |
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第
|
5114 |
+
笮
|
5115 |
+
笱
|
5116 |
+
笳
|
5117 |
+
笸
|
5118 |
+
笹
|
5119 |
+
笺
|
5120 |
+
笼
|
5121 |
+
笾
|
5122 |
+
筅
|
5123 |
+
筇
|
5124 |
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等
|
5125 |
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筊
|
5126 |
+
筋
|
5127 |
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筌
|
5128 |
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筏
|
5129 |
+
筐
|
5130 |
+
筑
|
5131 |
+
筒
|
5132 |
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答
|
5133 |
+
策
|
5134 |
+
筘
|
5135 |
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筚
|
5136 |
+
筛
|
5137 |
+
筜
|
5138 |
+
筝
|
5139 |
+
筠
|
5140 |
+
筭
|
5141 |
+
筮
|
5142 |
+
筯
|
5143 |
+
筱
|
5144 |
+
筲
|
5145 |
+
筴
|
5146 |
+
筵
|
5147 |
+
筷
|
5148 |
+
筹
|
5149 |
+
筼
|
5150 |
+
签
|
5151 |
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简
|
5152 |
+
箅
|
5153 |
+
箌
|
5154 |
+
箍
|
5155 |
+
箐
|
5156 |
+
箓
|
5157 |
+
箔
|
5158 |
+
箕
|
5159 |
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算
|
5160 |
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箜
|
5161 |
+
箝
|
5162 |
+
管
|
5163 |
+
箦
|
5164 |
+
箧
|
5165 |
+
箨
|
5166 |
+
箩
|
5167 |
+
箪
|
5168 |
+
箫
|
5169 |
+
箬
|
5170 |
+
箭
|
5171 |
+
箱
|
5172 |
+
箴
|
5173 |
+
箸
|
5174 |
+
篁
|
5175 |
+
篆
|
5176 |
+
篇
|
5177 |
+
篌
|
5178 |
+
篑
|
5179 |
+
篓
|
5180 |
+
篙
|
5181 |
+
篚
|
5182 |
+
篝
|
5183 |
+
篡
|
5184 |
+
篥
|
5185 |
+
篦
|
5186 |
+
篪
|
5187 |
+
篮
|
5188 |
+
篯
|
5189 |
+
篱
|
5190 |
+
篷
|
5191 |
+
篼
|
5192 |
+
篾
|
5193 |
+
簃
|
5194 |
+
簇
|
5195 |
+
簋
|
5196 |
+
簌
|
5197 |
+
簏
|
5198 |
+
簕
|
5199 |
+
簖
|
5200 |
+
簟
|
5201 |
+
簠
|
5202 |
+
簦
|
5203 |
+
簧
|
5204 |
+
簪
|
5205 |
+
簰
|
5206 |
+
簸
|
5207 |
+
簿
|
5208 |
+
籀
|
5209 |
+
籁
|
5210 |
+
籍
|
5211 |
+
籓
|
5212 |
+
籙
|
5213 |
+
米
|
5214 |
+
籴
|
5215 |
+
籺
|
5216 |
+
类
|
5217 |
+
籼
|
5218 |
+
籽
|
5219 |
+
粄
|
5220 |
+
粉
|
5221 |
+
粑
|
5222 |
+
粒
|
5223 |
+
粕
|
5224 |
+
粗
|
5225 |
+
粘
|
5226 |
+
粜
|
5227 |
+
粝
|
5228 |
+
粞
|
5229 |
+
粟
|
5230 |
+
粢
|
5231 |
+
粤
|
5232 |
+
粥
|
5233 |
+
粦
|
5234 |
+
粧
|
5235 |
+
粪
|
5236 |
+
粬
|
5237 |
+
粮
|
5238 |
+
粱
|
5239 |
+
粲
|
5240 |
+
粳
|
5241 |
+
粹
|
5242 |
+
粼
|
5243 |
+
粽
|
5244 |
+
精
|
5245 |
+
粿
|
5246 |
+
糁
|
5247 |
+
糅
|
5248 |
+
糊
|
5249 |
+
糌
|
5250 |
+
糍
|
5251 |
+
糕
|
5252 |
+
糖
|
5253 |
+
糗
|
5254 |
+
糙
|
5255 |
+
糜
|
5256 |
+
糟
|
5257 |
+
糠
|
5258 |
+
糨
|
5259 |
+
糬
|
5260 |
+
糯
|
5261 |
+
糸
|
5262 |
+
系
|
5263 |
+
紊
|
5264 |
+
紘
|
5265 |
+
素
|
5266 |
+
索
|
5267 |
+
紧
|
5268 |
+
紫
|
5269 |
+
紬
|
5270 |
+
紮
|
5271 |
+
累
|
5272 |
+
経
|
5273 |
+
絜
|
5274 |
+
絪
|
5275 |
+
絮
|
5276 |
+
絵
|
5277 |
+
絶
|
5278 |
+
絷
|
5279 |
+
絺
|
5280 |
+
綎
|
5281 |
+
綖
|
5282 |
+
継
|
5283 |
+
続
|
5284 |
+
綝
|
5285 |
+
綦
|
5286 |
+
綫
|
5287 |
+
綮
|
5288 |
+
総
|
5289 |
+
緑
|
5290 |
+
緾
|
5291 |
+
縁
|
5292 |
+
縂
|
5293 |
+
縠
|
5294 |
+
縢
|
5295 |
+
縻
|
5296 |
+
繁
|
5297 |
+
繇
|
5298 |
+
繋
|
5299 |
+
繸
|
5300 |
+
繻
|
5301 |
+
纁
|
5302 |
+
纂
|
5303 |
+
纚
|
5304 |
+
纛
|
5305 |
+
纟
|
5306 |
+
纠
|
5307 |
+
纡
|
5308 |
+
红
|
5309 |
+
纣
|
5310 |
+
纤
|
5311 |
+
纥
|
5312 |
+
约
|
5313 |
+
级
|
5314 |
+
纨
|
5315 |
+
纩
|
5316 |
+
纪
|
5317 |
+
纫
|
5318 |
+
纬
|
5319 |
+
纭
|
5320 |
+
纮
|
5321 |
+
纯
|
5322 |
+
纰
|
5323 |
+
纱
|
5324 |
+
纲
|
5325 |
+
纳
|
5326 |
+
纵
|
5327 |
+
纶
|
5328 |
+
纷
|
5329 |
+
纸
|
5330 |
+
纹
|
5331 |
+
纺
|
5332 |
+
纻
|
5333 |
+
纽
|
5334 |
+
纾
|
5335 |
+
线
|
5336 |
+
绀
|
5337 |
+
绁
|
5338 |
+
绂
|
5339 |
+
练
|
5340 |
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组
|
5341 |
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绅
|
5342 |
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细
|
5343 |
+
织
|
5344 |
+
终
|
5345 |
+
绉
|
5346 |
+
绊
|
5347 |
+
绌
|
5348 |
+
绍
|
5349 |
+
绎
|
5350 |
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经
|
5351 |
+
绐
|
5352 |
+
绑
|
5353 |
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绒
|
5354 |
+
结
|
5355 |
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绔
|
5356 |
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绕
|
5357 |
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绗
|
5358 |
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绘
|
5359 |
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给
|
5360 |
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绚
|
5361 |
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绛
|
5362 |
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络
|
5363 |
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绝
|
5364 |
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绞
|
5365 |
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统
|
5366 |
+
绠
|
5367 |
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绡
|
5368 |
+
绢
|
5369 |
+
绣
|
5370 |
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绥
|
5371 |
+
绦
|
5372 |
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继
|
5373 |
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绨
|
5374 |
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绩
|
5375 |
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绪
|
5376 |
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绫
|
5377 |
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续
|
5378 |
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绮
|
5379 |
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绯
|
5380 |
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绰
|
5381 |
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绱
|
5382 |
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绲
|
5383 |
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绳
|
5384 |
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维
|
5385 |
+
绵
|
5386 |
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绶
|
5387 |
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绷
|
5388 |
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绸
|
5389 |
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绹
|
5390 |
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绺
|
5391 |
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绻
|
5392 |
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综
|
5393 |
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绽
|
5394 |
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绾
|
5395 |
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绿
|
5396 |
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缀
|
5397 |
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缁
|
5398 |
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缂
|
5399 |
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缃
|
5400 |
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缄
|
5401 |
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缅
|
5402 |
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缆
|
5403 |
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缇
|
5404 |
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缈
|
5405 |
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缉
|
5406 |
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缊
|
5407 |
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缋
|
5408 |
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缌
|
5409 |
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缍
|
5410 |
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缎
|
5411 |
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缐
|
5412 |
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缑
|
5413 |
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缒
|
5414 |
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缓
|
5415 |
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缔
|
5416 |
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缕
|
5417 |
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编
|
5418 |
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缗
|
5419 |
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缘
|
5420 |
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缙
|
5421 |
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缚
|
5422 |
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缛
|
5423 |
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缜
|
5424 |
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缝
|
5425 |
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缟
|
5426 |
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缠
|
5427 |
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缡
|
5428 |
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缢
|
5429 |
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缣
|
5430 |
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缤
|
5431 |
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缥
|
5432 |
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缦
|
5433 |
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缧
|
5434 |
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缨
|
5435 |
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缩
|
5436 |
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缪
|
5437 |
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缫
|
5438 |
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缬
|
5439 |
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缭
|
5440 |
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缮
|
5441 |
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缯
|
5442 |
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缰
|
5443 |
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缱
|
5444 |
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缲
|
5445 |
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缳
|
5446 |
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缴
|
5447 |
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缵
|
5448 |
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缶
|
5449 |
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缷
|
5450 |
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缸
|
5451 |
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缺
|
5452 |
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罂
|
5453 |
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罃
|
5454 |
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罄
|
5455 |
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罅
|
5456 |
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罍
|
5457 |
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罐
|
5458 |
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网
|
5459 |
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罔
|
5460 |
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罕
|
5461 |
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罗
|
5462 |
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罘
|
5463 |
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罚
|
5464 |
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罝
|
5465 |
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罟
|
5466 |
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罠
|
5467 |
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罡
|
5468 |
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罢
|
5469 |
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罣
|
5470 |
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罥
|
5471 |
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罨
|
5472 |
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罩
|
5473 |
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罪
|
5474 |
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置
|
5475 |
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罱
|
5476 |
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署
|
5477 |
+
罴
|
5478 |
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罹
|
5479 |
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罽
|
5480 |
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罾
|
5481 |
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羁
|
5482 |
+
羊
|
5483 |
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羌
|
5484 |
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美
|
5485 |
+
羑
|
5486 |
+
羔
|
5487 |
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羕
|
5488 |
+
羚
|
5489 |
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羝
|
5490 |
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羞
|
5491 |
+
羟
|
5492 |
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羡
|
5493 |
+
羣
|
5494 |
+
群
|
5495 |
+
羧
|
5496 |
+
羮
|
5497 |
+
羯
|
5498 |
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羰
|
5499 |
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羲
|
5500 |
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羸
|
5501 |
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羹
|
5502 |
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羼
|
5503 |
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羽
|
5504 |
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羿
|
5505 |
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翀
|
5506 |
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翁
|
5507 |
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翃
|
5508 |
+
翅
|
5509 |
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翈
|
5510 |
+
翊
|
5511 |
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翌
|
5512 |
+
翎
|
5513 |
+
翔
|
5514 |
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翕
|
5515 |
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翘
|
5516 |
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翙
|
5517 |
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翚
|
5518 |
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翛
|
5519 |
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翟
|
5520 |
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翠
|
5521 |
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翡
|
5522 |
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翥
|
5523 |
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翦
|
5524 |
+
翩
|
5525 |
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翫
|
5526 |
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翮
|
5527 |
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翰
|
5528 |
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翱
|
5529 |
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翳
|
5530 |
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翻
|
5531 |
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翼
|
5532 |
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翾
|
5533 |
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耀
|
5534 |
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老
|
5535 |
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考
|
5536 |
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耄
|
5537 |
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者
|
5538 |
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耆
|
5539 |
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耋
|
5540 |
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而
|
5541 |
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耍
|
5542 |
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耐
|
5543 |
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耒
|
5544 |
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耔
|
5545 |
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耕
|
5546 |
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耗
|
5547 |
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耘
|
5548 |
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耙
|
5549 |
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耜
|
5550 |
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耦
|
5551 |
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耧
|
5552 |
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耨
|
5553 |
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耩
|
5554 |
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耪
|
5555 |
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耳
|
5556 |
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耵
|
5557 |
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耶
|
5558 |
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耷
|
5559 |
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耸
|
5560 |
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耻
|
5561 |
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耽
|
5562 |
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耿
|
5563 |
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聂
|
5564 |
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聃
|
5565 |
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聆
|
5566 |
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聊
|
5567 |
+
聋
|
5568 |
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职
|
5569 |
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聍
|
5570 |
+
聒
|
5571 |
+
联
|
5572 |
+
聕
|
5573 |
+
聘
|
5574 |
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聚
|
5575 |
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聡
|
5576 |
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聩
|
5577 |
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聪
|
5578 |
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聴
|
5579 |
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聼
|
5580 |
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聿
|
5581 |
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肃
|
5582 |
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肄
|
5583 |
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肆
|
5584 |
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肇
|
5585 |
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肉
|
5586 |
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肋
|
5587 |
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肌
|
5588 |
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肓
|
5589 |
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肖
|
5590 |
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肘
|
5591 |
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肚
|
5592 |
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肛
|
5593 |
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肜
|
5594 |
+
肝
|
5595 |
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肟
|
5596 |
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肠
|
5597 |
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股
|
5598 |
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肢
|
5599 |
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肤
|
5600 |
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肥
|
5601 |
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肩
|
5602 |
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肪
|
5603 |
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肫
|
5604 |
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肮
|
5605 |
+
肯
|
5606 |
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肱
|
5607 |
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育
|
5608 |
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肴
|
5609 |
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肸
|
5610 |
+
肺
|
5611 |
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肼
|
5612 |
+
肽
|
5613 |
+
肾
|
5614 |
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肿
|
5615 |
+
胀
|
5616 |
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胁
|
5617 |
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胂
|
5618 |
+
胃
|
5619 |
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胄
|
5620 |
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胆
|
5621 |
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背
|
5622 |
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胍
|
5623 |
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胎
|
5624 |
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胖
|
5625 |
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胗
|
5626 |
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胙
|
5627 |
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胚
|
5628 |
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胛
|
5629 |
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胜
|
5630 |
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胝
|
5631 |
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胞
|
5632 |
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胠
|
5633 |
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胡
|
5634 |
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胤
|
5635 |
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胥
|
5636 |
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胧
|
5637 |
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胨
|
5638 |
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胪
|
5639 |
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胫
|
5640 |
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胬
|
5641 |
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胭
|
5642 |
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胯
|
5643 |
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胰
|
5644 |
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胱
|
5645 |
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胳
|
5646 |
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胴
|
5647 |
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胶
|
5648 |
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胸
|
5649 |
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胺
|
5650 |
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胼
|
5651 |
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能
|
5652 |
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脁
|
5653 |
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脂
|
5654 |
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脆
|
5655 |
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脇
|
5656 |
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脉
|
5657 |
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脊
|
5658 |
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脍
|
5659 |
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脏
|
5660 |
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脐
|
5661 |
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脑
|
5662 |
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脒
|
5663 |
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脓
|
5664 |
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脔
|
5665 |
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脖
|
5666 |
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脘
|
5667 |
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脚
|
5668 |
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脞
|
5669 |
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脢
|
5670 |
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脩
|
5671 |
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脬
|
5672 |
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脯
|
5673 |
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脱
|
5674 |
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脲
|
5675 |
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脳
|
5676 |
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脷
|
5677 |
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脸
|
5678 |
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脾
|
5679 |
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脿
|
5680 |
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腆
|
5681 |
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腈
|
5682 |
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腊
|
5683 |
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腋
|
5684 |
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腌
|
5685 |
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腐
|
5686 |
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腑
|
5687 |
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腓
|
5688 |
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腔
|
5689 |
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腕
|
5690 |
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腘
|
5691 |
+
腙
|
5692 |
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腚
|
5693 |
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腠
|
5694 |
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腥
|
5695 |
+
腧
|
5696 |
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腩
|
5697 |
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腭
|
5698 |
+
腮
|
5699 |
+
腰
|
5700 |
+
腱
|
5701 |
+
腴
|
5702 |
+
腹
|
5703 |
+
腺
|
5704 |
+
腻
|
5705 |
+
腼
|
5706 |
+
腾
|
5707 |
+
腿
|
5708 |
+
膀
|
5709 |
+
膂
|
5710 |
+
膈
|
5711 |
+
膊
|
5712 |
+
膏
|
5713 |
+
膑
|
5714 |
+
膘
|
5715 |
+
膛
|
5716 |
+
膜
|
5717 |
+
膝
|
5718 |
+
膦
|
5719 |
+
膨
|
5720 |
+
膳
|
5721 |
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膺
|
5722 |
+
膻
|
5723 |
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臀
|
5724 |
+
臁
|
5725 |
+
臂
|
5726 |
+
臃
|
5727 |
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臆
|
5728 |
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臊
|
5729 |
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臌
|
5730 |
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臑
|
5731 |
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臓
|
5732 |
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臜
|
5733 |
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臞
|
5734 |
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臣
|
5735 |
+
臧
|
5736 |
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自
|
5737 |
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臬
|
5738 |
+
臭
|
5739 |
+
至
|
5740 |
+
致
|
5741 |
+
臵
|
5742 |
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臻
|
5743 |
+
臼
|
5744 |
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臾
|
5745 |
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舀
|
5746 |
+
舁
|
5747 |
+
舂
|
5748 |
+
舄
|
5749 |
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舅
|
5750 |
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舆
|
5751 |
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舌
|
5752 |
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舍
|
5753 |
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舎
|
5754 |
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舐
|
5755 |
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舒
|
5756 |
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舔
|
5757 |
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舖
|
5758 |
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舘
|
5759 |
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舛
|
5760 |
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舜
|
5761 |
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舞
|
5762 |
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舟
|
5763 |
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舡
|
5764 |
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舢
|
5765 |
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舣
|
5766 |
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舨
|
5767 |
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航
|
5768 |
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舫
|
5769 |
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般
|
5770 |
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舯
|
5771 |
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舰
|
5772 |
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舱
|
5773 |
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舲
|
5774 |
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舳
|
5775 |
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舴
|
5776 |
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舵
|
5777 |
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舶
|
5778 |
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舷
|
5779 |
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舸
|
5780 |
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船
|
5781 |
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舺
|
5782 |
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舻
|
5783 |
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舾
|
5784 |
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艄
|
5785 |
+
艇
|
5786 |
+
艉
|
5787 |
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艋
|
5788 |
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艏
|
5789 |
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艘
|
5790 |
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艟
|
5791 |
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艨
|
5792 |
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艮
|
5793 |
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良
|
5794 |
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艰
|
5795 |
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色
|
5796 |
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艳
|
5797 |
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艹
|
5798 |
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艺
|
5799 |
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艽
|
5800 |
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艾
|
5801 |
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艿
|
5802 |
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节
|
5803 |
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芃
|
5804 |
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芄
|
5805 |
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芈
|
5806 |
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芊
|
5807 |
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芋
|
5808 |
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芍
|
5809 |
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芎
|
5810 |
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芏
|
5811 |
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芑
|
5812 |
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芒
|
5813 |
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芗
|
5814 |
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芘
|
5815 |
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芙
|
5816 |
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芜
|
5817 |
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芝
|
5818 |
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芟
|
5819 |
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芡
|
5820 |
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芣
|
5821 |
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芤
|
5822 |
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芥
|
5823 |
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芦
|
5824 |
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芨
|
5825 |
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芩
|
5826 |
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芪
|
5827 |
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芫
|
5828 |
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芬
|
5829 |
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芭
|
5830 |
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芮
|
5831 |
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芯
|
5832 |
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芰
|
5833 |
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花
|
5834 |
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芳
|
5835 |
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芴
|
5836 |
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芶
|
5837 |
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芷
|
5838 |
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芸
|
5839 |
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芹
|
5840 |
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芽
|
5841 |
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芾
|
5842 |
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苁
|
5843 |
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苄
|
5844 |
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苇
|
5845 |
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苈
|
5846 |
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苊
|
5847 |
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苋
|
5848 |
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苌
|
5849 |
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苍
|
5850 |
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苎
|
5851 |
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苏
|
5852 |
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苑
|
5853 |
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苒
|
5854 |
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苓
|
5855 |
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苔
|
5856 |
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苕
|
5857 |
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苗
|
5858 |
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苘
|
5859 |
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苛
|
5860 |
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苜
|
5861 |
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苞
|
5862 |
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苟
|
5863 |
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苡
|
5864 |
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苢
|
5865 |
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苣
|
5866 |
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苤
|
5867 |
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若
|
5868 |
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苦
|
5869 |
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苫
|
5870 |
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苭
|
5871 |
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苯
|
5872 |
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英
|
5873 |
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苴
|
5874 |
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苷
|
5875 |
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苹
|
5876 |
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苺
|
5877 |
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苻
|
5878 |
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苼
|
5879 |
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苾
|
5880 |
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茀
|
5881 |
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茁
|
5882 |
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茂
|
5883 |
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范
|
5884 |
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茄
|
5885 |
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茅
|
5886 |
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茆
|
5887 |
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茇
|
5888 |
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茈
|
5889 |
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茉
|
5890 |
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茌
|
5891 |
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茎
|
5892 |
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茏
|
5893 |
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茑
|
5894 |
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茔
|
5895 |
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茕
|
5896 |
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茗
|
5897 |
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茚
|
5898 |
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茛
|
5899 |
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茜
|
5900 |
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茝
|
5901 |
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茧
|
5902 |
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茨
|
5903 |
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茫
|
5904 |
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茬
|
5905 |
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茭
|
5906 |
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茯
|
5907 |
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茱
|
5908 |
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茳
|
5909 |
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茴
|
5910 |
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茵
|
5911 |
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茶
|
5912 |
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茸
|
5913 |
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茹
|
5914 |
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茺
|
5915 |
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茼
|
5916 |
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荀
|
5917 |
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荃
|
5918 |
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荄
|
5919 |
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荅
|
5920 |
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荆
|
5921 |
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荇
|
5922 |
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荈
|
5923 |
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草
|
5924 |
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荏
|
5925 |
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荐
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5926 |
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荑
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5927 |
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荒
|
5928 |
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荔
|
5929 |
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荖
|
5930 |
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荘
|
5931 |
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荚
|
5932 |
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荛
|
5933 |
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荜
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5934 |
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荞
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5935 |
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荟
|
5936 |
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荠
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5937 |
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荡
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5938 |
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荣
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5939 |
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荤
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5940 |
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荥
|
5941 |
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荦
|
5942 |
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荧
|
5943 |
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荨
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5944 |
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荩
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5945 |
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荪
|
5946 |
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荫
|
5947 |
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荬
|
5948 |
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荭
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5949 |
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荮
|
5950 |
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药
|
5951 |
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荳
|
5952 |
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荷
|
5953 |
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荸
|
5954 |
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荻
|
5955 |
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荼
|
5956 |
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荽
|
5957 |
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莃
|
5958 |
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莅
|
5959 |
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莆
|
5960 |
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莉
|
5961 |
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莎
|
5962 |
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莒
|
5963 |
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莓
|
5964 |
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莘
|
5965 |
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莙
|
5966 |
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莛
|
5967 |
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莜
|
5968 |
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莞
|
5969 |
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莠
|
5970 |
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莨
|
5971 |
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莩
|
5972 |
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莪
|
5973 |
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莫
|
5974 |
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莱
|
5975 |
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莲
|
5976 |
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莳
|
5977 |
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莴
|
5978 |
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莶
|
5979 |
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获
|
5980 |
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莸
|
5981 |
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莹
|
5982 |
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莺
|
5983 |
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莼
|
5984 |
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莽
|
5985 |
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菀
|
5986 |
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菁
|
5987 |
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菂
|
5988 |
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菅
|
5989 |
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菇
|
5990 |
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菈
|
5991 |
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菉
|
5992 |
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菊
|
5993 |
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菌
|
5994 |
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菏
|
5995 |
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菑
|
5996 |
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菓
|
5997 |
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菔
|
5998 |
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菖
|
5999 |
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菘
|
6000 |
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菜
|
6001 |
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菝
|
6002 |
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菟
|
6003 |
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菠
|
6004 |
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菡
|
6005 |
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菩
|
6006 |
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菪
|
6007 |
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菫
|
6008 |
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菰
|
6009 |
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菱
|
6010 |
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菲
|
6011 |
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菸
|
6012 |
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菹
|
6013 |
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菽
|
6014 |
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菿
|
6015 |
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萁
|
6016 |
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萃
|
6017 |
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萄
|
6018 |
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萆
|
6019 |
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萋
|
6020 |
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萌
|
6021 |
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萍
|
6022 |
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萎
|
6023 |
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萏
|
6024 |
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萑
|
6025 |
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萘
|
6026 |
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萜
|
6027 |
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萝
|
6028 |
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萢
|
6029 |
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萤
|
6030 |
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营
|
6031 |
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萦
|
6032 |
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萧
|
6033 |
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萨
|
6034 |
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萩
|
6035 |
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萱
|
6036 |
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萸
|
6037 |
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萼
|
6038 |
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落
|
6039 |
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葆
|
6040 |
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葎
|
6041 |
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葑
|
6042 |
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葖
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6043 |
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著
|
6044 |
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葙
|
6045 |
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葚
|
6046 |
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葛
|
6047 |
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葜
|
6048 |
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葡
|
6049 |
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葢
|
6050 |
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董
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6051 |
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葩
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6052 |
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葫
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6053 |
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葬
|
6054 |
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葭
|
6055 |
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葱
|
6056 |
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葳
|
6057 |
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葵
|
6058 |
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葶
|
6059 |
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葸
|
6060 |
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葺
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6061 |
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蒂
|
6062 |
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蒇
|
6063 |
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蒉
|
6064 |
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蒋
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6065 |
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蒌
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6066 |
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蒍
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6067 |
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蒎
|
6068 |
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蒐
|
6069 |
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蒗
|
6070 |
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蒙
|
6071 |
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蒜
|
6072 |
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蒟
|
6073 |
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蒡
|
6074 |
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蒨
|
6075 |
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蒯
|
6076 |
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蒲
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6077 |
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蒴
|
6078 |
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蒸
|
6079 |
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蒹
|
6080 |
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蒺
|
6081 |
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蒻
|
6082 |
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蒽
|
6083 |
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蒾
|
6084 |
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蒿
|
6085 |
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蓁
|
6086 |
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蓂
|
6087 |
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蓄
|
6088 |
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蓇
|
6089 |
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蓉
|
6090 |
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蓊
|
6091 |
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蓍
|
6092 |
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蓐
|
6093 |
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蓑
|
6094 |
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蓓
|
6095 |
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蓖
|
6096 |
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蓝
|
6097 |
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蓟
|
6098 |
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蓠
|
6099 |
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蓢
|
6100 |
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蓣
|
6101 |
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蓥
|
6102 |
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蓦
|
6103 |
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蓬
|
6104 |
+
蓼
|
6105 |
+
蓿
|
6106 |
+
蔀
|
6107 |
+
蔌
|
6108 |
+
蔑
|
6109 |
+
蔓
|
6110 |
+
蔗
|
6111 |
+
蔘
|
6112 |
+
蔚
|
6113 |
+
蔟
|
6114 |
+
蔡
|
6115 |
+
蔫
|
6116 |
+
蔬
|
6117 |
+
蔴
|
6118 |
+
蔵
|
6119 |
+
蔷
|
6120 |
+
蔸
|
6121 |
+
蔹
|
6122 |
+
蔺
|
6123 |
+
蔻
|
6124 |
+
蔼
|
6125 |
+
蔽
|
6126 |
+
蕃
|
6127 |
+
蕅
|
6128 |
+
蕈
|
6129 |
+
蕉
|
6130 |
+
蕊
|
6131 |
+
蕖
|
6132 |
+
蕗
|
6133 |
+
蕙
|
6134 |
+
蕞
|
6135 |
+
蕡
|
6136 |
+
蕤
|
6137 |
+
蕨
|
6138 |
+
蕰
|
6139 |
+
蕲
|
6140 |
+
蕴
|
6141 |
+
蕹
|
6142 |
+
蕺
|
6143 |
+
蕻
|
6144 |
+
蕾
|
6145 |
+
薄
|
6146 |
+
薅
|
6147 |
+
薆
|
6148 |
+
薇
|
6149 |
+
薏
|
6150 |
+
薙
|
6151 |
+
薛
|
6152 |
+
薜
|
6153 |
+
薢
|
6154 |
+
薤
|
6155 |
+
薨
|
6156 |
+
薪
|
6157 |
+
薫
|
6158 |
+
薬
|
6159 |
+
薮
|
6160 |
+
薯
|
6161 |
+
薰
|
6162 |
+
薷
|
6163 |
+
薹
|
6164 |
+
藁
|
6165 |
+
藉
|
6166 |
+
藏
|
6167 |
+
藐
|
6168 |
+
藓
|
6169 |
+
藕
|
6170 |
+
藜
|
6171 |
+
藟
|
6172 |
+
藠
|
6173 |
+
藤
|
6174 |
+
藦
|
6175 |
+
藨
|
6176 |
+
藩
|
6177 |
+
藻
|
6178 |
+
藿
|
6179 |
+
蘅
|
6180 |
+
蘑
|
6181 |
+
蘖
|
6182 |
+
蘧
|
6183 |
+
蘩
|
6184 |
+
蘸
|
6185 |
+
蘼
|
6186 |
+
虎
|
6187 |
+
虏
|
6188 |
+
虐
|
6189 |
+
虑
|
6190 |
+
虒
|
6191 |
+
虓
|
6192 |
+
虔
|
6193 |
+
虚
|
6194 |
+
虞
|
6195 |
+
虢
|
6196 |
+
虫
|
6197 |
+
虬
|
6198 |
+
虮
|
6199 |
+
虱
|
6200 |
+
虹
|
6201 |
+
虺
|
6202 |
+
虻
|
6203 |
+
虽
|
6204 |
+
虾
|
6205 |
+
虿
|
6206 |
+
蚀
|
6207 |
+
蚁
|
6208 |
+
蚂
|
6209 |
+
蚊
|
6210 |
+
蚋
|
6211 |
+
蚌
|
6212 |
+
蚍
|
6213 |
+
蚓
|
6214 |
+
蚕
|
6215 |
+
蚖
|
6216 |
+
蚜
|
6217 |
+
蚝
|
6218 |
+
蚡
|
6219 |
+
蚣
|
6220 |
+
蚤
|
6221 |
+
蚧
|
6222 |
+
蚨
|
6223 |
+
蚩
|
6224 |
+
蚪
|
6225 |
+
蚬
|
6226 |
+
蚯
|
6227 |
+
蚰
|
6228 |
+
蚱
|
6229 |
+
蚴
|
6230 |
+
蚵
|
6231 |
+
蚶
|
6232 |
+
蚺
|
6233 |
+
蛀
|
6234 |
+
蛄
|
6235 |
+
蛆
|
6236 |
+
蛇
|
6237 |
+
蛉
|
6238 |
+
蛊
|
6239 |
+
蛋
|
6240 |
+
蛎
|
6241 |
+
蛏
|
6242 |
+
蛐
|
6243 |
+
蛔
|
6244 |
+
蛙
|
6245 |
+
蛛
|
6246 |
+
蛞
|
6247 |
+
蛟
|
6248 |
+
蛤
|
6249 |
+
蛩
|
6250 |
+
蛭
|
6251 |
+
蛮
|
6252 |
+
蛰
|
6253 |
+
蛱
|
6254 |
+
蛲
|
6255 |
+
蛳
|
6256 |
+
蛴
|
6257 |
+
蛸
|
6258 |
+
蛹
|
6259 |
+
蛾
|
6260 |
+
蜀
|
6261 |
+
蜂
|
6262 |
+
蜃
|
6263 |
+
蜇
|
6264 |
+
蜈
|
6265 |
+
蜉
|
6266 |
+
蜊
|
6267 |
+
蜍
|
6268 |
+
蜑
|
6269 |
+
蜒
|
6270 |
+
蜓
|
6271 |
+
蜕
|
6272 |
+
蜗
|
6273 |
+
蜘
|
6274 |
+
蜚
|
6275 |
+
蜜
|
6276 |
+
蜞
|
6277 |
+
蜡
|
6278 |
+
蜢
|
6279 |
+
蜣
|
6280 |
+
蜥
|
6281 |
+
蜩
|
6282 |
+
蜮
|
6283 |
+
蜱
|
6284 |
+
蜴
|
6285 |
+
蜷
|
6286 |
+
蜻
|
6287 |
+
蜾
|
6288 |
+
蜿
|
6289 |
+
蝇
|
6290 |
+
蝈
|
6291 |
+
蝉
|
6292 |
+
蝌
|
6293 |
+
蝎
|
6294 |
+
蝓
|
6295 |
+
蝗
|
6296 |
+
蝙
|
6297 |
+
蝠
|
6298 |
+
蝣
|
6299 |
+
蝤
|
6300 |
+
蝥
|
6301 |
+
蝮
|
6302 |
+
蝰
|
6303 |
+
蝲
|
6304 |
+
蝴
|
6305 |
+
蝶
|
6306 |
+
蝻
|
6307 |
+
蝼
|
6308 |
+
蝽
|
6309 |
+
蝾
|
6310 |
+
螂
|
6311 |
+
螃
|
6312 |
+
螅
|
6313 |
+
螈
|
6314 |
+
螋
|
6315 |
+
融
|
6316 |
+
螓
|
6317 |
+
螟
|
6318 |
+
螣
|
6319 |
+
螨
|
6320 |
+
螫
|
6321 |
+
螬
|
6322 |
+
螭
|
6323 |
+
螯
|
6324 |
+
螳
|
6325 |
+
螵
|
6326 |
+
螺
|
6327 |
+
螽
|
6328 |
+
蟀
|
6329 |
+
蟆
|
6330 |
+
蟊
|
6331 |
+
蟋
|
6332 |
+
蟌
|
6333 |
+
蟑
|
6334 |
+
蟒
|
6335 |
+
蟛
|
6336 |
+
蟜
|
6337 |
+
蟠
|
6338 |
+
蟥
|
6339 |
+
蟪
|
6340 |
+
蟮
|
6341 |
+
蟳
|
6342 |
+
蟹
|
6343 |
+
蟾
|
6344 |
+
蠃
|
6345 |
+
蠊
|
6346 |
+
蠋
|
6347 |
+
蠍
|
6348 |
+
蠓
|
6349 |
+
蠕
|
6350 |
+
蠖
|
6351 |
+
蠡
|
6352 |
+
蠢
|
6353 |
+
蠲
|
6354 |
+
蠹
|
6355 |
+
蠼
|
6356 |
+
血
|
6357 |
+
衄
|
6358 |
+
衅
|
6359 |
+
衆
|
6360 |
+
行
|
6361 |
+
衍
|
6362 |
+
衎
|
6363 |
+
衔
|
6364 |
+
街
|
6365 |
+
衙
|
6366 |
+
衞
|
6367 |
+
衡
|
6368 |
+
衢
|
6369 |
+
衣
|
6370 |
+
补
|
6371 |
+
表
|
6372 |
+
衩
|
6373 |
+
衪
|
6374 |
+
衫
|
6375 |
+
衬
|
6376 |
+
衮
|
6377 |
+
衰
|
6378 |
+
衲
|
6379 |
+
衷
|
6380 |
+
衽
|
6381 |
+
衾
|
6382 |
+
衿
|
6383 |
+
袁
|
6384 |
+
袂
|
6385 |
+
袄
|
6386 |
+
袅
|
6387 |
+
袆
|
6388 |
+
袈
|
6389 |
+
袋
|
6390 |
+
袍
|
6391 |
+
袒
|
6392 |
+
袓
|
6393 |
+
袖
|
6394 |
+
袛
|
6395 |
+
袜
|
6396 |
+
袢
|
6397 |
+
袤
|
6398 |
+
袪
|
6399 |
+
被
|
6400 |
+
袭
|
6401 |
+
袮
|
6402 |
+
袱
|
6403 |
+
袴
|
6404 |
+
袷
|
6405 |
+
袼
|
6406 |
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裁
|
6407 |
+
裂
|
6408 |
+
装
|
6409 |
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裆
|
6410 |
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裈
|
6411 |
+
裉
|
6412 |
+
裒
|
6413 |
+
裔
|
6414 |
+
裕
|
6415 |
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裘
|
6416 |
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裙
|
6417 |
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裛
|
6418 |
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裟
|
6419 |
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裡
|
6420 |
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裢
|
6421 |
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裤
|
6422 |
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裥
|
6423 |
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裨
|
6424 |
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裪
|
6425 |
+
裱
|
6426 |
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裳
|
6427 |
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裴
|
6428 |
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裸
|
6429 |
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裹
|
6430 |
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裼
|
6431 |
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裾
|
6432 |
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褀
|
6433 |
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褂
|
6434 |
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褆
|
6435 |
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褊
|
6436 |
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褐
|
6437 |
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褒
|
6438 |
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褓
|
6439 |
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褔
|
6440 |
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褙
|
6441 |
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褚
|
6442 |
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褛
|
6443 |
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褡
|
6444 |
+
褥
|
6445 |
+
褪
|
6446 |
+
褫
|
6447 |
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褰
|
6448 |
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褴
|
6449 |
+
褶
|
6450 |
+
襁
|
6451 |
+
襄
|
6452 |
+
襌
|
6453 |
+
襕
|
6454 |
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襜
|
6455 |
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襞
|
6456 |
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襟
|
6457 |
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襦
|
6458 |
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襻
|
6459 |
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西
|
6460 |
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要
|
6461 |
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覃
|
6462 |
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覆
|
6463 |
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覇
|
6464 |
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覚
|
6465 |
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覧
|
6466 |
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覩
|
6467 |
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観
|
6468 |
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见
|
6469 |
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观
|
6470 |
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规
|
6471 |
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觅
|
6472 |
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视
|
6473 |
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觇
|
6474 |
+
览
|
6475 |
+
觉
|
6476 |
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觊
|
6477 |
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觋
|
6478 |
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觌
|
6479 |
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觎
|
6480 |
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觏
|
6481 |
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觐
|
6482 |
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觑
|
6483 |
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角
|
6484 |
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觚
|
6485 |
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觜
|
6486 |
+
觞
|
6487 |
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解
|
6488 |
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觥
|
6489 |
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触
|
6490 |
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觯
|
6491 |
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觳
|
6492 |
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觽
|
6493 |
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觿
|
6494 |
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言
|
6495 |
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訇
|
6496 |
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訏
|
6497 |
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訚
|
6498 |
+
訫
|
6499 |
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訳
|
6500 |
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訾
|
6501 |
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詈
|
6502 |
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詜
|
6503 |
+
詝
|
6504 |
+
詧
|
6505 |
+
詹
|
6506 |
+
誉
|
6507 |
+
誊
|
6508 |
+
誐
|
6509 |
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誓
|
6510 |
+
説
|
6511 |
+
読
|
6512 |
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諡
|
6513 |
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諲
|
6514 |
+
諴
|
6515 |
+
謇
|
6516 |
+
謦
|
6517 |
+
譞
|
6518 |
+
警
|
6519 |
+
譬
|
6520 |
+
譲
|
6521 |
+
讌
|
6522 |
+
讠
|
6523 |
+
计
|
6524 |
+
订
|
6525 |
+
讣
|
6526 |
+
认
|
6527 |
+
讥
|
6528 |
+
讦
|
6529 |
+
讧
|
6530 |
+
讨
|
6531 |
+
让
|
6532 |
+
讪
|
6533 |
+
讫
|
6534 |
+
讬
|
6535 |
+
训
|
6536 |
+
议
|
6537 |
+
讯
|
6538 |
+
记
|
6539 |
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讲
|
6540 |
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讳
|
6541 |
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讴
|
6542 |
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讵
|
6543 |
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讶
|
6544 |
+
讷
|
6545 |
+
许
|
6546 |
+
讹
|
6547 |
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论
|
6548 |
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讼
|
6549 |
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讽
|
6550 |
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设
|
6551 |
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访
|
6552 |
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诀
|
6553 |
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证
|
6554 |
+
诂
|
6555 |
+
诃
|
6556 |
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评
|
6557 |
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诅
|
6558 |
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识
|
6559 |
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诈
|
6560 |
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诉
|
6561 |
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诊
|
6562 |
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诋
|
6563 |
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诌
|
6564 |
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词
|
6565 |
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诎
|
6566 |
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诏
|
6567 |
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译
|
6568 |
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诒
|
6569 |
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诓
|
6570 |
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诔
|
6571 |
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试
|
6572 |
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诖
|
6573 |
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诗
|
6574 |
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诘
|
6575 |
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诙
|
6576 |
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诚
|
6577 |
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诛
|
6578 |
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诜
|
6579 |
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话
|
6580 |
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诞
|
6581 |
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诟
|
6582 |
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诠
|
6583 |
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诡
|
6584 |
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询
|
6585 |
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诣
|
6586 |
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诤
|
6587 |
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该
|
6588 |
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详
|
6589 |
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诧
|
6590 |
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诨
|
6591 |
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诩
|
6592 |
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诫
|
6593 |
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诬
|
6594 |
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语
|
6595 |
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诮
|
6596 |
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误
|
6597 |
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诰
|
6598 |
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诱
|
6599 |
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诲
|
6600 |
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诳
|
6601 |
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说
|
6602 |
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诵
|
6603 |
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诶
|
6604 |
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请
|
6605 |
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诸
|
6606 |
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诹
|
6607 |
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诺
|
6608 |
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读
|
6609 |
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诼
|
6610 |
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诽
|
6611 |
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课
|
6612 |
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诿
|
6613 |
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谀
|
6614 |
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谁
|
6615 |
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谂
|
6616 |
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调
|
6617 |
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谄
|
6618 |
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谅
|
6619 |
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谆
|
6620 |
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谇
|
6621 |
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谈
|
6622 |
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谊
|
6623 |
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谋
|
6624 |
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谌
|
6625 |
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谍
|
6626 |
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谎
|
6627 |
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谏
|
6628 |
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谐
|
6629 |
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谑
|
6630 |
+
谒
|
6631 |
+
谓
|
6632 |
+
谔
|
6633 |
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谕
|
6634 |
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谖
|
6635 |
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谗
|
6636 |
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谘
|
6637 |
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谙
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6638 |
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谚
|
6639 |
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谛
|
6640 |
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谜
|
6641 |
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谝
|
6642 |
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谞
|
6643 |
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谟
|
6644 |
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谠
|
6645 |
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谡
|
6646 |
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谢
|
6647 |
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谣
|
6648 |
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谤
|
6649 |
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谥
|
6650 |
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谦
|
6651 |
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谧
|
6652 |
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谨
|
6653 |
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谩
|
6654 |
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谪
|
6655 |
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谫
|
6656 |
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谬
|
6657 |
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谭
|
6658 |
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谮
|
6659 |
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谯
|
6660 |
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谰
|
6661 |
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谱
|
6662 |
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谲
|
6663 |
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谳
|
6664 |
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谴
|
6665 |
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谵
|
6666 |
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谶
|
6667 |
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谷
|
6668 |
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谿
|
6669 |
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豁
|
6670 |
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豆
|
6671 |
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豇
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6672 |
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豉
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6673 |
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豊
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6674 |
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豌
|
6675 |
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豕
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6676 |
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豚
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6677 |
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象
|
6678 |
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豢
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6679 |
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豨
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6680 |
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豪
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6681 |
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豫
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6682 |
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豳
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6683 |
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豸
|
6684 |
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豹
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6685 |
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豺
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6686 |
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貂
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6687 |
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貅
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6688 |
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貉
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6689 |
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貊
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6690 |
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貌
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6691 |
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貐
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6692 |
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貔
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6693 |
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貘
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6694 |
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賨
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6695 |
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賸
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6696 |
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贇
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6697 |
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贝
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6698 |
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贞
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6699 |
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负
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6700 |
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贠
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6701 |
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贡
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6702 |
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财
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6703 |
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责
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6704 |
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贤
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6705 |
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败
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6706 |
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账
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6707 |
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货
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6708 |
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质
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6709 |
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贩
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6710 |
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贪
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6711 |
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贫
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6712 |
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贬
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6713 |
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购
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6714 |
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贮
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6715 |
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贯
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6716 |
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贰
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6717 |
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贱
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6718 |
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贲
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6719 |
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贳
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6720 |
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贴
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6721 |
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贵
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6722 |
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贶
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6723 |
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贷
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6724 |
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贸
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6725 |
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费
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6726 |
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贺
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6727 |
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贻
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6728 |
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贼
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6729 |
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贽
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6730 |
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贾
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6731 |
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贿
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6732 |
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赀
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6733 |
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赁
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6734 |
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赂
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6735 |
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赃
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6736 |
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资
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6737 |
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赅
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6738 |
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赈
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6739 |
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赉
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6740 |
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赊
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6741 |
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赋
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6742 |
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赌
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6743 |
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赍
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6744 |
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赎
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6745 |
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赏
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6746 |
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赐
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6747 |
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赑
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6748 |
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赓
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6749 |
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赔
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6750 |
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赕
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6751 |
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赖
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6752 |
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赘
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6753 |
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赙
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6754 |
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赚
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6755 |
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赛
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6756 |
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赜
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6757 |
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赝
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6758 |
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赞
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6759 |
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赟
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6760 |
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赠
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6761 |
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赡
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6762 |
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赢
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6763 |
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赣
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6764 |
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赤
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6765 |
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赦
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6766 |
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赧
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6767 |
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赪
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6768 |
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赫
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6769 |
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赭
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6770 |
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赮
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6771 |
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走
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6772 |
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赳
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6773 |
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赴
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6774 |
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赵
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6775 |
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赶
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6776 |
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起
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6777 |
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趁
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6778 |
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趄
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6779 |
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超
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6780 |
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越
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6781 |
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趋
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6782 |
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趔
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6783 |
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趟
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6784 |
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趣
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6785 |
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趯
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6786 |
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趱
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6787 |
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足
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6788 |
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趴
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6789 |
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趵
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6790 |
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趸
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6791 |
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趺
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6792 |
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趼
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6793 |
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趾
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6794 |
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趿
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6795 |
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跂
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6796 |
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跃
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6797 |
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跄
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6798 |
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跆
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6799 |
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跋
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6800 |
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跌
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6801 |
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跎
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6802 |
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跏
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6803 |
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跑
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6804 |
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跖
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6805 |
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跗
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6806 |
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跚
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6807 |
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跛
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6808 |
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距
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6809 |
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跞
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6810 |
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跟
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6811 |
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跢
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6812 |
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跣
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6813 |
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跤
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6814 |
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跨
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6815 |
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跩
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6816 |
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跪
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6817 |
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跫
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6818 |
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跬
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6819 |
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路
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6820 |
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跳
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6821 |
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践
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6822 |
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跶
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6823 |
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跷
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6824 |
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跸
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6825 |
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跹
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6826 |
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跺
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6827 |
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跻
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6828 |
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跽
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6829 |
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6830 |
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踉
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6831 |
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踊
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6832 |
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踌
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6833 |
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踏
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6834 |
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踔
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6835 |
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踝
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6836 |
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踞
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6837 |
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踟
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6838 |
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踢
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6839 |
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踣
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6840 |
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踩
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6841 |
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踪
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6842 |
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踬
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6843 |
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踮
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6844 |
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踯
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6845 |
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踰
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6846 |
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踱
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6847 |
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踵
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6848 |
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踹
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6849 |
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踽
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6850 |
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6851 |
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蹁
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6852 |
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6853 |
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蹄
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6854 |
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蹇
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6855 |
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蹈
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6856 |
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蹉
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6857 |
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蹊
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6858 |
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蹋
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6859 |
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蹑
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6860 |
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蹒
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6861 |
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蹓
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6862 |
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蹙
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6863 |
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蹚
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6864 |
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蹟
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6865 |
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6866 |
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蹩
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6867 |
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蹬
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6868 |
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蹭
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6869 |
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蹰
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6870 |
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蹲
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6871 |
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蹴
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6872 |
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蹶
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6873 |
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6874 |
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蹼
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6875 |
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蹿
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6876 |
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躁
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6877 |
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躄
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6878 |
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躅
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6879 |
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躇
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6880 |
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躏
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6881 |
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躐
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6882 |
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躔
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6883 |
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躜
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6884 |
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躞
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6885 |
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身
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6886 |
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躬
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6887 |
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躯
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6888 |
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躲
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6889 |
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躺
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6890 |
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転
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6891 |
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軽
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6892 |
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輋
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6893 |
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轘
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6894 |
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车
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6895 |
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轧
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6896 |
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轨
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6897 |
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轩
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6898 |
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轫
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6899 |
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转
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6900 |
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6901 |
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轮
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6902 |
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软
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6903 |
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轰
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6904 |
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轱
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6905 |
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轲
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6906 |
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轳
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6907 |
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轴
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6908 |
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轵
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6909 |
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轶
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6910 |
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轸
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6911 |
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轹
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6912 |
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轺
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6913 |
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轻
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6914 |
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轼
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6915 |
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载
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6916 |
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轾
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6917 |
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轿
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6918 |
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辂
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6919 |
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较
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6920 |
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辄
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6921 |
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辅
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6922 |
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辆
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6923 |
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辇
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6924 |
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辈
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6925 |
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辉
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6926 |
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辊
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6927 |
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辋
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6928 |
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辍
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6929 |
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辎
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6930 |
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辏
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6931 |
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辐
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6932 |
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辑
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6933 |
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输
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6934 |
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辔
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6935 |
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辕
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6936 |
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辖
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6937 |
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辗
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6938 |
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辘
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6939 |
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辙
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6940 |
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辚
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6941 |
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辛
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6942 |
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辜
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6943 |
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辞
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6944 |
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辟
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6945 |
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辣
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6946 |
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辨
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6947 |
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辩
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6948 |
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辫
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6949 |
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辰
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6950 |
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辱
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6951 |
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辶
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6952 |
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边
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6953 |
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辺
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6954 |
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辻
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6955 |
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込
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6956 |
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辽
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6957 |
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达
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6958 |
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辿
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6959 |
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迁
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6960 |
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迂
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6961 |
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迄
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6962 |
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迅
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6963 |
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过
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6964 |
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迈
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6965 |
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6966 |
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迎
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6967 |
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运
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6968 |
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近
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6969 |
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迓
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6970 |
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返
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6971 |
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迕
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6972 |
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还
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6973 |
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这
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6974 |
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进
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6975 |
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远
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6976 |
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违
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6977 |
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连
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6978 |
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迟
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6979 |
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迢
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6980 |
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迤
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6981 |
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迥
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6982 |
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迦
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6983 |
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迨
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6984 |
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迩
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6985 |
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迪
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6986 |
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迫
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6987 |
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迭
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6988 |
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迮
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6989 |
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述
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6990 |
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迳
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6991 |
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迷
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6992 |
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迸
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6993 |
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迹
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6994 |
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追
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退
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送
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适
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逃
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6999 |
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逄
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逅
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逆
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7002 |
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选
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逊
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逋
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逍
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透
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逐
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逑
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递
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途
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7011 |
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逖
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7012 |
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逗
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這
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通
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7015 |
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逛
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7016 |
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逝
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逞
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7018 |
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速
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造
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7020 |
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逡
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7021 |
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逢
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7022 |
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逦
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逭
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逮
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逯
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進
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逵
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逶
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逸
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逹
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7031 |
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逺
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逻
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7033 |
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逼
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7034 |
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逾
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7035 |
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遁
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7036 |
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遂
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遄
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7038 |
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遇
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7039 |
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遍
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7040 |
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遏
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7041 |
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遐
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7042 |
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遑
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7043 |
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遒
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7044 |
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道
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7045 |
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遗
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7046 |
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遘
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7047 |
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遛
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7048 |
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遢
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7049 |
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遣
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7050 |
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遥
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遨
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7052 |
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遭
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7053 |
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遮
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7054 |
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遯
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7055 |
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遴
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7056 |
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遵
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7057 |
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遶
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遹
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遽
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7060 |
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避
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7061 |
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7062 |
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邂
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7063 |
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邃
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7064 |
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邅
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7065 |
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邈
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7066 |
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邉
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7067 |
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邋
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7068 |
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邑
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7069 |
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邓
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7070 |
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邕
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7071 |
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邗
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7072 |
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邙
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7073 |
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邛
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7074 |
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邝
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7075 |
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邠
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7076 |
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邡
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7077 |
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邢
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7078 |
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那
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7079 |
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邦
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7080 |
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邨
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7081 |
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邪
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7082 |
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邬
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7083 |
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邮
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7084 |
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邯
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7085 |
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邰
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7086 |
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邱
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7087 |
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邳
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7088 |
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邴
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7089 |
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邵
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7090 |
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邶
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7091 |
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邸
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7092 |
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邹
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7093 |
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邺
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7094 |
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邻
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7095 |
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邽
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7096 |
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邾
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7097 |
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郁
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7098 |
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郄
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7099 |
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郅
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7100 |
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郇
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7101 |
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郈
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7102 |
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郊
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7103 |
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郎
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7104 |
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郏
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7105 |
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郐
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7106 |
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郑
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7107 |
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郓
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7108 |
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郕
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7109 |
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郗
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7110 |
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郚
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7111 |
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郛
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7112 |
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郜
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7113 |
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郝
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7114 |
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郞
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7115 |
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郡
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7116 |
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郢
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7117 |
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郤
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7118 |
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郦
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7119 |
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郧
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7120 |
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部
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7121 |
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郪
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7122 |
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郫
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7123 |
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郭
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7124 |
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郯
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7125 |
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郴
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7126 |
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郷
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7127 |
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郸
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都
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郾
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7130 |
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郿
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7131 |
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7132 |
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鄂
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7133 |
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鄄
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7135 |
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鄘
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鄙
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鄞
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7140 |
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鄠
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鄢
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7142 |
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鄣
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7143 |
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鄩
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7144 |
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鄫
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7145 |
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鄮
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7146 |
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鄯
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7147 |
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鄱
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7148 |
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鄹
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7149 |
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酂
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7150 |
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酃
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7151 |
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酆
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7152 |
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酉
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7153 |
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酊
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7154 |
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酋
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7155 |
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酌
|
7156 |
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配
|
7157 |
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酎
|
7158 |
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酐
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7159 |
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酒
|
7160 |
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酗
|
7161 |
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酚
|
7162 |
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酝
|
7163 |
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酞
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7164 |
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酡
|
7165 |
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酢
|
7166 |
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酣
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7167 |
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酤
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7168 |
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酥
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7169 |
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酩
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7170 |
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酪
|
7171 |
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酬
|
7172 |
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酮
|
7173 |
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酯
|
7174 |
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酰
|
7175 |
+
酱
|
7176 |
+
酲
|
7177 |
+
酴
|
7178 |
+
酵
|
7179 |
+
酶
|
7180 |
+
酷
|
7181 |
+
酸
|
7182 |
+
酹
|
7183 |
+
酺
|
7184 |
+
酽
|
7185 |
+
酾
|
7186 |
+
酿
|
7187 |
+
醂
|
7188 |
+
醅
|
7189 |
+
醇
|
7190 |
+
醉
|
7191 |
+
醋
|
7192 |
+
醌
|
7193 |
+
醍
|
7194 |
+
醐
|
7195 |
+
醑
|
7196 |
+
醒
|
7197 |
+
醚
|
7198 |
+
醛
|
7199 |
+
醢
|
7200 |
+
醣
|
7201 |
+
醪
|
7202 |
+
醭
|
7203 |
+
醮
|
7204 |
+
醯
|
7205 |
+
醴
|
7206 |
+
醵
|
7207 |
+
醺
|
7208 |
+
醿
|
7209 |
+
釆
|
7210 |
+
采
|
7211 |
+
釉
|
7212 |
+
释
|
7213 |
+
里
|
7214 |
+
重
|
7215 |
+
野
|
7216 |
+
量
|
7217 |
+
金
|
7218 |
+
釜
|
7219 |
+
釭
|
7220 |
+
釿
|
7221 |
+
鈇
|
7222 |
+
鈈
|
7223 |
+
鈊
|
7224 |
+
鈎
|
7225 |
+
鈡
|
7226 |
+
鉄
|
7227 |
+
鉏
|
7228 |
+
鉨
|
7229 |
+
鉴
|
7230 |
+
鉷
|
7231 |
+
銎
|
7232 |
+
銙
|
7233 |
+
銭
|
7234 |
+
銮
|
7235 |
+
鋆
|
7236 |
+
鋈
|
7237 |
+
鋐
|
7238 |
+
鋗
|
7239 |
+
鋬
|
7240 |
+
鋹
|
7241 |
+
錞
|
7242 |
+
錡
|
7243 |
+
錤
|
7244 |
+
録
|
7245 |
+
錾
|
7246 |
+
鍀
|
7247 |
+
鍪
|
7248 |
+
鎌
|
7249 |
+
鎏
|
7250 |
+
鎚
|
7251 |
+
鏊
|
7252 |
+
鏐
|
7253 |
+
鏖
|
7254 |
+
鐏
|
7255 |
+
鑨
|
7256 |
+
鑫
|
7257 |
+
钅
|
7258 |
+
钆
|
7259 |
+
钇
|
7260 |
+
针
|
7261 |
+
钉
|
7262 |
+
钊
|
7263 |
+
钋
|
7264 |
+
钌
|
7265 |
+
钍
|
7266 |
+
钎
|
7267 |
+
钏
|
7268 |
+
钐
|
7269 |
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钒
|
7270 |
+
钓
|
7271 |
+
钔
|
7272 |
+
钕
|
7273 |
+
钖
|
7274 |
+
钗
|
7275 |
+
钙
|
7276 |
+
钚
|
7277 |
+
钛
|
7278 |
+
钜
|
7279 |
+
钝
|
7280 |
+
钞
|
7281 |
+
钟
|
7282 |
+
钠
|
7283 |
+
钡
|
7284 |
+
钢
|
7285 |
+
钣
|
7286 |
+
钤
|
7287 |
+
钥
|
7288 |
+
钦
|
7289 |
+
钧
|
7290 |
+
钨
|
7291 |
+
钩
|
7292 |
+
钪
|
7293 |
+
钫
|
7294 |
+
钬
|
7295 |
+
钭
|
7296 |
+
钮
|
7297 |
+
钯
|
7298 |
+
钰
|
7299 |
+
钱
|
7300 |
+
钲
|
7301 |
+
钳
|
7302 |
+
钴
|
7303 |
+
钵
|
7304 |
+
钶
|
7305 |
+
钷
|
7306 |
+
钹
|
7307 |
+
钺
|
7308 |
+
钻
|
7309 |
+
钼
|
7310 |
+
钽
|
7311 |
+
钾
|
7312 |
+
钿
|
7313 |
+
铀
|
7314 |
+
铁
|
7315 |
+
铂
|
7316 |
+
铃
|
7317 |
+
铄
|
7318 |
+
铅
|
7319 |
+
铆
|
7320 |
+
铈
|
7321 |
+
铉
|
7322 |
+
铊
|
7323 |
+
铋
|
7324 |
+
铌
|
7325 |
+
铍
|
7326 |
+
铎
|
7327 |
+
铐
|
7328 |
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铑
|
7329 |
+
铒
|
7330 |
+
铓
|
7331 |
+
铕
|
7332 |
+
铖
|
7333 |
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铗
|
7334 |
+
铙
|
7335 |
+
铚
|
7336 |
+
铛
|
7337 |
+
铜
|
7338 |
+
铝
|
7339 |
+
铟
|
7340 |
+
铠
|
7341 |
+
铡
|
7342 |
+
铢
|
7343 |
+
铣
|
7344 |
+
铤
|
7345 |
+
铥
|
7346 |
+
铦
|
7347 |
+
铧
|
7348 |
+
铨
|
7349 |
+
铩
|
7350 |
+
铪
|
7351 |
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铫
|
7352 |
+
铬
|
7353 |
+
铭
|
7354 |
+
铮
|
7355 |
+
铯
|
7356 |
+
铰
|
7357 |
+
铱
|
7358 |
+
铲
|
7359 |
+
铳
|
7360 |
+
铵
|
7361 |
+
银
|
7362 |
+
铷
|
7363 |
+
铸
|
7364 |
+
铺
|
7365 |
+
铼
|
7366 |
+
铽
|
7367 |
+
链
|
7368 |
+
铿
|
7369 |
+
销
|
7370 |
+
锁
|
7371 |
+
锂
|
7372 |
+
锃
|
7373 |
+
锄
|
7374 |
+
锅
|
7375 |
+
锆
|
7376 |
+
锇
|
7377 |
+
锈
|
7378 |
+
锉
|
7379 |
+
锋
|
7380 |
+
锌
|
7381 |
+
锍
|
7382 |
+
锎
|
7383 |
+
锏
|
7384 |
+
锐
|
7385 |
+
锑
|
7386 |
+
锒
|
7387 |
+
锔
|
7388 |
+
锕
|
7389 |
+
锖
|
7390 |
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锗
|
7391 |
+
锘
|
7392 |
+
错
|
7393 |
+
锚
|
7394 |
+
锛
|
7395 |
+
锜
|
7396 |
+
锝
|
7397 |
+
锞
|
7398 |
+
锟
|
7399 |
+
锠
|
7400 |
+
锡
|
7401 |
+
锢
|
7402 |
+
锣
|
7403 |
+
锤
|
7404 |
+
锥
|
7405 |
+
锦
|
7406 |
+
锨
|
7407 |
+
锩
|
7408 |
+
锪
|
7409 |
+
锫
|
7410 |
+
锬
|
7411 |
+
锭
|
7412 |
+
键
|
7413 |
+
锯
|
7414 |
+
锰
|
7415 |
+
锱
|
7416 |
+
锲
|
7417 |
+
锳
|
7418 |
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锴
|
7419 |
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锵
|
7420 |
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锶
|
7421 |
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锷
|
7422 |
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锸
|
7423 |
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锹
|
7424 |
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锺
|
7425 |
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锻
|
7426 |
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锼
|
7427 |
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锽
|
7428 |
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镀
|
7429 |
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镁
|
7430 |
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镂
|
7431 |
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镄
|
7432 |
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镅
|
7433 |
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镆
|
7434 |
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镇
|
7435 |
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镈
|
7436 |
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镉
|
7437 |
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镊
|
7438 |
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镋
|
7439 |
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镌
|
7440 |
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镍
|
7441 |
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镎
|
7442 |
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镏
|
7443 |
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镐
|
7444 |
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镑
|
7445 |
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镒
|
7446 |
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镓
|
7447 |
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镔
|
7448 |
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镕
|
7449 |
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镖
|
7450 |
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镗
|
7451 |
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镘
|
7452 |
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镙
|
7453 |
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镚
|
7454 |
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镛
|
7455 |
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镜
|
7456 |
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镝
|
7457 |
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镞
|
7458 |
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镟
|
7459 |
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镠
|
7460 |
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镡
|
7461 |
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镢
|
7462 |
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镣
|
7463 |
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镥
|
7464 |
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镦
|
7465 |
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镧
|
7466 |
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镨
|
7467 |
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镪
|
7468 |
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镫
|
7469 |
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镬
|
7470 |
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镭
|
7471 |
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镯
|
7472 |
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镰
|
7473 |
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镱
|
7474 |
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镲
|
7475 |
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镳
|
7476 |
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镶
|
7477 |
+
长
|
7478 |
+
開
|
7479 |
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閟
|
7480 |
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関
|
7481 |
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閦
|
7482 |
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闇
|
7483 |
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闍
|
7484 |
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闘
|
7485 |
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门
|
7486 |
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闩
|
7487 |
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闪
|
7488 |
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闫
|
7489 |
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闭
|
7490 |
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问
|
7491 |
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闯
|
7492 |
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闰
|
7493 |
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闱
|
7494 |
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闲
|
7495 |
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闳
|
7496 |
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间
|
7497 |
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闵
|
7498 |
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闶
|
7499 |
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闷
|
7500 |
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闸
|
7501 |
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闹
|
7502 |
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闺
|
7503 |
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闻
|
7504 |
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闼
|
7505 |
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闽
|
7506 |
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闾
|
7507 |
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闿
|
7508 |
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阀
|
7509 |
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阁
|
7510 |
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阂
|
7511 |
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阃
|
7512 |
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阄
|
7513 |
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阅
|
7514 |
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阆
|
7515 |
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阇
|
7516 |
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阈
|
7517 |
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阉
|
7518 |
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阊
|
7519 |
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阋
|
7520 |
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阌
|
7521 |
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阍
|
7522 |
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阎
|
7523 |
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阏
|
7524 |
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阐
|
7525 |
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阑
|
7526 |
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阒
|
7527 |
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阓
|
7528 |
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阔
|
7529 |
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阕
|
7530 |
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阖
|
7531 |
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阗
|
7532 |
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阙
|
7533 |
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阚
|
7534 |
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阛
|
7535 |
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阜
|
7536 |
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阝
|
7537 |
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队
|
7538 |
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阡
|
7539 |
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阪
|
7540 |
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阮
|
7541 |
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阱
|
7542 |
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防
|
7543 |
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阳
|
7544 |
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阴
|
7545 |
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阵
|
7546 |
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阶
|
7547 |
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阻
|
7548 |
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阼
|
7549 |
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阿
|
7550 |
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陀
|
7551 |
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陂
|
7552 |
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附
|
7553 |
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际
|
7554 |
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陆
|
7555 |
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陇
|
7556 |
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陈
|
7557 |
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陉
|
7558 |
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陋
|
7559 |
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陌
|
7560 |
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降
|
7561 |
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限
|
7562 |
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陔
|
7563 |
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陕
|
7564 |
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陛
|
7565 |
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陞
|
7566 |
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陟
|
7567 |
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陡
|
7568 |
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院
|
7569 |
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除
|
7570 |
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陨
|
7571 |
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险
|
7572 |
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陪
|
7573 |
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陬
|
7574 |
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陲
|
7575 |
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陵
|
7576 |
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陶
|
7577 |
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陷
|
7578 |
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険
|
7579 |
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隂
|
7580 |
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隅
|
7581 |
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隆
|
7582 |
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隈
|
7583 |
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隋
|
7584 |
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隍
|
7585 |
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随
|
7586 |
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隐
|
7587 |
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隔
|
7588 |
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隗
|
7589 |
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隘
|
7590 |
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隙
|
7591 |
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障
|
7592 |
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隠
|
7593 |
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隣
|
7594 |
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隧
|
7595 |
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隰
|
7596 |
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隳
|
7597 |
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隶
|
7598 |
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隹
|
7599 |
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隼
|
7600 |
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隽
|
7601 |
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难
|
7602 |
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雀
|
7603 |
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雁
|
7604 |
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雄
|
7605 |
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雅
|
7606 |
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集
|
7607 |
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雇
|
7608 |
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雉
|
7609 |
+
雊
|
7610 |
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雌
|
7611 |
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雍
|
7612 |
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雎
|
7613 |
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雏
|
7614 |
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雑
|
7615 |
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雒
|
7616 |
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雕
|
7617 |
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雠
|
7618 |
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雨
|
7619 |
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雩
|
7620 |
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雪
|
7621 |
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雫
|
7622 |
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雯
|
7623 |
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雱
|
7624 |
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雳
|
7625 |
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零
|
7626 |
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雷
|
7627 |
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雹
|
7628 |
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雾
|
7629 |
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需
|
7630 |
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霁
|
7631 |
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霂
|
7632 |
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霄
|
7633 |
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霅
|
7634 |
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霆
|
7635 |
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震
|
7636 |
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霈
|
7637 |
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霉
|
7638 |
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霊
|
7639 |
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霍
|
7640 |
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霎
|
7641 |
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霏
|
7642 |
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霓
|
7643 |
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霖
|
7644 |
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霙
|
7645 |
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霜
|
7646 |
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霞
|
7647 |
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霪
|
7648 |
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霭
|
7649 |
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霰
|
7650 |
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露
|
7651 |
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霸
|
7652 |
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霹
|
7653 |
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霾
|
7654 |
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靑
|
7655 |
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青
|
7656 |
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靓
|
7657 |
+
靖
|
7658 |
+
静
|
7659 |
+
靛
|
7660 |
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非
|
7661 |
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靠
|
7662 |
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靡
|
7663 |
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面
|
7664 |
+
靥
|
7665 |
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革
|
7666 |
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靬
|
7667 |
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靳
|
7668 |
+
靴
|
7669 |
+
靶
|
7670 |
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靺
|
7671 |
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靼
|
7672 |
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鞅
|
7673 |
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鞋
|
7674 |
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鞍
|
7675 |
+
鞑
|
7676 |
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鞔
|
7677 |
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鞘
|
7678 |
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鞞
|
7679 |
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鞠
|
7680 |
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鞣
|
7681 |
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鞥
|
7682 |
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鞨
|
7683 |
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鞫
|
7684 |
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鞬
|
7685 |
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鞭
|
7686 |
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鞮
|
7687 |
+
鞯
|
7688 |
+
鞴
|
7689 |
+
韘
|
7690 |
+
韡
|
7691 |
+
韦
|
7692 |
+
韧
|
7693 |
+
韩
|
7694 |
+
韪
|
7695 |
+
韫
|
7696 |
+
韬
|
7697 |
+
韭
|
7698 |
+
音
|
7699 |
+
韵
|
7700 |
+
韶
|
7701 |
+
頔
|
7702 |
+
頞
|
7703 |
+
頠
|
7704 |
+
頫
|
7705 |
+
頵
|
7706 |
+
頼
|
7707 |
+
顒
|
7708 |
+
顔
|
7709 |
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顕
|
7710 |
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顗
|
7711 |
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页
|
7712 |
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顶
|
7713 |
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顷
|
7714 |
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顸
|
7715 |
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项
|
7716 |
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顺
|
7717 |
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须
|
7718 |
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顼
|
7719 |
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顽
|
7720 |
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顾
|
7721 |
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顿
|
7722 |
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颀
|
7723 |
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颁
|
7724 |
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颂
|
7725 |
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颃
|
7726 |
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预
|
7727 |
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颅
|
7728 |
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领
|
7729 |
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颇
|
7730 |
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颈
|
7731 |
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颉
|
7732 |
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颊
|
7733 |
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颋
|
7734 |
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颌
|
7735 |
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颍
|
7736 |
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颎
|
7737 |
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颏
|
7738 |
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颐
|
7739 |
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频
|
7740 |
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颓
|
7741 |
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颔
|
7742 |
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颖
|
7743 |
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颗
|
7744 |
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题
|
7745 |
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颙
|
7746 |
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颚
|
7747 |
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颛
|
7748 |
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颜
|
7749 |
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额
|
7750 |
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颞
|
7751 |
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颟
|
7752 |
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颠
|
7753 |
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颡
|
7754 |
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颢
|
7755 |
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颤
|
7756 |
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颦
|
7757 |
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颧
|
7758 |
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风
|
7759 |
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飏
|
7760 |
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飐
|
7761 |
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飑
|
7762 |
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飒
|
7763 |
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飓
|
7764 |
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飕
|
7765 |
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飖
|
7766 |
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飘
|
7767 |
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飙
|
7768 |
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飚
|
7769 |
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飞
|
7770 |
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食
|
7771 |
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飧
|
7772 |
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飨
|
7773 |
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餍
|
7774 |
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餐
|
7775 |
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餗
|
7776 |
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餮
|
7777 |
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饔
|
7778 |
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饕
|
7779 |
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饣
|
7780 |
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饥
|
7781 |
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饦
|
7782 |
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饧
|
7783 |
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饨
|
7784 |
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饩
|
7785 |
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饪
|
7786 |
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饫
|
7787 |
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饬
|
7788 |
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饭
|
7789 |
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饮
|
7790 |
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饯
|
7791 |
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饰
|
7792 |
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饱
|
7793 |
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饲
|
7794 |
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饴
|
7795 |
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饵
|
7796 |
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饶
|
7797 |
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饷
|
7798 |
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饸
|
7799 |
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饹
|
7800 |
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饺
|
7801 |
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饼
|
7802 |
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饽
|
7803 |
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饿
|
7804 |
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馀
|
7805 |
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馁
|
7806 |
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馃
|
7807 |
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馄
|
7808 |
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馅
|
7809 |
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馆
|
7810 |
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馇
|
7811 |
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馈
|
7812 |
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馊
|
7813 |
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馋
|
7814 |
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馍
|
7815 |
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馏
|
7816 |
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馐
|
7817 |
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馑
|
7818 |
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馒
|
7819 |
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馓
|
7820 |
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馔
|
7821 |
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馕
|
7822 |
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首
|
7823 |
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馗
|
7824 |
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馘
|
7825 |
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香
|
7826 |
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馥
|
7827 |
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馨
|
7828 |
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駄
|
7829 |
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駅
|
7830 |
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駆
|
7831 |
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騄
|
7832 |
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騑
|
7833 |
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騒
|
7834 |
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験
|
7835 |
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驎
|
7836 |
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驒
|
7837 |
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驩
|
7838 |
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马
|
7839 |
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驭
|
7840 |
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驮
|
7841 |
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驯
|
7842 |
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驰
|
7843 |
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驱
|
7844 |
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驳
|
7845 |
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驴
|
7846 |
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驶
|
7847 |
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驷
|
7848 |
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驸
|
7849 |
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驹
|
7850 |
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驺
|
7851 |
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驻
|
7852 |
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驼
|
7853 |
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驽
|
7854 |
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驾
|
7855 |
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驿
|
7856 |
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骀
|
7857 |
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骁
|
7858 |
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骂
|
7859 |
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骃
|
7860 |
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骄
|
7861 |
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骅
|
7862 |
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骆
|
7863 |
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骇
|
7864 |
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骈
|
7865 |
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骉
|
7866 |
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骊
|
7867 |
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骋
|
7868 |
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验
|
7869 |
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骍
|
7870 |
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骎
|
7871 |
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骏
|
7872 |
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骐
|
7873 |
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骑
|
7874 |
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骓
|
7875 |
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骕
|
7876 |
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骖
|
7877 |
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骗
|
7878 |
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骘
|
7879 |
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骙
|
7880 |
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骚
|
7881 |
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骛
|
7882 |
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骜
|
7883 |
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骝
|
7884 |
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骞
|
7885 |
+
骟
|
7886 |
+
骠
|
7887 |
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骡
|
7888 |
+
骢
|
7889 |
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骤
|
7890 |
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骥
|
7891 |
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骧
|
7892 |
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骨
|
7893 |
+
骰
|
7894 |
+
骱
|
7895 |
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骶
|
7896 |
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骷
|
7897 |
+
骸
|
7898 |
+
骺
|
7899 |
+
骼
|
7900 |
+
髀
|
7901 |
+
髁
|
7902 |
+
髂
|
7903 |
+
髃
|
7904 |
+
髅
|
7905 |
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髈
|
7906 |
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髋
|
7907 |
+
髌
|
7908 |
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髎
|
7909 |
+
髑
|
7910 |
+
髓
|
7911 |
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高
|
7912 |
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髙
|
7913 |
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髡
|
7914 |
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髦
|
7915 |
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髪
|
7916 |
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髫
|
7917 |
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髭
|
7918 |
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髯
|
7919 |
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髹
|
7920 |
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髻
|
7921 |
+
鬃
|
7922 |
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鬈
|
7923 |
+
鬐
|
7924 |
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鬓
|
7925 |
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鬘
|
7926 |
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鬟
|
7927 |
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鬣
|
7928 |
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鬯
|
7929 |
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鬲
|
7930 |
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鬶
|
7931 |
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鬻
|
7932 |
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鬼
|
7933 |
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魁
|
7934 |
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魂
|
7935 |
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魃
|
7936 |
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魄
|
7937 |
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魅
|
7938 |
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魆
|
7939 |
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魇
|
7940 |
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魈
|
7941 |
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魉
|
7942 |
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魋
|
7943 |
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魍
|
7944 |
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魏
|
7945 |
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魑
|
7946 |
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魔
|
7947 |
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魟
|
7948 |
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鮀
|
7949 |
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鮈
|
7950 |
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鮋
|
7951 |
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鮟
|
7952 |
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鮠
|
7953 |
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鮨
|
7954 |
+
鮰
|
7955 |
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鰕
|
7956 |
+
鰤
|
7957 |
+
鱀
|
7958 |
+
鱇
|
7959 |
+
鱓
|
7960 |
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鱬
|
7961 |
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鱲
|
7962 |
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鱻
|
7963 |
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鱼
|
7964 |
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鱿
|
7965 |
+
鲀
|
7966 |
+
鲁
|
7967 |
+
鲂
|
7968 |
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鲃
|
7969 |
+
鲅
|
7970 |
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鲆
|
7971 |
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鲇
|
7972 |
+
鲈
|
7973 |
+
鲉
|
7974 |
+
鲊
|
7975 |
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鲋
|
7976 |
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鲌
|
7977 |
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鲍
|
7978 |
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鲎
|
7979 |
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鲏
|
7980 |
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鲐
|
7981 |
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鲑
|
7982 |
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鲔
|
7983 |
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鲕
|
7984 |
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鲗
|
7985 |
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鲘
|
7986 |
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鲙
|
7987 |
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鲚
|
7988 |
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鲛
|
7989 |
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鲜
|
7990 |
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鲞
|
7991 |
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鲟
|
7992 |
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鲠
|
7993 |
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鲡
|
7994 |
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鲢
|
7995 |
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鲣
|
7996 |
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鲤
|
7997 |
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鲥
|
7998 |
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鲦
|
7999 |
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鲧
|
8000 |
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鲨
|
8001 |
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鲩
|
8002 |
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鲫
|
8003 |
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鲭
|
8004 |
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鲮
|
8005 |
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鲱
|
8006 |
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鲲
|
8007 |
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鲳
|
8008 |
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鲴
|
8009 |
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鲵
|
8010 |
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鲶
|
8011 |
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鲷
|
8012 |
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鲸
|
8013 |
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鲹
|
8014 |
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鲺
|
8015 |
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鲻
|
8016 |
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鲼
|
8017 |
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鲽
|
8018 |
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鲿
|
8019 |
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鳀
|
8020 |
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鳃
|
8021 |
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鳄
|
8022 |
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鳅
|
8023 |
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鳇
|
8024 |
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鳉
|
8025 |
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鳊
|
8026 |
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鳌
|
8027 |
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鳍
|
8028 |
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鳎
|
8029 |
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鳏
|
8030 |
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鳐
|
8031 |
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鳑
|
8032 |
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鳓
|
8033 |
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鳔
|
8034 |
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鳕
|
8035 |
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鳖
|
8036 |
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鳗
|
8037 |
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鳙
|
8038 |
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鳚
|
8039 |
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鳜
|
8040 |
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鳝
|
8041 |
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鳞
|
8042 |
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鳟
|
8043 |
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鳡
|
8044 |
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鳢
|
8045 |
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鳣
|
8046 |
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鳯
|
8047 |
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鳽
|
8048 |
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鳾
|
8049 |
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鴂
|
8050 |
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鴞
|
8051 |
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鴷
|
8052 |
+
鵖
|
8053 |
+
鵙
|
8054 |
+
鵟
|
8055 |
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鵺
|
8056 |
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鶒
|
8057 |
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鶲
|
8058 |
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鷇
|
8059 |
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鷉
|
8060 |
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鷟
|
8061 |
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鸂
|
8062 |
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鸊
|
8063 |
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鸑
|
8064 |
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鸟
|
8065 |
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鸠
|
8066 |
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鸡
|
8067 |
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鸢
|
8068 |
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鸣
|
8069 |
+
鸥
|
8070 |
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鸦
|
8071 |
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鸨
|
8072 |
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鸩
|
8073 |
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鸪
|
8074 |
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鸫
|
8075 |
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鸬
|
8076 |
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鸭
|
8077 |
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鸮
|
8078 |
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鸯
|
8079 |
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鸰
|
8080 |
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鸱
|
8081 |
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鸲
|
8082 |
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鸳
|
8083 |
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鸵
|
8084 |
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鸶
|
8085 |
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鸷
|
8086 |
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鸸
|
8087 |
+
鸹
|
8088 |
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鸺
|
8089 |
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鸻
|
8090 |
+
鸽
|
8091 |
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鸾
|
8092 |
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鸿
|
8093 |
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鹀
|
8094 |
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鹁
|
8095 |
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鹂
|
8096 |
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鹃
|
8097 |
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鹄
|
8098 |
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鹅
|
8099 |
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鹆
|
8100 |
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鹇
|
8101 |
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鹈
|
8102 |
+
鹉
|
8103 |
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鹊
|
8104 |
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鹋
|
8105 |
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鹌
|
8106 |
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鹍
|
8107 |
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鹎
|
8108 |
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鹏
|
8109 |
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鹑
|
8110 |
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鹓
|
8111 |
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鹕
|
8112 |
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鹖
|
8113 |
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鹗
|
8114 |
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鹘
|
8115 |
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鹚
|
8116 |
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鹛
|
8117 |
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鹜
|
8118 |
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鹞
|
8119 |
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鹟
|
8120 |
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鹠
|
8121 |
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鹡
|
8122 |
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鹢
|
8123 |
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鹣
|
8124 |
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鹤
|
8125 |
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鹦
|
8126 |
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鹧
|
8127 |
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鹨
|
8128 |
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鹩
|
8129 |
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鹪
|
8130 |
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鹫
|
8131 |
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鹬
|
8132 |
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鹭
|
8133 |
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鹮
|
8134 |
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鹰
|
8135 |
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鹱
|
8136 |
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鹳
|
8137 |
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鹾
|
8138 |
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鹿
|
8139 |
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麂
|
8140 |
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麇
|
8141 |
+
麈
|
8142 |
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麋
|
8143 |
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麐
|
8144 |
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麑
|
8145 |
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麒
|
8146 |
+
麓
|
8147 |
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麝
|
8148 |
+
麟
|
8149 |
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麤
|
8150 |
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麦
|
8151 |
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麴
|
8152 |
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麸
|
8153 |
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麹
|
8154 |
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麻
|
8155 |
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麽
|
8156 |
+
麾
|
8157 |
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麿
|
8158 |
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黄
|
8159 |
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黉
|
8160 |
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黍
|
8161 |
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黎
|
8162 |
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黏
|
8163 |
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黐
|
8164 |
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黑
|
8165 |
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黒
|
8166 |
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黔
|
8167 |
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默
|
8168 |
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黙
|
8169 |
+
黛
|
8170 |
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黜
|
8171 |
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黝
|
8172 |
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黟
|
8173 |
+
黠
|
8174 |
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黡
|
8175 |
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黢
|
8176 |
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黥
|
8177 |
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黧
|
8178 |
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黩
|
8179 |
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黯
|
8180 |
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黻
|
8181 |
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黼
|
8182 |
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黾
|
8183 |
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鼋
|
8184 |
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鼍
|
8185 |
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鼎
|
8186 |
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鼐
|
8187 |
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鼓
|
8188 |
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鼗
|
8189 |
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鼙
|
8190 |
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鼠
|
8191 |
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鼢
|
8192 |
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鼩
|
8193 |
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鼬
|
8194 |
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鼯
|
8195 |
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鼱
|
8196 |
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鼷
|
8197 |
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鼹
|
8198 |
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鼻
|
8199 |
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鼽
|
8200 |
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鼾
|
8201 |
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齁
|
8202 |
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齐
|
8203 |
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齑
|
8204 |
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齢
|
8205 |
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齮
|
8206 |
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齿
|
8207 |
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龀
|
8208 |
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龁
|
8209 |
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龃
|
8210 |
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龄
|
8211 |
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龅
|
8212 |
+
龆
|
8213 |
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龇
|
8214 |
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龈
|
8215 |
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龉
|
8216 |
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龊
|
8217 |
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龋
|
8218 |
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龌
|
8219 |
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龑
|
8220 |
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龘
|
8221 |
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龙
|
8222 |
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龚
|
8223 |
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龛
|
8224 |
+
龟
|
8225 |
+
龠
|
8226 |
+
龢
|
8227 |
+
A
|
8228 |
+
B
|
8229 |
+
C
|
8230 |
+
D
|
8231 |
+
E
|
8232 |
+
F
|
8233 |
+
G
|
8234 |
+
H
|
8235 |
+
I
|
8236 |
+
J
|
8237 |
+
K
|
8238 |
+
L
|
8239 |
+
M
|
8240 |
+
N
|
8241 |
+
O
|
8242 |
+
P
|
8243 |
+
Q
|
8244 |
+
R
|
8245 |
+
S
|
8246 |
+
T
|
8247 |
+
U
|
8248 |
+
V
|
8249 |
+
W
|
8250 |
+
X
|
8251 |
+
Y
|
8252 |
+
Z
|
8253 |
+
a
|
8254 |
+
b
|
8255 |
+
c
|
8256 |
+
d
|
8257 |
+
e
|
8258 |
+
f
|
8259 |
+
g
|
8260 |
+
h
|
8261 |
+
i
|
8262 |
+
j
|
8263 |
+
k
|
8264 |
+
l
|
8265 |
+
m
|
8266 |
+
n
|
8267 |
+
o
|
8268 |
+
p
|
8269 |
+
q
|
8270 |
+
r
|
8271 |
+
s
|
8272 |
+
t
|
8273 |
+
u
|
8274 |
+
v
|
8275 |
+
w
|
8276 |
+
x
|
8277 |
+
y
|
8278 |
+
z
|
8279 |
+
AA
|
8280 |
+
AB
|
8281 |
+
AC
|
8282 |
+
AD
|
8283 |
+
AE
|
8284 |
+
AF
|
8285 |
+
AG
|
8286 |
+
AH
|
8287 |
+
AI
|
8288 |
+
AJ
|
8289 |
+
AK
|
8290 |
+
AL
|
8291 |
+
AM
|
8292 |
+
AN
|
8293 |
+
AP
|
8294 |
+
AQ
|
8295 |
+
AR
|
8296 |
+
AS
|
8297 |
+
AT
|
8298 |
+
AU
|
8299 |
+
AV
|
8300 |
+
AW
|
8301 |
+
AX
|
8302 |
+
AZ
|
8303 |
+
Al
|
8304 |
+
An
|
8305 |
+
Au
|
8306 |
+
Aw
|
8307 |
+
BA
|
8308 |
+
BB
|
8309 |
+
BC
|
8310 |
+
BD
|
8311 |
+
BE
|
8312 |
+
BF
|
8313 |
+
BG
|
8314 |
+
BH
|
8315 |
+
BI
|
8316 |
+
BJ
|
8317 |
+
BK
|
8318 |
+
BL
|
8319 |
+
BM
|
8320 |
+
BN
|
8321 |
+
BO
|
8322 |
+
BP
|
8323 |
+
BQ
|
8324 |
+
BR
|
8325 |
+
BS
|
8326 |
+
BT
|
8327 |
+
BU
|
8328 |
+
BV
|
8329 |
+
BW
|
8330 |
+
BY
|
8331 |
+
Bo
|
8332 |
+
Br
|
8333 |
+
Bu
|
8334 |
+
CA
|
8335 |
+
CB
|
8336 |
+
CC
|
8337 |
+
CD
|
8338 |
+
CE
|
8339 |
+
CF
|
8340 |
+
CG
|
8341 |
+
CH
|
8342 |
+
CI
|
8343 |
+
CJ
|
8344 |
+
CK
|
8345 |
+
CL
|
8346 |
+
CM
|
8347 |
+
CN
|
8348 |
+
CO
|
8349 |
+
CP
|
8350 |
+
CQ
|
8351 |
+
CR
|
8352 |
+
CS
|
8353 |
+
CT
|
8354 |
+
CU
|
8355 |
+
CV
|
8356 |
+
CW
|
8357 |
+
CX
|
8358 |
+
CY
|
8359 |
+
CZ
|
8360 |
+
Ca
|
8361 |
+
Ch
|
8362 |
+
Cl
|
8363 |
+
Co
|
8364 |
+
Cu
|
8365 |
+
DA
|
8366 |
+
DB
|
8367 |
+
DC
|
8368 |
+
DD
|
8369 |
+
DE
|
8370 |
+
DF
|
8371 |
+
DG
|
8372 |
+
DH
|
8373 |
+
DI
|
8374 |
+
DJ
|
8375 |
+
DK
|
8376 |
+
DL
|
8377 |
+
DM
|
8378 |
+
DN
|
8379 |
+
DO
|
8380 |
+
DQ
|
8381 |
+
DR
|
8382 |
+
DS
|
8383 |
+
DT
|
8384 |
+
DV
|
8385 |
+
DW
|
8386 |
+
DX
|
8387 |
+
DY
|
8388 |
+
DZ
|
8389 |
+
Da
|
8390 |
+
De
|
8391 |
+
Di
|
8392 |
+
Do
|
8393 |
+
Dr
|
8394 |
+
Du
|
8395 |
+
EA
|
8396 |
+
EB
|
8397 |
+
EC
|
8398 |
+
ED
|
8399 |
+
EE
|
8400 |
+
EF
|
8401 |
+
EG
|
8402 |
+
EH
|
8403 |
+
EI
|
8404 |
+
EK
|
8405 |
+
EL
|
8406 |
+
EM
|
8407 |
+
EN
|
8408 |
+
EP
|
8409 |
+
EQ
|
8410 |
+
ER
|
8411 |
+
ES
|
8412 |
+
ET
|
8413 |
+
EU
|
8414 |
+
EV
|
8415 |
+
EW
|
8416 |
+
EX
|
8417 |
+
EZ
|
8418 |
+
Ed
|
8419 |
+
En
|
8420 |
+
Ev
|
8421 |
+
Ex
|
8422 |
+
FA
|
8423 |
+
FB
|
8424 |
+
FC
|
8425 |
+
FD
|
8426 |
+
FE
|
8427 |
+
FF
|
8428 |
+
FG
|
8429 |
+
FH
|
8430 |
+
FI
|
8431 |
+
FJ
|
8432 |
+
FL
|
8433 |
+
FM
|
8434 |
+
FN
|
8435 |
+
FO
|
8436 |
+
FP
|
8437 |
+
FR
|
8438 |
+
FS
|
8439 |
+
FT
|
8440 |
+
FU
|
8441 |
+
FW
|
8442 |
+
FX
|
8443 |
+
FY
|
8444 |
+
FZ
|
8445 |
+
Fa
|
8446 |
+
Fi
|
8447 |
+
Fl
|
8448 |
+
Fo
|
8449 |
+
Fr
|
8450 |
+
Fu
|
8451 |
+
GA
|
8452 |
+
GB
|
8453 |
+
GC
|
8454 |
+
GD
|
8455 |
+
GE
|
8456 |
+
GF
|
8457 |
+
GG
|
8458 |
+
GH
|
8459 |
+
GI
|
8460 |
+
GJ
|
8461 |
+
GK
|
8462 |
+
GL
|
8463 |
+
GM
|
8464 |
+
GN
|
8465 |
+
GO
|
8466 |
+
GP
|
8467 |
+
GQ
|
8468 |
+
GR
|
8469 |
+
GS
|
8470 |
+
GT
|
8471 |
+
GU
|
8472 |
+
GW
|
8473 |
+
GX
|
8474 |
+
GY
|
8475 |
+
GZ
|
8476 |
+
Ga
|
8477 |
+
Go
|
8478 |
+
Gr
|
8479 |
+
Gu
|
8480 |
+
HA
|
8481 |
+
HB
|
8482 |
+
HC
|
8483 |
+
HD
|
8484 |
+
HE
|
8485 |
+
HF
|
8486 |
+
HG
|
8487 |
+
HH
|
8488 |
+
HI
|
8489 |
+
HJ
|
8490 |
+
HK
|
8491 |
+
HL
|
8492 |
+
HO
|
8493 |
+
HP
|
8494 |
+
HQ
|
8495 |
+
HR
|
8496 |
+
HS
|
8497 |
+
HT
|
8498 |
+
HU
|
8499 |
+
HV
|
8500 |
+
HW
|
8501 |
+
HX
|
8502 |
+
HY
|
8503 |
+
HZ
|
8504 |
+
Ha
|
8505 |
+
He
|
8506 |
+
Hi
|
8507 |
+
Ho
|
8508 |
+
Hu
|
8509 |
+
Hz
|
8510 |
+
IB
|
8511 |
+
IC
|
8512 |
+
ID
|
8513 |
+
IE
|
8514 |
+
IF
|
8515 |
+
IG
|
8516 |
+
IH
|
8517 |
+
II
|
8518 |
+
IK
|
8519 |
+
IL
|
8520 |
+
IM
|
8521 |
+
IN
|
8522 |
+
IO
|
8523 |
+
IP
|
8524 |
+
IQ
|
8525 |
+
IR
|
8526 |
+
IS
|
8527 |
+
IT
|
8528 |
+
IU
|
8529 |
+
IV
|
8530 |
+
IX
|
8531 |
+
If
|
8532 |
+
In
|
8533 |
+
JA
|
8534 |
+
JB
|
8535 |
+
JC
|
8536 |
+
JD
|
8537 |
+
JF
|
8538 |
+
JG
|
8539 |
+
JH
|
8540 |
+
JI
|
8541 |
+
JJ
|
8542 |
+
JK
|
8543 |
+
JL
|
8544 |
+
JM
|
8545 |
+
JO
|
8546 |
+
JP
|
8547 |
+
JQ
|
8548 |
+
JR
|
8549 |
+
JS
|
8550 |
+
JT
|
8551 |
+
JU
|
8552 |
+
JW
|
8553 |
+
JX
|
8554 |
+
JY
|
8555 |
+
JZ
|
8556 |
+
Ja
|
8557 |
+
Ji
|
8558 |
+
Jo
|
8559 |
+
Ju
|
8560 |
+
KA
|
8561 |
+
KB
|
8562 |
+
KC
|
8563 |
+
KD
|
8564 |
+
KE
|
8565 |
+
KF
|
8566 |
+
KG
|
8567 |
+
KH
|
8568 |
+
KI
|
8569 |
+
KJ
|
8570 |
+
KK
|
8571 |
+
KL
|
8572 |
+
KM
|
8573 |
+
KN
|
8574 |
+
KO
|
8575 |
+
KP
|
8576 |
+
KR
|
8577 |
+
KS
|
8578 |
+
KT
|
8579 |
+
KV
|
8580 |
+
KW
|
8581 |
+
KX
|
8582 |
+
KY
|
8583 |
+
KZ
|
8584 |
+
LA
|
8585 |
+
LB
|
8586 |
+
LC
|
8587 |
+
LD
|
8588 |
+
LE
|
8589 |
+
LF
|
8590 |
+
LG
|
8591 |
+
LH
|
8592 |
+
LI
|
8593 |
+
LJ
|
8594 |
+
LK
|
8595 |
+
LL
|
8596 |
+
LM
|
8597 |
+
LN
|
8598 |
+
LO
|
8599 |
+
LP
|
8600 |
+
LQ
|
8601 |
+
LR
|
8602 |
+
LS
|
8603 |
+
LT
|
8604 |
+
LU
|
8605 |
+
LV
|
8606 |
+
LW
|
8607 |
+
LX
|
8608 |
+
LY
|
8609 |
+
LZ
|
8610 |
+
La
|
8611 |
+
Le
|
8612 |
+
Li
|
8613 |
+
Lo
|
8614 |
+
Lu
|
8615 |
+
MA
|
8616 |
+
MB
|
8617 |
+
MC
|
8618 |
+
MD
|
8619 |
+
ME
|
8620 |
+
MF
|
8621 |
+
MG
|
8622 |
+
MH
|
8623 |
+
MI
|
8624 |
+
MJ
|
8625 |
+
MK
|
8626 |
+
ML
|
8627 |
+
MM
|
8628 |
+
MN
|
8629 |
+
MO
|
8630 |
+
MP
|
8631 |
+
MQ
|
8632 |
+
MR
|
8633 |
+
MS
|
8634 |
+
MT
|
8635 |
+
MU
|
8636 |
+
MV
|
8637 |
+
MW
|
8638 |
+
MX
|
8639 |
+
MY
|
8640 |
+
Ma
|
8641 |
+
Me
|
8642 |
+
Mi
|
8643 |
+
Mo
|
8644 |
+
Mu
|
8645 |
+
My
|
8646 |
+
NA
|
8647 |
+
NB
|
8648 |
+
NC
|
8649 |
+
ND
|
8650 |
+
NE
|
8651 |
+
NF
|
8652 |
+
NG
|
8653 |
+
NH
|
8654 |
+
NI
|
8655 |
+
NJ
|
8656 |
+
NK
|
8657 |
+
NL
|
8658 |
+
NN
|
8659 |
+
NO
|
8660 |
+
NP
|
8661 |
+
NR
|
8662 |
+
NS
|
8663 |
+
NT
|
8664 |
+
NU
|
8665 |
+
NV
|
8666 |
+
NW
|
8667 |
+
NX
|
8668 |
+
NY
|
8669 |
+
NZ
|
8670 |
+
Na
|
8671 |
+
Ne
|
8672 |
+
No
|
8673 |
+
Nu
|
8674 |
+
OA
|
8675 |
+
OB
|
8676 |
+
OC
|
8677 |
+
OD
|
8678 |
+
OE
|
8679 |
+
OF
|
8680 |
+
OG
|
8681 |
+
OH
|
8682 |
+
OK
|
8683 |
+
OL
|
8684 |
+
OM
|
8685 |
+
ON
|
8686 |
+
OO
|
8687 |
+
OP
|
8688 |
+
OR
|
8689 |
+
OS
|
8690 |
+
OT
|
8691 |
+
OU
|
8692 |
+
OV
|
8693 |
+
OZ
|
8694 |
+
Of
|
8695 |
+
Oh
|
8696 |
+
On
|
8697 |
+
Op
|
8698 |
+
Or
|
8699 |
+
Ou
|
8700 |
+
Ox
|
8701 |
+
PA
|
8702 |
+
PB
|
8703 |
+
PC
|
8704 |
+
PD
|
8705 |
+
PE
|
8706 |
+
PF
|
8707 |
+
PG
|
8708 |
+
PH
|
8709 |
+
PI
|
8710 |
+
PJ
|
8711 |
+
PK
|
8712 |
+
PL
|
8713 |
+
PM
|
8714 |
+
PN
|
8715 |
+
PO
|
8716 |
+
PP
|
8717 |
+
PQ
|
8718 |
+
PR
|
8719 |
+
PS
|
8720 |
+
PT
|
8721 |
+
PU
|
8722 |
+
PV
|
8723 |
+
PW
|
8724 |
+
PX
|
8725 |
+
Pa
|
8726 |
+
Ph
|
8727 |
+
Pl
|
8728 |
+
Po
|
8729 |
+
Pr
|
8730 |
+
Pu
|
8731 |
+
QA
|
8732 |
+
QB
|
8733 |
+
QC
|
8734 |
+
QE
|
8735 |
+
QF
|
8736 |
+
QG
|
8737 |
+
QJ
|
8738 |
+
QL
|
8739 |
+
QQ
|
8740 |
+
QR
|
8741 |
+
QS
|
8742 |
+
QT
|
8743 |
+
QU
|
8744 |
+
QW
|
8745 |
+
QY
|
8746 |
+
Qi
|
8747 |
+
Qu
|
8748 |
+
RA
|
8749 |
+
RB
|
8750 |
+
RC
|
8751 |
+
RE
|
8752 |
+
RF
|
8753 |
+
RG
|
8754 |
+
RH
|
8755 |
+
RI
|
8756 |
+
RJ
|
8757 |
+
RK
|
8758 |
+
RL
|
8759 |
+
RM
|
8760 |
+
RN
|
8761 |
+
RO
|
8762 |
+
RP
|
8763 |
+
RQ
|
8764 |
+
RR
|
8765 |
+
RS
|
8766 |
+
RT
|
8767 |
+
RV
|
8768 |
+
RW
|
8769 |
+
RX
|
8770 |
+
RZ
|
8771 |
+
Ra
|
8772 |
+
Re
|
8773 |
+
Ro
|
8774 |
+
Ru
|
8775 |
+
SA
|
8776 |
+
SB
|
8777 |
+
SC
|
8778 |
+
SD
|
8779 |
+
SE
|
8780 |
+
SF
|
8781 |
+
SG
|
8782 |
+
SH
|
8783 |
+
SI
|
8784 |
+
SJ
|
8785 |
+
SK
|
8786 |
+
SL
|
8787 |
+
SM
|
8788 |
+
SN
|
8789 |
+
SO
|
8790 |
+
SP
|
8791 |
+
SQ
|
8792 |
+
SR
|
8793 |
+
SS
|
8794 |
+
ST
|
8795 |
+
SU
|
8796 |
+
SV
|
8797 |
+
SW
|
8798 |
+
SX
|
8799 |
+
SY
|
8800 |
+
SZ
|
8801 |
+
Sc
|
8802 |
+
Sh
|
8803 |
+
So
|
8804 |
+
Sp
|
8805 |
+
St
|
8806 |
+
Su
|
8807 |
+
Sw
|
8808 |
+
Sy
|
8809 |
+
TA
|
8810 |
+
TB
|
8811 |
+
TC
|
8812 |
+
TD
|
8813 |
+
TE
|
8814 |
+
TF
|
8815 |
+
TG
|
8816 |
+
TH
|
8817 |
+
TI
|
8818 |
+
TJ
|
8819 |
+
TK
|
8820 |
+
TL
|
8821 |
+
TM
|
8822 |
+
TN
|
8823 |
+
TO
|
8824 |
+
TP
|
8825 |
+
TQ
|
8826 |
+
TR
|
8827 |
+
TS
|
8828 |
+
TT
|
8829 |
+
TU
|
8830 |
+
TV
|
8831 |
+
TW
|
8832 |
+
TX
|
8833 |
+
TY
|
8834 |
+
TZ
|
8835 |
+
Th
|
8836 |
+
To
|
8837 |
+
Tr
|
8838 |
+
Tw
|
8839 |
+
UA
|
8840 |
+
UC
|
8841 |
+
UD
|
8842 |
+
UE
|
8843 |
+
UF
|
8844 |
+
UG
|
8845 |
+
UH
|
8846 |
+
UI
|
8847 |
+
UK
|
8848 |
+
UL
|
8849 |
+
UM
|
8850 |
+
UN
|
8851 |
+
UP
|
8852 |
+
UR
|
8853 |
+
US
|
8854 |
+
UT
|
8855 |
+
UU
|
8856 |
+
UV
|
8857 |
+
UW
|
8858 |
+
UX
|
8859 |
+
Ub
|
8860 |
+
Un
|
8861 |
+
Up
|
8862 |
+
VA
|
8863 |
+
VB
|
8864 |
+
VC
|
8865 |
+
VE
|
8866 |
+
VF
|
8867 |
+
VG
|
8868 |
+
VH
|
8869 |
+
VI
|
8870 |
+
VJ
|
8871 |
+
VK
|
8872 |
+
VL
|
8873 |
+
VM
|
8874 |
+
VN
|
8875 |
+
VO
|
8876 |
+
VP
|
8877 |
+
VR
|
8878 |
+
VS
|
8879 |
+
VT
|
8880 |
+
VU
|
8881 |
+
VV
|
8882 |
+
VX
|
8883 |
+
Vi
|
8884 |
+
Vo
|
8885 |
+
WA
|
8886 |
+
WB
|
8887 |
+
WC
|
8888 |
+
WE
|
8889 |
+
WH
|
8890 |
+
WI
|
8891 |
+
WJ
|
8892 |
+
WL
|
8893 |
+
WM
|
8894 |
+
WN
|
8895 |
+
WO
|
8896 |
+
WQ
|
8897 |
+
WR
|
8898 |
+
WS
|
8899 |
+
WT
|
8900 |
+
WU
|
8901 |
+
WW
|
8902 |
+
WX
|
8903 |
+
WZ
|
8904 |
+
Wa
|
8905 |
+
We
|
8906 |
+
Wi
|
8907 |
+
Wo
|
8908 |
+
Wu
|
8909 |
+
XB
|
8910 |
+
XC
|
8911 |
+
XD
|
8912 |
+
XF
|
8913 |
+
XG
|
8914 |
+
XH
|
8915 |
+
XI
|
8916 |
+
XJ
|
8917 |
+
XK
|
8918 |
+
XL
|
8919 |
+
XM
|
8920 |
+
XO
|
8921 |
+
XP
|
8922 |
+
XQ
|
8923 |
+
XR
|
8924 |
+
XS
|
8925 |
+
XT
|
8926 |
+
XU
|
8927 |
+
XV
|
8928 |
+
XW
|
8929 |
+
XX
|
8930 |
+
XY
|
8931 |
+
XZ
|
8932 |
+
Xi
|
8933 |
+
Xu
|
8934 |
+
YA
|
8935 |
+
YB
|
8936 |
+
YC
|
8937 |
+
YD
|
8938 |
+
YE
|
8939 |
+
YF
|
8940 |
+
YG
|
8941 |
+
YH
|
8942 |
+
YJ
|
8943 |
+
YL
|
8944 |
+
YM
|
8945 |
+
YO
|
8946 |
+
YP
|
8947 |
+
YS
|
8948 |
+
YT
|
8949 |
+
YU
|
8950 |
+
YX
|
8951 |
+
YY
|
8952 |
+
YZ
|
8953 |
+
Ya
|
8954 |
+
Yo
|
8955 |
+
Yu
|
8956 |
+
ZA
|
8957 |
+
ZB
|
8958 |
+
ZC
|
8959 |
+
ZD
|
8960 |
+
ZE
|
8961 |
+
ZF
|
8962 |
+
ZG
|
8963 |
+
ZH
|
8964 |
+
ZI
|
8965 |
+
ZJ
|
8966 |
+
ZL
|
8967 |
+
ZM
|
8968 |
+
ZN
|
8969 |
+
ZO
|
8970 |
+
ZQ
|
8971 |
+
ZR
|
8972 |
+
ZS
|
8973 |
+
ZU
|
8974 |
+
ZW
|
8975 |
+
ZX
|
8976 |
+
ZY
|
8977 |
+
ZZ
|
8978 |
+
Zh
|
8979 |
+
ab
|
8980 |
+
aj
|
8981 |
+
an
|
8982 |
+
ap
|
8983 |
+
ar
|
8984 |
+
bb
|
8985 |
+
be
|
8986 |
+
bj
|
8987 |
+
bo
|
8988 |
+
bu
|
8989 |
+
by
|
8990 |
+
ca
|
8991 |
+
cb
|
8992 |
+
cf
|
8993 |
+
ch
|
8994 |
+
cl
|
8995 |
+
cm
|
8996 |
+
co
|
8997 |
+
cp
|
8998 |
+
cv
|
8999 |
+
dB
|
9000 |
+
da
|
9001 |
+
de
|
9002 |
+
di
|
9003 |
+
dj
|
9004 |
+
dn
|
9005 |
+
do
|
9006 |
+
dr
|
9007 |
+
dv
|
9008 |
+
ed
|
9009 |
+
em
|
9010 |
+
en
|
9011 |
+
ep
|
9012 |
+
eq
|
9013 |
+
ev
|
9014 |
+
ex
|
9015 |
+
ez
|
9016 |
+
fa
|
9017 |
+
fe
|
9018 |
+
ff
|
9019 |
+
fi
|
9020 |
+
fl
|
9021 |
+
fo
|
9022 |
+
fr
|
9023 |
+
fu
|
9024 |
+
gb
|
9025 |
+
gd
|
9026 |
+
gh
|
9027 |
+
gi
|
9028 |
+
go
|
9029 |
+
gp
|
9030 |
+
gr
|
9031 |
+
gu
|
9032 |
+
gz
|
9033 |
+
ha
|
9034 |
+
he
|
9035 |
+
hi
|
9036 |
+
ho
|
9037 |
+
hp
|
9038 |
+
hz
|
9039 |
+
iP
|
9040 |
+
iT
|
9041 |
+
ib
|
9042 |
+
ic
|
9043 |
+
id
|
9044 |
+
if
|
9045 |
+
ig
|
9046 |
+
im
|
9047 |
+
in
|
9048 |
+
io
|
9049 |
+
ip
|
9050 |
+
iq
|
9051 |
+
is
|
9052 |
+
it
|
9053 |
+
jQ
|
9054 |
+
ja
|
9055 |
+
ji
|
9056 |
+
jj
|
9057 |
+
jo
|
9058 |
+
jq
|
9059 |
+
ju
|
9060 |
+
kJ
|
9061 |
+
kN
|
9062 |
+
kW
|
9063 |
+
kg
|
9064 |
+
kn
|
9065 |
+
kz
|
9066 |
+
la
|
9067 |
+
ld
|
9068 |
+
le
|
9069 |
+
lg
|
9070 |
+
li
|
9071 |
+
ll
|
9072 |
+
lo
|
9073 |
+
lp
|
9074 |
+
lz
|
9075 |
+
ma
|
9076 |
+
mb
|
9077 |
+
me
|
9078 |
+
mi
|
9079 |
+
mm
|
9080 |
+
mo
|
9081 |
+
mp
|
9082 |
+
mq
|
9083 |
+
mu
|
9084 |
+
mv
|
9085 |
+
my
|
9086 |
+
na
|
9087 |
+
nb
|
9088 |
+
ng
|
9089 |
+
no
|
9090 |
+
nv
|
9091 |
+
ob
|
9092 |
+
of
|
9093 |
+
oh
|
9094 |
+
ok
|
9095 |
+
ol
|
9096 |
+
on
|
9097 |
+
op
|
9098 |
+
or
|
9099 |
+
ou
|
9100 |
+
ow
|
9101 |
+
oz
|
9102 |
+
pH
|
9103 |
+
pa
|
9104 |
+
pc
|
9105 |
+
ph
|
9106 |
+
pk
|
9107 |
+
pl
|
9108 |
+
po
|
9109 |
+
pp
|
9110 |
+
pr
|
9111 |
+
pu
|
9112 |
+
pv
|
9113 |
+
qf
|
9114 |
+
qq
|
9115 |
+
qu
|
9116 |
+
qz
|
9117 |
+
ra
|
9118 |
+
re
|
9119 |
+
rn
|
9120 |
+
ro
|
9121 |
+
rq
|
9122 |
+
se
|
9123 |
+
sh
|
9124 |
+
sk
|
9125 |
+
so
|
9126 |
+
sp
|
9127 |
+
sq
|
9128 |
+
st
|
9129 |
+
su
|
9130 |
+
sw
|
9131 |
+
sz
|
9132 |
+
th
|
9133 |
+
ti
|
9134 |
+
to
|
9135 |
+
tr
|
9136 |
+
tv
|
9137 |
+
tw
|
9138 |
+
ub
|
9139 |
+
uc
|
9140 |
+
uf
|
9141 |
+
ui
|
9142 |
+
uk
|
9143 |
+
un
|
9144 |
+
up
|
9145 |
+
us
|
9146 |
+
uv
|
9147 |
+
ux
|
9148 |
+
uz
|
9149 |
+
vc
|
9150 |
+
vi
|
9151 |
+
vo
|
9152 |
+
vr
|
9153 |
+
wa
|
9154 |
+
we
|
9155 |
+
wh
|
9156 |
+
wi
|
9157 |
+
wo
|
9158 |
+
wr
|
9159 |
+
ww
|
9160 |
+
xj
|
9161 |
+
xq
|
9162 |
+
xx
|
9163 |
+
ya
|
9164 |
+
ye
|
9165 |
+
yj
|
9166 |
+
yo
|
9167 |
+
yu
|
9168 |
+
yy
|
9169 |
+
yz
|
9170 |
+
zf
|
9171 |
+
zh
|
9172 |
+
zi
|
9173 |
+
zj
|
9174 |
+
zq
|
9175 |
+
zu
|
9176 |
+
zz
|
9177 |
+
AAA
|
9178 |
+
AAC
|
9179 |
+
ABA
|
9180 |
+
ABB
|
9181 |
+
ABC
|
9182 |
+
ABO
|
9183 |
+
ABS
|
9184 |
+
ABT
|
9185 |
+
ACA
|
9186 |
+
ACC
|
9187 |
+
ACD
|
9188 |
+
ACE
|
9189 |
+
ACG
|
9190 |
+
ACK
|
9191 |
+
ACL
|
9192 |
+
ACM
|
9193 |
+
ACP
|
9194 |
+
ACR
|
9195 |
+
ACS
|
9196 |
+
ACT
|
9197 |
+
ADA
|
9198 |
+
ADC
|
9199 |
+
ADD
|
9200 |
+
ADF
|
9201 |
+
ADI
|
9202 |
+
ADO
|
9203 |
+
ADP
|
9204 |
+
ADR
|
9205 |
+
ADS
|
9206 |
+
ADV
|
9207 |
+
AED
|
9208 |
+
AES
|
9209 |
+
AFC
|
9210 |
+
AFP
|
9211 |
+
AFS
|
9212 |
+
AGB
|
9213 |
+
AGC
|
9214 |
+
AGE
|
9215 |
+
AGM
|
9216 |
+
AGP
|
9217 |
+
AGV
|
9218 |
+
AIA
|
9219 |
+
AIC
|
9220 |
+
AIG
|
9221 |
+
AIM
|
9222 |
+
AIP
|
9223 |
+
AIR
|
9224 |
+
AIS
|
9225 |
+
AIX
|
9226 |
+
AKB
|
9227 |
+
AKM
|
9228 |
+
ALA
|
9229 |
+
ALL
|
9230 |
+
ALT
|
9231 |
+
AMA
|
9232 |
+
AMC
|
9233 |
+
AMD
|
9234 |
+
AMG
|
9235 |
+
AMI
|
9236 |
+
AML
|
9237 |
+
AMP
|
9238 |
+
AMR
|
9239 |
+
AMS
|
9240 |
+
AMT
|
9241 |
+
AMX
|
9242 |
+
AND
|
9243 |
+
AOC
|
9244 |
+
AOE
|
9245 |
+
AOL
|
9246 |
+
APA
|
9247 |
+
APC
|
9248 |
+
APE
|
9249 |
+
APG
|
9250 |
+
API
|
9251 |
+
APK
|
9252 |
+
APL
|
9253 |
+
APM
|
9254 |
+
APP
|
9255 |
+
APS
|
9256 |
+
APT
|
9257 |
+
APU
|
9258 |
+
ARA
|
9259 |
+
ARC
|
9260 |
+
ARE
|
9261 |
+
ARM
|
9262 |
+
ARP
|
9263 |
+
ART
|
9264 |
+
ASA
|
9265 |
+
ASC
|
9266 |
+
ASF
|
9267 |
+
ASM
|
9268 |
+
ASP
|
9269 |
+
ASR
|
9270 |
+
AST
|
9271 |
+
ATA
|
9272 |
+
ATC
|
9273 |
+
ATF
|
9274 |
+
ATI
|
9275 |
+
ATK
|
9276 |
+
ATM
|
9277 |
+
ATP
|
9278 |
+
ATS
|
9279 |
+
ATV
|
9280 |
+
ATX
|
9281 |
+
AUC
|
9282 |
+
AUG
|
9283 |
+
AUX
|
9284 |
+
AVC
|
9285 |
+
AVG
|
9286 |
+
AVI
|
9287 |
+
AVR
|
9288 |
+
AVS
|
9289 |
+
AVX
|
9290 |
+
AWM
|
9291 |
+
AWS
|
9292 |
+
All
|
9293 |
+
And
|
9294 |
+
Ang
|
9295 |
+
App
|
9296 |
+
Aqu
|
9297 |
+
BAC
|
9298 |
+
BAD
|
9299 |
+
BAE
|
9300 |
+
BAR
|
9301 |
+
BAT
|
9302 |
+
BAU
|
9303 |
+
BBA
|
9304 |
+
BBB
|
9305 |
+
BBC
|
9306 |
+
BBE
|
9307 |
+
BBQ
|
9308 |
+
BBS
|
9309 |
+
BBT
|
9310 |
+
BCD
|
9311 |
+
BEA
|
9312 |
+
BEC
|
9313 |
+
BEI
|
9314 |
+
BET
|
9315 |
+
BGA
|
9316 |
+
BGM
|
9317 |
+
BGP
|
9318 |
+
BIG
|
9319 |
+
BIM
|
9320 |
+
BIS
|
9321 |
+
BLG
|
9322 |
+
BMC
|
9323 |
+
BMD
|
9324 |
+
BMG
|
9325 |
+
BMI
|
9326 |
+
BMP
|
9327 |
+
BMW
|
9328 |
+
BMX
|
9329 |
+
BNC
|
9330 |
+
BOD
|
9331 |
+
BOM
|
9332 |
+
BOT
|
9333 |
+
BOX
|
9334 |
+
BOY
|
9335 |
+
BPM
|
9336 |
+
BPO
|
9337 |
+
BRN
|
9338 |
+
BRT
|
9339 |
+
BSA
|
9340 |
+
BSC
|
9341 |
+
BSD
|
9342 |
+
BSI
|
9343 |
+
BSM
|
9344 |
+
BSP
|
9345 |
+
BSS
|
9346 |
+
BTC
|
9347 |
+
BTR
|
9348 |
+
BTS
|
9349 |
+
BTV
|
9350 |
+
BUG
|
9351 |
+
BUN
|
9352 |
+
BUS
|
9353 |
+
BWV
|
9354 |
+
Bur
|
9355 |
+
Bus
|
9356 |
+
But
|
9357 |
+
CAA
|
9358 |
+
CAC
|
9359 |
+
CAD
|
9360 |
+
CAE
|
9361 |
+
CAI
|
9362 |
+
CAJ
|
9363 |
+
CAM
|
9364 |
+
CAN
|
9365 |
+
CAP
|
9366 |
+
CAR
|
9367 |
+
CAS
|
9368 |
+
CAT
|
9369 |
+
CBA
|
9370 |
+
CBC
|
9371 |
+
CBD
|
9372 |
+
CBN
|
9373 |
+
CBR
|
9374 |
+
CBS
|
9375 |
+
CCA
|
9376 |
+
CCC
|
9377 |
+
CCD
|
9378 |
+
CCF
|
9379 |
+
CCG
|
9380 |
+
CCI
|
9381 |
+
CCK
|
9382 |
+
CCM
|
9383 |
+
CCN
|
9384 |
+
CCP
|
9385 |
+
CCS
|
9386 |
+
CDC
|
9387 |
+
CDM
|
9388 |
+
CDN
|
9389 |
+
CDO
|
9390 |
+
CDP
|
9391 |
+
CDR
|
9392 |
+
CDS
|
9393 |
+
CEA
|
9394 |
+
CEC
|
9395 |
+
CEO
|
9396 |
+
CES
|
9397 |
+
CET
|
9398 |
+
CFA
|
9399 |
+
CFC
|
9400 |
+
CFD
|
9401 |
+
CFO
|
9402 |
+
CFR
|
9403 |
+
CGI
|
9404 |
+
CHA
|
9405 |
+
CHM
|
9406 |
+
CHO
|
9407 |
+
CIA
|
9408 |
+
CIC
|
9409 |
+
CID
|
9410 |
+
CIE
|
9411 |
+
CIF
|
9412 |
+
CIK
|
9413 |
+
CIO
|
9414 |
+
CIP
|
9415 |
+
CIS
|
9416 |
+
CLA
|
9417 |
+
CLI
|
9418 |
+
CLM
|
9419 |
+
CLS
|
9420 |
+
CMA
|
9421 |
+
CMC
|
9422 |
+
CME
|
9423 |
+
CML
|
9424 |
+
CMM
|
9425 |
+
CMO
|
9426 |
+
CMP
|
9427 |
+
CMS
|
9428 |
+
CMV
|
9429 |
+
CNC
|
9430 |
+
CNG
|
9431 |
+
CNN
|
9432 |
+
CNS
|
9433 |
+
COB
|
9434 |
+
COC
|
9435 |
+
COD
|
9436 |
+
COM
|
9437 |
+
CON
|
9438 |
+
COO
|
9439 |
+
COP
|
9440 |
+
COS
|
9441 |
+
COX
|
9442 |
+
CPA
|
9443 |
+
CPC
|
9444 |
+
CPE
|
9445 |
+
CPI
|
9446 |
+
CPL
|
9447 |
+
CPM
|
9448 |
+
CPP
|
9449 |
+
CPR
|
9450 |
+
CPS
|
9451 |
+
CPU
|
9452 |
+
CQC
|
9453 |
+
CRC
|
9454 |
+
CRM
|
9455 |
+
CRP
|
9456 |
+
CRS
|
9457 |
+
CRT
|
9458 |
+
CSA
|
9459 |
+
CSF
|
9460 |
+
CSI
|
9461 |
+
CSM
|
9462 |
+
CSP
|
9463 |
+
CSR
|
9464 |
+
CSS
|
9465 |
+
CST
|
9466 |
+
CTA
|
9467 |
+
CTC
|
9468 |
+
CTI
|
9469 |
+
CTO
|
9470 |
+
CTP
|
9471 |
+
CTS
|
9472 |
+
CUB
|
9473 |
+
CUT
|
9474 |
+
CVD
|
9475 |
+
CVN
|
9476 |
+
CVS
|
9477 |
+
CVT
|
9478 |
+
CXW
|
9479 |
+
CYP
|
9480 |
+
Car
|
9481 |
+
Cha
|
9482 |
+
Chr
|
9483 |
+
Chu
|
9484 |
+
Com
|
9485 |
+
Con
|
9486 |
+
Cou
|
9487 |
+
Cur
|
9488 |
+
DAB
|
9489 |
+
DAC
|
9490 |
+
DAO
|
9491 |
+
DAS
|
9492 |
+
DAT
|
9493 |
+
DAY
|
9494 |
+
DBA
|
9495 |
+
DBM
|
9496 |
+
DCD
|
9497 |
+
DCE
|
9498 |
+
DCF
|
9499 |
+
DCS
|
9500 |
+
DCT
|
9501 |
+
DDC
|
9502 |
+
DDD
|
9503 |
+
DDG
|
9504 |
+
DDN
|
9505 |
+
DDR
|
9506 |
+
DDS
|
9507 |
+
DDT
|
9508 |
+
DEA
|
9509 |
+
DEC
|
9510 |
+
DEM
|
9511 |
+
DES
|
9512 |
+
DFS
|
9513 |
+
DFT
|
9514 |
+
DHA
|
9515 |
+
DHL
|
9516 |
+
DIC
|
9517 |
+
DID
|
9518 |
+
DIF
|
9519 |
+
DIN
|
9520 |
+
DIP
|
9521 |
+
DIV
|
9522 |
+
DIY
|
9523 |
+
DLC
|
9524 |
+
DLL
|
9525 |
+
DLP
|
9526 |
+
DLT
|
9527 |
+
DMA
|
9528 |
+
DMC
|
9529 |
+
DMD
|
9530 |
+
DMF
|
9531 |
+
DMI
|
9532 |
+
DMO
|
9533 |
+
DMZ
|
9534 |
+
DNA
|
9535 |
+
DNF
|
9536 |
+
DNS
|
9537 |
+
DNV
|
9538 |
+
DOC
|
9539 |
+
DOI
|
9540 |
+
DOM
|
9541 |
+
DON
|
9542 |
+
DOS
|
9543 |
+
DOT
|
9544 |
+
DPI
|
9545 |
+
DPP
|
9546 |
+
DPS
|
9547 |
+
DRM
|
9548 |
+
DRX
|
9549 |
+
DSA
|
9550 |
+
DSC
|
9551 |
+
DSG
|
9552 |
+
DSL
|
9553 |
+
DSM
|
9554 |
+
DSP
|
9555 |
+
DSS
|
9556 |
+
DTC
|
9557 |
+
DTE
|
9558 |
+
DTM
|
9559 |
+
DTS
|
9560 |
+
DTU
|
9561 |
+
DVB
|
9562 |
+
DVD
|
9563 |
+
DVI
|
9564 |
+
DVR
|
9565 |
+
DWG
|
9566 |
+
DYG
|
9567 |
+
Day
|
9568 |
+
Div
|
9569 |
+
Don
|
9570 |
+
Dou
|
9571 |
+
Dow
|
9572 |
+
EAN
|
9573 |
+
EAP
|
9574 |
+
EBD
|
9575 |
+
EBS
|
9576 |
+
ECC
|
9577 |
+
ECM
|
9578 |
+
ECO
|
9579 |
+
ECT
|
9580 |
+
ECU
|
9581 |
+
ECW
|
9582 |
+
EDA
|
9583 |
+
EDG
|
9584 |
+
EDI
|
9585 |
+
EDM
|
9586 |
+
EDP
|
9587 |
+
EDR
|
9588 |
+
EEG
|
9589 |
+
EEP
|
9590 |
+
EFR
|
9591 |
+
EGF
|
9592 |
+
EHS
|
9593 |
+
EIA
|
9594 |
+
EJB
|
9595 |
+
EMA
|
9596 |
+
EMC
|
9597 |
+
EMI
|
9598 |
+
EMP
|
9599 |
+
EMS
|
9600 |
+
END
|
9601 |
+
EOS
|
9602 |
+
EPA
|
9603 |
+
EPC
|
9604 |
+
EPO
|
9605 |
+
EPR
|
9606 |
+
EPS
|
9607 |
+
ERP
|
9608 |
+
ESC
|
9609 |
+
ESD
|
9610 |
+
ESI
|
9611 |
+
ESL
|
9612 |
+
ESP
|
9613 |
+
ESR
|
9614 |
+
EST
|
9615 |
+
ETC
|
9616 |
+
ETF
|
9617 |
+
ETH
|
9618 |
+
ETL
|
9619 |
+
ETS
|
9620 |
+
EVA
|
9621 |
+
EVE
|
9622 |
+
EVO
|
9623 |
+
EXE
|
9624 |
+
EXO
|
9625 |
+
EXP
|
9626 |
+
EYE
|
9627 |
+
Eff
|
9628 |
+
Ell
|
9629 |
+
Emb
|
9630 |
+
Emp
|
9631 |
+
End
|
9632 |
+
Eng
|
9633 |
+
Equ
|
9634 |
+
Eur
|
9635 |
+
Eva
|
9636 |
+
Exc
|
9637 |
+
Exp
|
9638 |
+
FAA
|
9639 |
+
FAB
|
9640 |
+
FAG
|
9641 |
+
FAL
|
9642 |
+
FAN
|
9643 |
+
FAO
|
9644 |
+
FAQ
|
9645 |
+
FAT
|
9646 |
+
FBI
|
9647 |
+
FCA
|
9648 |
+
FCC
|
9649 |
+
FCI
|
9650 |
+
FCS
|
9651 |
+
FDA
|
9652 |
+
FDD
|
9653 |
+
FDI
|
9654 |
+
FEM
|
9655 |
+
FES
|
9656 |
+
FET
|
9657 |
+
FFT
|
9658 |
+
FGO
|
9659 |
+
FHD
|
9660 |
+
FIA
|
9661 |
+
FLV
|
9662 |
+
FLY
|
9663 |
+
FMS
|
9664 |
+
FNC
|
9665 |
+
FOB
|
9666 |
+
FOF
|
9667 |
+
FOR
|
9668 |
+
FOX
|
9669 |
+
FPC
|
9670 |
+
FPS
|
9671 |
+
FPX
|
9672 |
+
FRP
|
9673 |
+
FSA
|
9674 |
+
FSB
|
9675 |
+
FSC
|
9676 |
+
FSH
|
9677 |
+
FTA
|
9678 |
+
FTC
|
9679 |
+
FTP
|
9680 |
+
FUE
|
9681 |
+
FUN
|
9682 |
+
Fin
|
9683 |
+
Fiv
|
9684 |
+
Fly
|
9685 |
+
For
|
9686 |
+
Fou
|
9687 |
+
Fuj
|
9688 |
+
Fun
|
9689 |
+
Fut
|
9690 |
+
GAP
|
9691 |
+
GAT
|
9692 |
+
GAY
|
9693 |
+
GBA
|
9694 |
+
GBK
|
9695 |
+
GBT
|
9696 |
+
GBU
|
9697 |
+
GCC
|
9698 |
+
GCS
|
9699 |
+
GCT
|
9700 |
+
GDI
|
9701 |
+
GDP
|
9702 |
+
GEN
|
9703 |
+
GEO
|
9704 |
+
GET
|
9705 |
+
GFP
|
9706 |
+
GHz
|
9707 |
+
GIA
|
9708 |
+
GIF
|
9709 |
+
GIS
|
9710 |
+
GLA
|
9711 |
+
GLC
|
9712 |
+
GLP
|
9713 |
+
GLS
|
9714 |
+
GMA
|
9715 |
+
GMC
|
9716 |
+
GMP
|
9717 |
+
GMS
|
9718 |
+
GMT
|
9719 |
+
GMV
|
9720 |
+
GND
|
9721 |
+
GNP
|
9722 |
+
GNU
|
9723 |
+
GOD
|
9724 |
+
GOT
|
9725 |
+
GPA
|
9726 |
+
GPL
|
9727 |
+
GPS
|
9728 |
+
GPT
|
9729 |
+
GPU
|
9730 |
+
GRC
|
9731 |
+
GRE
|
9732 |
+
GRF
|
9733 |
+
GSH
|
9734 |
+
GSM
|
9735 |
+
GSP
|
9736 |
+
GTA
|
9737 |
+
GTI
|
9738 |
+
GTO
|
9739 |
+
GTP
|
9740 |
+
GTR
|
9741 |
+
GTS
|
9742 |
+
GTX
|
9743 |
+
GUI
|
9744 |
+
Giv
|
9745 |
+
Gmb
|
9746 |
+
Gua
|
9747 |
+
Gui
|
9748 |
+
Gun
|
9749 |
+
Guo
|
9750 |
+
Guy
|
9751 |
+
HAD
|
9752 |
+
HAL
|
9753 |
+
HBA
|
9754 |
+
HBO
|
9755 |
+
HBV
|
9756 |
+
HBs
|
9757 |
+
HCG
|
9758 |
+
HCI
|
9759 |
+
HCV
|
9760 |
+
HCl
|
9761 |
+
HDD
|
9762 |
+
HDL
|
9763 |
+
HDR
|
9764 |
+
HDV
|
9765 |
+
HEY
|
9766 |
+
HFC
|
9767 |
+
HGH
|
9768 |
+
HGT
|
9769 |
+
HID
|
9770 |
+
HIP
|
9771 |
+
HIS
|
9772 |
+
HIT
|
9773 |
+
HIV
|
9774 |
+
HLA
|
9775 |
+
HMG
|
9776 |
+
HMI
|
9777 |
+
HMS
|
9778 |
+
HOP
|
9779 |
+
HOT
|
9780 |
+
HOW
|
9781 |
+
HPC
|
9782 |
+
HPV
|
9783 |
+
HRC
|
9784 |
+
HRT
|
9785 |
+
HSE
|
9786 |
+
HSK
|
9787 |
+
HSV
|
9788 |
+
HTC
|
9789 |
+
HUB
|
9790 |
+
HUD
|
9791 |
+
HVG
|
9792 |
+
Haz
|
9793 |
+
Her
|
9794 |
+
Hom
|
9795 |
+
Hon
|
9796 |
+
Hou
|
9797 |
+
How
|
9798 |
+
Hua
|
9799 |
+
Hub
|
9800 |
+
Hum
|
9801 |
+
Hun
|
9802 |
+
IAI
|
9803 |
+
IAS
|
9804 |
+
IAT
|
9805 |
+
IBC
|
9806 |
+
IBF
|
9807 |
+
IBM
|
9808 |
+
ICA
|
9809 |
+
ICC
|
9810 |
+
ICD
|
9811 |
+
ICE
|
9812 |
+
ICO
|
9813 |
+
ICP
|
9814 |
+
ICQ
|
9815 |
+
ICS
|
9816 |
+
ICT
|
9817 |
+
ICU
|
9818 |
+
IDC
|
9819 |
+
IDD
|
9820 |
+
IDE
|
9821 |
+
IDF
|
9822 |
+
IDG
|
9823 |
+
IDS
|
9824 |
+
IEC
|
9825 |
+
IET
|
9826 |
+
IFA
|
9827 |
+
IFC
|
9828 |
+
IFI
|
9829 |
+
IFN
|
9830 |
+
IGF
|
9831 |
+
IGN
|
9832 |
+
IIA
|
9833 |
+
III
|
9834 |
+
IIS
|
9835 |
+
IKO
|
9836 |
+
IMA
|
9837 |
+
IMC
|
9838 |
+
IMD
|
9839 |
+
IME
|
9840 |
+
IMF
|
9841 |
+
IMG
|
9842 |
+
IMO
|
9843 |
+
IMS
|
9844 |
+
IMT
|
9845 |
+
INA
|
9846 |
+
INC
|
9847 |
+
INF
|
9848 |
+
ING
|
9849 |
+
INS
|
9850 |
+
INT
|
9851 |
+
IOS
|
9852 |
+
IPA
|
9853 |
+
IPC
|
9854 |
+
IPO
|
9855 |
+
IPS
|
9856 |
+
IPX
|
9857 |
+
IRC
|
9858 |
+
IRI
|
9859 |
+
ISA
|
9860 |
+
ISI
|
9861 |
+
ISM
|
9862 |
+
ISO
|
9863 |
+
ISP
|
9864 |
+
ITC
|
9865 |
+
ITF
|
9866 |
+
ITO
|
9867 |
+
ITS
|
9868 |
+
ITT
|
9869 |
+
ITU
|
9870 |
+
ITV
|
9871 |
+
IVR
|
9872 |
+
Imp
|
9873 |
+
InC
|
9874 |
+
Inf
|
9875 |
+
Inj
|
9876 |
+
Int
|
9877 |
+
JAR
|
9878 |
+
JBL
|
9879 |
+
JBT
|
9880 |
+
JCB
|
9881 |
+
JCR
|
9882 |
+
JDB
|
9883 |
+
JDG
|
9884 |
+
JET
|
9885 |
+
JGJ
|
9886 |
+
JIS
|
9887 |
+
JIT
|
9888 |
+
JKL
|
9889 |
+
JOE
|
9890 |
+
JPG
|
9891 |
+
JSF
|
9892 |
+
JSP
|
9893 |
+
JST
|
9894 |
+
JTA
|
9895 |
+
JVC
|
9896 |
+
JVM
|
9897 |
+
JYJ
|
9898 |
+
JYP
|
9899 |
+
Jac
|
9900 |
+
Jam
|
9901 |
+
Jan
|
9902 |
+
Jap
|
9903 |
+
Jav
|
9904 |
+
Jay
|
9905 |
+
Jin
|
9906 |
+
Joh
|
9907 |
+
Jon
|
9908 |
+
Jul
|
9909 |
+
Jun
|
9910 |
+
Jus
|
9911 |
+
KAB
|
9912 |
+
KAT
|
9913 |
+
KBS
|
9914 |
+
KDF
|
9915 |
+
KDJ
|
9916 |
+
KEY
|
9917 |
+
KFC
|
9918 |
+
KFR
|
9919 |
+
KID
|
9920 |
+
KIS
|
9921 |
+
KJm
|
9922 |
+
KOF
|
9923 |
+
KOH
|
9924 |
+
KOL
|
9925 |
+
KPI
|
9926 |
+
KPL
|
9927 |
+
KTV
|
9928 |
+
KVM
|
9929 |
+
Kin
|
9930 |
+
Kon
|
9931 |
+
Kur
|
9932 |
+
LAB
|
9933 |
+
LAN
|
9934 |
+
LBS
|
9935 |
+
LCA
|
9936 |
+
LCD
|
9937 |
+
LCK
|
9938 |
+
LCS
|
9939 |
+
LDA
|
9940 |
+
LDH
|
9941 |
+
LDL
|
9942 |
+
LDP
|
9943 |
+
LED
|
9944 |
+
LEE
|
9945 |
+
LEO
|
9946 |
+
LES
|
9947 |
+
LET
|
9948 |
+
LGA
|
9949 |
+
LGD
|
9950 |
+
LIN
|
9951 |
+
LIU
|
9952 |
+
LLC
|
9953 |
+
LME
|
9954 |
+
LMS
|
9955 |
+
LNG
|
9956 |
+
LOF
|
9957 |
+
LOL
|
9958 |
+
LOW
|
9959 |
+
LPG
|
9960 |
+
LPL
|
9961 |
+
LPR
|
9962 |
+
LRC
|
9963 |
+
LSA
|
9964 |
+
LSD
|
9965 |
+
LSI
|
9966 |
+
LSP
|
9967 |
+
LTD
|
9968 |
+
LTE
|
9969 |
+
LUC
|
9970 |
+
LUN
|
9971 |
+
LVM
|
9972 |
+
Laz
|
9973 |
+
Lib
|
9974 |
+
Lif
|
9975 |
+
Lin
|
9976 |
+
Liu
|
9977 |
+
Liz
|
9978 |
+
Lon
|
9979 |
+
Lou
|
9980 |
+
Low
|
9981 |
+
Luc
|
9982 |
+
Lum
|
9983 |
+
Luo
|
9984 |
+
Lux
|
9985 |
+
MAC
|
9986 |
+
MAD
|
9987 |
+
MAG
|
9988 |
+
MAN
|
9989 |
+
MAO
|
9990 |
+
MAP
|
9991 |
+
MAR
|
9992 |
+
MAS
|
9993 |
+
MAT
|
9994 |
+
MAX
|
9995 |
+
MAY
|
9996 |
+
MBA
|
9997 |
+
MBC
|
9998 |
+
MBO
|
9999 |
+
MBR
|
10000 |
+
MBS
|
10001 |
+
MCA
|
10002 |
+
MCC
|
10003 |
+
MCM
|
10004 |
+
MCN
|
10005 |
+
MCP
|
10006 |
+
MCS
|
10007 |
+
MCU
|
10008 |
+
MDA
|
10009 |
+
MDI
|
10010 |
+
MDL
|
10011 |
+
MDR
|
10012 |
+
MDS
|
10013 |
+
MEN
|
10014 |
+
MES
|
10015 |
+
MFA
|
10016 |
+
MFC
|
10017 |
+
MHC
|
10018 |
+
MHz
|
10019 |
+
MIB
|
10020 |
+
MIC
|
10021 |
+
MID
|
10022 |
+
MIL
|
10023 |
+
MIN
|
10024 |
+
MIS
|
10025 |
+
MIT
|
10026 |
+
MIX
|
10027 |
+
MKV
|
10028 |
+
MLC
|
10029 |
+
MLF
|
10030 |
+
MMA
|
10031 |
+
MMC
|
10032 |
+
MMI
|
10033 |
+
MMO
|
10034 |
+
MMS
|
10035 |
+
MMX
|
10036 |
+
MOD
|
10037 |
+
MOM
|
10038 |
+
MOS
|
10039 |
+
MOV
|
10040 |
+
MPA
|
10041 |
+
MPC
|
10042 |
+
MPG
|
10043 |
+
MPI
|
10044 |
+
MPS
|
10045 |
+
MPV
|
10046 |
+
MPa
|
10047 |
+
MRC
|
10048 |
+
MRI
|
10049 |
+
MRO
|
10050 |
+
MRP
|
10051 |
+
MSA
|
10052 |
+
MSC
|
10053 |
+
MSI
|
10054 |
+
MSN
|
10055 |
+
MTI
|
10056 |
+
MTK
|
10057 |
+
MTS
|
10058 |
+
MTU
|
10059 |
+
MTV
|
10060 |
+
MVC
|
10061 |
+
MVP
|
10062 |
+
Mac
|
10063 |
+
Mag
|
10064 |
+
Maj
|
10065 |
+
Man
|
10066 |
+
Mar
|
10067 |
+
Max
|
10068 |
+
May
|
10069 |
+
Mic
|
10070 |
+
Min
|
10071 |
+
Mon
|
10072 |
+
Mou
|
10073 |
+
Mur
|
10074 |
+
NAD
|
10075 |
+
NAS
|
10076 |
+
NAT
|
10077 |
+
NBA
|
10078 |
+
NBC
|
10079 |
+
NBL
|
10080 |
+
NCT
|
10081 |
+
NDS
|
10082 |
+
NEC
|
10083 |
+
NEO
|
10084 |
+
NES
|
10085 |
+
NET
|
10086 |
+
NEW
|
10087 |
+
NEX
|
10088 |
+
NFA
|
10089 |
+
NFC
|
10090 |
+
NFL
|
10091 |
+
NFS
|
10092 |
+
NGC
|
10093 |
+
NGN
|
10094 |
+
NGO
|
10095 |
+
NHK
|
10096 |
+
NHL
|
10097 |
+
NIC
|
10098 |
+
NIH
|
10099 |
+
NLP
|
10100 |
+
NME
|
10101 |
+
NMR
|
10102 |
+
NOT
|
10103 |
+
NOW
|
10104 |
+
NOX
|
10105 |
+
NOx
|
10106 |
+
NPC
|
10107 |
+
NPN
|
10108 |
+
NPR
|
10109 |
+
NSA
|
10110 |
+
NSC
|
10111 |
+
NSF
|
10112 |
+
NSK
|
10113 |
+
NTN
|
10114 |
+
NTP
|
10115 |
+
NTT
|
10116 |
+
NTV
|
10117 |
+
NVH
|
10118 |
+
NWA
|
10119 |
+
NXT
|
10120 |
+
NYT
|
10121 |
+
Nic
|
10122 |
+
Nob
|
10123 |
+
Nor
|
10124 |
+
Nov
|
10125 |
+
Now
|
10126 |
+
Nur
|
10127 |
+
OAD
|
10128 |
+
OBD
|
10129 |
+
OCG
|
10130 |
+
OCP
|
10131 |
+
OCR
|
10132 |
+
OCT
|
10133 |
+
ODM
|
10134 |
+
OEM
|
10135 |
+
OFF
|
10136 |
+
OGG
|
10137 |
+
OLE
|
10138 |
+
OMG
|
10139 |
+
ONE
|
10140 |
+
ONU
|
10141 |
+
OOO
|
10142 |
+
OPC
|
10143 |
+
OPP
|
10144 |
+
ORC
|
10145 |
+
OSD
|
10146 |
+
OSI
|
10147 |
+
OSS
|
10148 |
+
OST
|
10149 |
+
OTA
|
10150 |
+
OTC
|
10151 |
+
OTG
|
10152 |
+
OTT
|
10153 |
+
OUT
|
10154 |
+
OVA
|
10155 |
+
OVP
|
10156 |
+
Obj
|
10157 |
+
Off
|
10158 |
+
Oly
|
10159 |
+
Ope
|
10160 |
+
Oph
|
10161 |
+
Opt
|
10162 |
+
Our
|
10163 |
+
Out
|
10164 |
+
Ove
|
10165 |
+
PAC
|
10166 |
+
PAD
|
10167 |
+
PAH
|
10168 |
+
PAL
|
10169 |
+
PAM
|
10170 |
+
PAN
|
10171 |
+
PAS
|
10172 |
+
PBS
|
10173 |
+
PBT
|
10174 |
+
PCA
|
10175 |
+
PCB
|
10176 |
+
PCD
|
10177 |
+
PCI
|
10178 |
+
PCL
|
10179 |
+
PCM
|
10180 |
+
PCR
|
10181 |
+
PCS
|
10182 |
+
PCT
|
10183 |
+
PDA
|
10184 |
+
PDB
|
10185 |
+
PDC
|
10186 |
+
PDD
|
10187 |
+
PDF
|
10188 |
+
PDM
|
10189 |
+
PDP
|
10190 |
+
PDU
|
10191 |
+
PEG
|
10192 |
+
PEP
|
10193 |
+
PER
|
10194 |
+
PES
|
10195 |
+
PET
|
10196 |
+
PFA
|
10197 |
+
PFC
|
10198 |
+
PGA
|
10199 |
+
PGC
|
10200 |
+
PHP
|
10201 |
+
PHS
|
10202 |
+
PIC
|
10203 |
+
PID
|
10204 |
+
PIM
|
10205 |
+
PIN
|
10206 |
+
PKI
|
10207 |
+
PLA
|
10208 |
+
PLC
|
10209 |
+
PLD
|
10210 |
+
PLL
|
10211 |
+
PLM
|
10212 |
+
PMC
|
10213 |
+
PMI
|
10214 |
+
PMP
|
10215 |
+
PND
|
10216 |
+
PNG
|
10217 |
+
PNP
|
10218 |
+
POE
|
10219 |
+
POM
|
10220 |
+
PON
|
10221 |
+
POP
|
10222 |
+
POS
|
10223 |
+
PPA
|
10224 |
+
PPC
|
10225 |
+
PPG
|
10226 |
+
PPH
|
10227 |
+
PPI
|
10228 |
+
PPM
|
10229 |
+
PPP
|
10230 |
+
PPR
|
10231 |
+
PPS
|
10232 |
+
PPT
|
10233 |
+
PPV
|
10234 |
+
PRL
|
10235 |
+
PRO
|
10236 |
+
PSA
|
10237 |
+
PSD
|
10238 |
+
PSE
|
10239 |
+
PSG
|
10240 |
+
PSI
|
10241 |
+
PSK
|
10242 |
+
PSP
|
10243 |
+
PSS
|
10244 |
+
PSV
|
10245 |
+
PSW
|
10246 |
+
PSY
|
10247 |
+
PTA
|
10248 |
+
PTC
|
10249 |
+
PTH
|
10250 |
+
PTT
|
10251 |
+
PUB
|
10252 |
+
PVA
|
10253 |
+
PVC
|
10254 |
+
PVE
|
10255 |
+
PVP
|
10256 |
+
PWM
|
10257 |
+
Par
|
10258 |
+
Per
|
10259 |
+
Pic
|
10260 |
+
Pow
|
10261 |
+
Pro
|
10262 |
+
Pur
|
10263 |
+
QAM
|
10264 |
+
QDI
|
10265 |
+
QFP
|
10266 |
+
QGh
|
10267 |
+
QOS
|
10268 |
+
QPI
|
10269 |
+
QPS
|
10270 |
+
QRS
|
10271 |
+
QTL
|
10272 |
+
Qin
|
10273 |
+
Qua
|
10274 |
+
Que
|
10275 |
+
RAM
|
10276 |
+
RAP
|
10277 |
+
RAR
|
10278 |
+
RAS
|
10279 |
+
RAW
|
10280 |
+
RBC
|
10281 |
+
RCA
|
10282 |
+
RCS
|
10283 |
+
RDF
|
10284 |
+
RDS
|
10285 |
+
RED
|
10286 |
+
REF
|
10287 |
+
REG
|
10288 |
+
REM
|
10289 |
+
REX
|
10290 |
+
RFC
|
10291 |
+
RGB
|
10292 |
+
RIA
|
10293 |
+
RIM
|
10294 |
+
RIP
|
10295 |
+
RMB
|
10296 |
+
RMS
|
10297 |
+
RNA
|
10298 |
+
RNG
|
10299 |
+
ROC
|
10300 |
+
ROE
|
10301 |
+
ROI
|
10302 |
+
ROM
|
10303 |
+
RPC
|
10304 |
+
RPG
|
10305 |
+
RPM
|
10306 |
+
RRW
|
10307 |
+
RSA
|
10308 |
+
RSC
|
10309 |
+
RSI
|
10310 |
+
RSS
|
10311 |
+
RTA
|
10312 |
+
RTC
|
10313 |
+
RTK
|
10314 |
+
RTP
|
10315 |
+
RTS
|
10316 |
+
RTU
|
10317 |
+
RTX
|
10318 |
+
RUN
|
10319 |
+
RUS
|
10320 |
+
Ray
|
10321 |
+
Raz
|
10322 |
+
Ric
|
10323 |
+
Riv
|
10324 |
+
Rom
|
10325 |
+
Rou
|
10326 |
+
Rub
|
10327 |
+
Run
|
10328 |
+
Rus
|
10329 |
+
SAC
|
10330 |
+
SAE
|
10331 |
+
SAM
|
10332 |
+
SAN
|
10333 |
+
SAO
|
10334 |
+
SAP
|
10335 |
+
SAR
|
10336 |
+
SAS
|
10337 |
+
SAT
|
10338 |
+
SAY
|
10339 |
+
SBR
|
10340 |
+
SBS
|
10341 |
+
SCE
|
10342 |
+
SCH
|
10343 |
+
SCI
|
10344 |
+
SCM
|
10345 |
+
SCP
|
10346 |
+
SCR
|
10347 |
+
SDH
|
10348 |
+
SDI
|
10349 |
+
SDK
|
10350 |
+
SDR
|
10351 |
+
SDS
|
10352 |
+
SEA
|
10353 |
+
SEC
|
10354 |
+
SEE
|
10355 |
+
SEM
|
10356 |
+
SEO
|
10357 |
+
SER
|
10358 |
+
SET
|
10359 |
+
SFC
|
10360 |
+
SFP
|
10361 |
+
SGH
|
10362 |
+
SGI
|
10363 |
+
SGS
|
10364 |
+
SHA
|
10365 |
+
SHE
|
10366 |
+
SID
|
10367 |
+
SIG
|
10368 |
+
SIM
|
10369 |
+
SIP
|
10370 |
+
SIR
|
10371 |
+
SIS
|
10372 |
+
SKF
|
10373 |
+
SKT
|
10374 |
+
SKU
|
10375 |
+
SKY
|
10376 |
+
SLA
|
10377 |
+
SLC
|
10378 |
+
SLE
|
10379 |
+
SLG
|
10380 |
+
SLI
|
10381 |
+
SLR
|
10382 |
+
SLS
|
10383 |
+
SMA
|
10384 |
+
SMB
|
10385 |
+
SMC
|
10386 |
+
SMD
|
10387 |
+
SMG
|
10388 |
+
SMI
|
10389 |
+
SMP
|
10390 |
+
SMS
|
10391 |
+
SMT
|
10392 |
+
SNK
|
10393 |
+
SNP
|
10394 |
+
SNR
|
10395 |
+
SNS
|
10396 |
+
SOA
|
10397 |
+
SOC
|
10398 |
+
SOD
|
10399 |
+
SOI
|
10400 |
+
SOP
|
10401 |
+
SOS
|
10402 |
+
SPA
|
10403 |
+
SPC
|
10404 |
+
SPD
|
10405 |
+
SPE
|
10406 |
+
SPF
|
10407 |
+
SPI
|
10408 |
+
SPR
|
10409 |
+
SPS
|
10410 |
+
SPT
|
10411 |
+
SPV
|
10412 |
+
SQL
|
10413 |
+
SQU
|
10414 |
+
SRS
|
10415 |
+
SRT
|
10416 |
+
SSA
|
10417 |
+
SSC
|
10418 |
+
SSD
|
10419 |
+
SSE
|
10420 |
+
SSH
|
10421 |
+
SSL
|
10422 |
+
SSR
|
10423 |
+
SSS
|
10424 |
+
SST
|
10425 |
+
STC
|
10426 |
+
STD
|
10427 |
+
STK
|
10428 |
+
STL
|
10429 |
+
STM
|
10430 |
+
STN
|
10431 |
+
STP
|
10432 |
+
STR
|
10433 |
+
STS
|
10434 |
+
SUB
|
10435 |
+
SUN
|
10436 |
+
SUV
|
10437 |
+
SVC
|
10438 |
+
SVD
|
10439 |
+
SVG
|
10440 |
+
SVM
|
10441 |
+
SWF
|
10442 |
+
SXG
|
10443 |
+
SYN
|
10444 |
+
SYS
|
10445 |
+
Sch
|
10446 |
+
Ser
|
10447 |
+
She
|
10448 |
+
Siz
|
10449 |
+
Som
|
10450 |
+
Sou
|
10451 |
+
Squ
|
10452 |
+
Sub
|
10453 |
+
Sum
|
10454 |
+
Sun
|
10455 |
+
Sup
|
10456 |
+
Suz
|
10457 |
+
TAB
|
10458 |
+
TAC
|
10459 |
+
TAG
|
10460 |
+
TAO
|
10461 |
+
TBC
|
10462 |
+
TBM
|
10463 |
+
TBS
|
10464 |
+
TCG
|
10465 |
+
TCL
|
10466 |
+
TCM
|
10467 |
+
TCO
|
10468 |
+
TCP
|
10469 |
+
TCR
|
10470 |
+
TCS
|
10471 |
+
TCT
|
10472 |
+
TDD
|
10473 |
+
TDI
|
10474 |
+
TDM
|
10475 |
+
TDP
|
10476 |
+
TDS
|
10477 |
+
TEC
|
10478 |
+
TED
|
10479 |
+
TEL
|
10480 |
+
TEM
|
10481 |
+
TES
|
10482 |
+
TEU
|
10483 |
+
TEX
|
10484 |
+
TFT
|
10485 |
+
TGA
|
10486 |
+
TGF
|
10487 |
+
TGV
|
10488 |
+
THD
|
10489 |
+
THE
|
10490 |
+
TIA
|
10491 |
+
TIF
|
10492 |
+
TKO
|
10493 |
+
TLC
|
10494 |
+
TLS
|
10495 |
+
TMD
|
10496 |
+
TMP
|
10497 |
+
TMS
|
10498 |
+
TMT
|
10499 |
+
TNA
|
10500 |
+
TNF
|
10501 |
+
TNT
|
10502 |
+
TOC
|
10503 |
+
TOD
|
10504 |
+
TOE
|
10505 |
+
TOM
|
10506 |
+
TOP
|
10507 |
+
TPC
|
10508 |
+
TPE
|
10509 |
+
TPM
|
10510 |
+
TPO
|
10511 |
+
TPP
|
10512 |
+
TPR
|
10513 |
+
TPS
|
10514 |
+
TPU
|
10515 |
+
TQM
|
10516 |
+
TSC
|
10517 |
+
TSH
|
10518 |
+
TSI
|
10519 |
+
TSP
|
10520 |
+
TTL
|
10521 |
+
TTS
|
10522 |
+
TTT
|
10523 |
+
TUV
|
10524 |
+
TVB
|
10525 |
+
TVC
|
10526 |
+
TVP
|
10527 |
+
TVS
|
10528 |
+
TWO
|
10529 |
+
TXT
|
10530 |
+
Tay
|
10531 |
+
The
|
10532 |
+
Tom
|
10533 |
+
Tou
|
10534 |
+
Tow
|
10535 |
+
Tur
|
10536 |
+
UAR
|
10537 |
+
UBC
|
10538 |
+
UCC
|
10539 |
+
UCL
|
10540 |
+
UDP
|
10541 |
+
UFC
|
10542 |
+
UFO
|
10543 |
+
UGC
|
10544 |
+
UHF
|
10545 |
+
UIP
|
10546 |
+
UMD
|
10547 |
+
UML
|
10548 |
+
UNI
|
10549 |
+
UPC
|
10550 |
+
UPS
|
10551 |
+
URL
|
10552 |
+
USA
|
10553 |
+
USB
|
10554 |
+
USD
|
10555 |
+
USM
|
10556 |
+
USP
|
10557 |
+
USS
|
10558 |
+
UTC
|
10559 |
+
UTF
|
10560 |
+
UTP
|
10561 |
+
UTR
|
10562 |
+
UVA
|
10563 |
+
UVB
|
10564 |
+
UWB
|
10565 |
+
UZI
|
10566 |
+
Umb
|
10567 |
+
Uni
|
10568 |
+
Upp
|
10569 |
+
Uzi
|
10570 |
+
VAC
|
10571 |
+
VAR
|
10572 |
+
VBA
|
10573 |
+
VBR
|
10574 |
+
VBS
|
10575 |
+
VCC
|
10576 |
+
VCD
|
10577 |
+
VCR
|
10578 |
+
VDC
|
10579 |
+
VDE
|
10580 |
+
VGA
|
10581 |
+
VHF
|
10582 |
+
VHS
|
10583 |
+
VIA
|
10584 |
+
VII
|
10585 |
+
VIP
|
10586 |
+
VIS
|
10587 |
+
VMw
|
10588 |
+
VOA
|
10589 |
+
VOB
|
10590 |
+
VOC
|
10591 |
+
VOD
|
10592 |
+
VOL
|
10593 |
+
VPN
|
10594 |
+
VPS
|
10595 |
+
VRP
|
10596 |
+
VSS
|
10597 |
+
VTE
|
10598 |
+
VVT
|
10599 |
+
Ver
|
10600 |
+
Vic
|
10601 |
+
Vid
|
10602 |
+
Vis
|
10603 |
+
Viv
|
10604 |
+
WAN
|
10605 |
+
WAP
|
10606 |
+
WAV
|
10607 |
+
WAY
|
10608 |
+
WBA
|
10609 |
+
WBC
|
10610 |
+
WBO
|
10611 |
+
WBS
|
10612 |
+
WCG
|
10613 |
+
WCW
|
10614 |
+
WDM
|
10615 |
+
WDS
|
10616 |
+
WEB
|
10617 |
+
WEP
|
10618 |
+
WEY
|
10619 |
+
WGK
|
10620 |
+
WHO
|
10621 |
+
WIN
|
10622 |
+
WMA
|
10623 |
+
WMS
|
10624 |
+
WMV
|
10625 |
+
WOW
|
10626 |
+
WPA
|
10627 |
+
WPF
|
10628 |
+
WPS
|
10629 |
+
WRC
|
10630 |
+
WSA
|
10631 |
+
WTA
|
10632 |
+
WTI
|
10633 |
+
WTO
|
10634 |
+
WVG
|
10635 |
+
WWE
|
10636 |
+
WWF
|
10637 |
+
WWW
|
10638 |
+
Way
|
10639 |
+
Wha
|
10640 |
+
Whe
|
10641 |
+
Whi
|
10642 |
+
Who
|
10643 |
+
Why
|
10644 |
+
WiF
|
10645 |
+
Win
|
10646 |
+
Wiz
|
10647 |
+
Wom
|
10648 |
+
Wor
|
10649 |
+
Wou
|
10650 |
+
XGA
|
10651 |
+
XII
|
10652 |
+
XML
|
10653 |
+
XPS
|
10654 |
+
XXX
|
10655 |
+
XYZ
|
10656 |
+
YAG
|
10657 |
+
YES
|
10658 |
+
YOU
|
10659 |
+
YZB
|
10660 |
+
Yin
|
10661 |
+
You
|
10662 |
+
Yua
|
10663 |
+
Yuk
|
10664 |
+
Yun
|
10665 |
+
ZIP
|
10666 |
+
ZOL
|
10667 |
+
Zer
|
10668 |
+
Zha
|
10669 |
+
Zhu
|
10670 |
+
Zom
|
10671 |
+
Zon
|
10672 |
+
Zou
|
10673 |
+
abb
|
10674 |
+
abc
|
10675 |
+
abo
|
10676 |
+
abs
|
10677 |
+
act
|
10678 |
+
adj
|
10679 |
+
aff
|
10680 |
+
all
|
10681 |
+
and
|
10682 |
+
ang
|
10683 |
+
any
|
10684 |
+
app
|
10685 |
+
aws
|
10686 |
+
bbb
|
10687 |
+
bbc
|
10688 |
+
bbq
|
10689 |
+
bbs
|
10690 |
+
but
|
10691 |
+
cAM
|
10692 |
+
cDN
|
10693 |
+
cGM
|
10694 |
+
can
|
10695 |
+
car
|
10696 |
+
cba
|
10697 |
+
cha
|
10698 |
+
chi
|
10699 |
+
col
|
10700 |
+
com
|
10701 |
+
con
|
10702 |
+
cor
|
10703 |
+
cou
|
10704 |
+
cpi
|
10705 |
+
cpu
|
10706 |
+
dan
|
10707 |
+
day
|
10708 |
+
des
|
10709 |
+
did
|
10710 |
+
dif
|
10711 |
+
dis
|
10712 |
+
div
|
10713 |
+
diy
|
10714 |
+
doc
|
10715 |
+
don
|
10716 |
+
dow
|
10717 |
+
eAA
|
10718 |
+
eSA
|
10719 |
+
ech
|
10720 |
+
eff
|
10721 |
+
emb
|
10722 |
+
emp
|
10723 |
+
end
|
10724 |
+
eng
|
10725 |
+
eqc
|
10726 |
+
equ
|
10727 |
+
euv
|
10728 |
+
eve
|
10729 |
+
exc
|
10730 |
+
exe
|
10731 |
+
exp
|
10732 |
+
fac
|
10733 |
+
fil
|
10734 |
+
fin
|
10735 |
+
fir
|
10736 |
+
fiv
|
10737 |
+
fla
|
10738 |
+
fly
|
10739 |
+
for
|
10740 |
+
fox
|
10741 |
+
fre
|
10742 |
+
fri
|
10743 |
+
gAS
|
10744 |
+
gdp
|
10745 |
+
gen
|
10746 |
+
giv
|
10747 |
+
gmp
|
10748 |
+
gon
|
10749 |
+
goo
|
10750 |
+
got
|
10751 |
+
gps
|
10752 |
+
gra
|
10753 |
+
gre
|
10754 |
+
gro
|
10755 |
+
had
|
10756 |
+
har
|
10757 |
+
has
|
10758 |
+
hav
|
10759 |
+
haz
|
10760 |
+
her
|
10761 |
+
his
|
10762 |
+
hiv
|
10763 |
+
hol
|
10764 |
+
hom
|
10765 |
+
hou
|
10766 |
+
how
|
10767 |
+
iBT
|
10768 |
+
iOS
|
10769 |
+
iPa
|
10770 |
+
iPh
|
10771 |
+
iPo
|
10772 |
+
iSC
|
10773 |
+
ima
|
10774 |
+
imp
|
10775 |
+
inc
|
10776 |
+
inf
|
10777 |
+
inj
|
10778 |
+
int
|
10779 |
+
ipa
|
10780 |
+
iph
|
10781 |
+
ipo
|
10782 |
+
isb
|
10783 |
+
iso
|
10784 |
+
jam
|
10785 |
+
jap
|
10786 |
+
jav
|
10787 |
+
jay
|
10788 |
+
jus
|
10789 |
+
kHz
|
10790 |
+
kJm
|
10791 |
+
kdj
|
10792 |
+
kin
|
10793 |
+
lay
|
10794 |
+
laz
|
10795 |
+
lck
|
10796 |
+
lea
|
10797 |
+
led
|
10798 |
+
let
|
10799 |
+
lib
|
10800 |
+
lif
|
10801 |
+
lin
|
10802 |
+
liq
|
10803 |
+
lis
|
10804 |
+
lit
|
10805 |
+
liv
|
10806 |
+
liz
|
10807 |
+
lly
|
10808 |
+
lng
|
10809 |
+
loc
|
10810 |
+
lof
|
10811 |
+
log
|
10812 |
+
loo
|
10813 |
+
los
|
10814 |
+
low
|
10815 |
+
mRN
|
10816 |
+
mac
|
10817 |
+
mad
|
10818 |
+
maj
|
10819 |
+
man
|
10820 |
+
mar
|
10821 |
+
mat
|
10822 |
+
max
|
10823 |
+
may
|
10824 |
+
maz
|
10825 |
+
mba
|
10826 |
+
men
|
10827 |
+
mic
|
10828 |
+
min
|
10829 |
+
mmH
|
10830 |
+
mod
|
10831 |
+
mon
|
10832 |
+
mor
|
10833 |
+
mys
|
10834 |
+
nVI
|
10835 |
+
nba
|
10836 |
+
nex
|
10837 |
+
nic
|
10838 |
+
not
|
10839 |
+
nov
|
10840 |
+
now
|
10841 |
+
nxp
|
10842 |
+
obj
|
10843 |
+
off
|
10844 |
+
one
|
10845 |
+
ope
|
10846 |
+
opp
|
10847 |
+
our
|
10848 |
+
out
|
10849 |
+
ove
|
10850 |
+
par
|
10851 |
+
pay
|
10852 |
+
per
|
10853 |
+
phe
|
10854 |
+
php
|
10855 |
+
piz
|
10856 |
+
pla
|
10857 |
+
pow
|
10858 |
+
ppp
|
10859 |
+
pre
|
10860 |
+
pro
|
10861 |
+
pvc
|
10862 |
+
qHD
|
10863 |
+
qgh
|
10864 |
+
qua
|
10865 |
+
que
|
10866 |
+
qui
|
10867 |
+
rRN
|
10868 |
+
ray
|
10869 |
+
raz
|
10870 |
+
rea
|
10871 |
+
rec
|
10872 |
+
red
|
10873 |
+
ref
|
10874 |
+
reg
|
10875 |
+
rem
|
10876 |
+
rep
|
10877 |
+
req
|
10878 |
+
res
|
10879 |
+
rev
|
10880 |
+
ric
|
10881 |
+
riv
|
10882 |
+
rmb
|
10883 |
+
rng
|
10884 |
+
rom
|
10885 |
+
rou
|
10886 |
+
say
|
10887 |
+
sch
|
10888 |
+
sha
|
10889 |
+
she
|
10890 |
+
shi
|
10891 |
+
sho
|
10892 |
+
sim
|
10893 |
+
sin
|
10894 |
+
siz
|
10895 |
+
som
|
10896 |
+
sou
|
10897 |
+
spa
|
10898 |
+
spe
|
10899 |
+
sql
|
10900 |
+
squ
|
10901 |
+
sta
|
10902 |
+
ste
|
10903 |
+
sto
|
10904 |
+
str
|
10905 |
+
sty
|
10906 |
+
sub
|
10907 |
+
suv
|
10908 |
+
tRN
|
10909 |
+
tha
|
10910 |
+
the
|
10911 |
+
thi
|
10912 |
+
thr
|
10913 |
+
tim
|
10914 |
+
tip
|
10915 |
+
top
|
10916 |
+
tow
|
10917 |
+
tpp
|
10918 |
+
tra
|
10919 |
+
tur
|
10920 |
+
tuv
|
10921 |
+
two
|
10922 |
+
ubc
|
10923 |
+
uiv
|
10924 |
+
unc
|
10925 |
+
und
|
10926 |
+
uni
|
10927 |
+
unk
|
10928 |
+
ups
|
10929 |
+
usb
|
10930 |
+
uva
|
10931 |
+
uvb
|
10932 |
+
uzi
|
10933 |
+
val
|
10934 |
+
var
|
10935 |
+
ver
|
10936 |
+
vie
|
10937 |
+
vip
|
10938 |
+
vis
|
10939 |
+
viv
|
10940 |
+
wan
|
10941 |
+
was
|
10942 |
+
way
|
10943 |
+
web
|
10944 |
+
wer
|
10945 |
+
wha
|
10946 |
+
whi
|
10947 |
+
who
|
10948 |
+
why
|
10949 |
+
wif
|
10950 |
+
wit
|
10951 |
+
wom
|
10952 |
+
won
|
10953 |
+
wor
|
10954 |
+
wou
|
10955 |
+
www
|
10956 |
+
xin
|
10957 |
+
xxx
|
10958 |
+
yin
|
10959 |
+
you
|
10960 |
+
zha
|
10961 |
+
zhi
|
10962 |
+
zho
|
10963 |
+
zhu
|
10964 |
+
zon
|
10965 |
+
zzf
|
10966 |
+
zzy
|
10967 |
+
AAAA
|
10968 |
+
AACS
|
10969 |
+
ABCD
|
10970 |
+
ACCA
|
10971 |
+
ACCE
|
10972 |
+
ACCP
|
10973 |
+
ACDC
|
10974 |
+
ACGN
|
10975 |
+
ACID
|
10976 |
+
ACPI
|
10977 |
+
ACTH
|
10978 |
+
ADHD
|
10979 |
+
ADPC
|
10980 |
+
ADSL
|
10981 |
+
AIDS
|
10982 |
+
AJAX
|
10983 |
+
ALPH
|
10984 |
+
AMEX
|
10985 |
+
AMOL
|
10986 |
+
ANGE
|
10987 |
+
ANSI
|
10988 |
+
ANSY
|
10989 |
+
APEC
|
10990 |
+
APPL
|
10991 |
+
APTE
|
10992 |
+
ARDS
|
10993 |
+
ARPA
|
10994 |
+
ARPG
|
10995 |
+
ASCE
|
10996 |
+
ASCI
|
10997 |
+
ASIA
|
10998 |
+
ASIC
|
10999 |
+
ASIN
|
11000 |
+
ASME
|
11001 |
+
ASSO
|
11002 |
+
ASTM
|
11003 |
+
ASUS
|
11004 |
+
AUDI
|
11005 |
+
AUTO
|
11006 |
+
AVCH
|
11007 |
+
AWAR
|
11008 |
+
Andr
|
11009 |
+
BABY
|
11010 |
+
BACK
|
11011 |
+
BAND
|
11012 |
+
BANG
|
11013 |
+
BANK
|
11014 |
+
BASI
|
11015 |
+
BASS
|
11016 |
+
BATT
|
11017 |
+
BEAS
|
11018 |
+
BEAT
|
11019 |
+
BEST
|
11020 |
+
BEYO
|
11021 |
+
BIGB
|
11022 |
+
BIOS
|
11023 |
+
BLAC
|
11024 |
+
BLEA
|
11025 |
+
BLOG
|
11026 |
+
BLOO
|
11027 |
+
BLUE
|
11028 |
+
BOBO
|
11029 |
+
BOOK
|
11030 |
+
BOOL
|
11031 |
+
BOOM
|
11032 |
+
BOPP
|
11033 |
+
BOSS
|
11034 |
+
BOYS
|
11035 |
+
BRAV
|
11036 |
+
BREA
|
11037 |
+
BUFF
|
11038 |
+
Buck
|
11039 |
+
Buff
|
11040 |
+
Bull
|
11041 |
+
Bung
|
11042 |
+
Buzz
|
11043 |
+
CADC
|
11044 |
+
CALL
|
11045 |
+
CAPC
|
11046 |
+
CAPP
|
11047 |
+
CARD
|
11048 |
+
CASE
|
11049 |
+
CASI
|
11050 |
+
CAST
|
11051 |
+
CATI
|
11052 |
+
CATV
|
11053 |
+
CAXA
|
11054 |
+
CCFL
|
11055 |
+
CCIE
|
11056 |
+
CCNA
|
11057 |
+
CCTV
|
11058 |
+
CDMA
|
11059 |
+
CEPA
|
11060 |
+
CERN
|
11061 |
+
CHAN
|
11062 |
+
CHAP
|
11063 |
+
CHAR
|
11064 |
+
CHEN
|
11065 |
+
CHIN
|
11066 |
+
CHOR
|
11067 |
+
CIMS
|
11068 |
+
CIPA
|
11069 |
+
CISC
|
11070 |
+
CITE
|
11071 |
+
CITY
|
11072 |
+
CLAM
|
11073 |
+
CLAN
|
11074 |
+
CLAS
|
11075 |
+
CLOS
|
11076 |
+
CLUB
|
11077 |
+
CMMB
|
11078 |
+
CMMI
|
11079 |
+
CMOS
|
11080 |
+
CMYK
|
11081 |
+
CNAS
|
11082 |
+
CNBC
|
11083 |
+
CNBL
|
11084 |
+
CNKI
|
11085 |
+
CNNI
|
11086 |
+
COCO
|
11087 |
+
CODE
|
11088 |
+
COLL
|
11089 |
+
COLO
|
11090 |
+
COMB
|
11091 |
+
COME
|
11092 |
+
COMI
|
11093 |
+
COMP
|
11094 |
+
CONT
|
11095 |
+
COOL
|
11096 |
+
CORB
|
11097 |
+
CORE
|
11098 |
+
COSM
|
11099 |
+
COSP
|
11100 |
+
COST
|
11101 |
+
COUN
|
11102 |
+
COVI
|
11103 |
+
CPLD
|
11104 |
+
CREA
|
11105 |
+
CROS
|
11106 |
+
CSCD
|
11107 |
+
CSDN
|
11108 |
+
CSMA
|
11109 |
+
CSOL
|
11110 |
+
CSSC
|
11111 |
+
CSTN
|
11112 |
+
CTRL
|
11113 |
+
CUBA
|
11114 |
+
CUDA
|
11115 |
+
CURR
|
11116 |
+
CVBS
|
11117 |
+
Chin
|
11118 |
+
Chur
|
11119 |
+
DANC
|
11120 |
+
DARK
|
11121 |
+
DARP
|
11122 |
+
DASH
|
11123 |
+
DATA
|
11124 |
+
DAYS
|
11125 |
+
DCDC
|
11126 |
+
DDNS
|
11127 |
+
DDOS
|
11128 |
+
DDRI
|
11129 |
+
DELL
|
11130 |
+
DEMO
|
11131 |
+
DESI
|
11132 |
+
DEST
|
11133 |
+
DHCP
|
11134 |
+
DIGI
|
11135 |
+
DIMM
|
11136 |
+
DISC
|
11137 |
+
DIVX
|
11138 |
+
DLNA
|
11139 |
+
DOHC
|
11140 |
+
DOTA
|
11141 |
+
DOWN
|
11142 |
+
DRAG
|
11143 |
+
DRAM
|
11144 |
+
DREA
|
11145 |
+
DRIV
|
11146 |
+
DSLR
|
11147 |
+
DVDC
|
11148 |
+
DVGA
|
11149 |
+
DWDM
|
11150 |
+
DWOR
|
11151 |
+
EAST
|
11152 |
+
EASY
|
11153 |
+
EBIT
|
11154 |
+
ECMO
|
11155 |
+
EDGE
|
11156 |
+
EDIT
|
11157 |
+
EDTA
|
11158 |
+
EGFR
|
11159 |
+
EINE
|
11160 |
+
ELIS
|
11161 |
+
ELLE
|
11162 |
+
EMBA
|
11163 |
+
ENER
|
11164 |
+
ENGI
|
11165 |
+
ENTE
|
11166 |
+
EPDM
|
11167 |
+
EPIS
|
11168 |
+
EPON
|
11169 |
+
EPSO
|
11170 |
+
EPUB
|
11171 |
+
ERCP
|
11172 |
+
ERRO
|
11173 |
+
ESET
|
11174 |
+
ESPN
|
11175 |
+
ETSI
|
11176 |
+
EVDO
|
11177 |
+
EVER
|
11178 |
+
EXCE
|
11179 |
+
EXIL
|
11180 |
+
EXPO
|
11181 |
+
Ever
|
11182 |
+
Exch
|
11183 |
+
Exer
|
11184 |
+
FACE
|
11185 |
+
FALS
|
11186 |
+
FANS
|
11187 |
+
FANU
|
11188 |
+
FAST
|
11189 |
+
FDDI
|
11190 |
+
FIBA
|
11191 |
+
FIDI
|
11192 |
+
FIFA
|
11193 |
+
FIFO
|
11194 |
+
FILE
|
11195 |
+
FINA
|
11196 |
+
FIRE
|
11197 |
+
FIRS
|
11198 |
+
FISH
|
11199 |
+
FIVE
|
11200 |
+
FLAC
|
11201 |
+
FLAS
|
11202 |
+
FLOW
|
11203 |
+
FMVP
|
11204 |
+
FORT
|
11205 |
+
FPGA
|
11206 |
+
FREE
|
11207 |
+
FROM
|
11208 |
+
FTTH
|
11209 |
+
FULL
|
11210 |
+
FWVG
|
11211 |
+
FXCM
|
11212 |
+
Fuck
|
11213 |
+
Full
|
11214 |
+
Fund
|
11215 |
+
Fung
|
11216 |
+
Fuzz
|
11217 |
+
GABA
|
11218 |
+
GALA
|
11219 |
+
GAME
|
11220 |
+
GANK
|
11221 |
+
GATT
|
11222 |
+
GEAR
|
11223 |
+
GENE
|
11224 |
+
GHOS
|
11225 |
+
GIRL
|
11226 |
+
GLON
|
11227 |
+
GMAT
|
11228 |
+
GNSS
|
11229 |
+
GOLD
|
11230 |
+
GOOD
|
11231 |
+
GOOG
|
11232 |
+
GPRS
|
11233 |
+
GREE
|
11234 |
+
GROU
|
11235 |
+
GSMG
|
11236 |
+
GUCC
|
11237 |
+
GUND
|
11238 |
+
GUTS
|
11239 |
+
Gund
|
11240 |
+
HACC
|
11241 |
+
HAPP
|
11242 |
+
HARD
|
11243 |
+
HART
|
11244 |
+
HDCP
|
11245 |
+
HDMI
|
11246 |
+
HDPE
|
11247 |
+
HDTV
|
11248 |
+
HEAD
|
11249 |
+
HEAR
|
11250 |
+
HELL
|
11251 |
+
HEPA
|
11252 |
+
HERO
|
11253 |
+
HIFI
|
11254 |
+
HIGH
|
11255 |
+
HIPH
|
11256 |
+
HKEY
|
11257 |
+
HOLD
|
11258 |
+
HOME
|
11259 |
+
HOST
|
11260 |
+
HOUS
|
11261 |
+
HPLC
|
11262 |
+
HSDP
|
11263 |
+
HSPA
|
11264 |
+
HTML
|
11265 |
+
HTTP
|
11266 |
+
HUNT
|
11267 |
+
Hugh
|
11268 |
+
Hung
|
11269 |
+
ICAN
|
11270 |
+
ICMP
|
11271 |
+
ICON
|
11272 |
+
IDEA
|
11273 |
+
IDOL
|
11274 |
+
IEEE
|
11275 |
+
IELT
|
11276 |
+
IETF
|
11277 |
+
IFPI
|
11278 |
+
IGBT
|
11279 |
+
IGMP
|
11280 |
+
IMAX
|
11281 |
+
IMDB
|
11282 |
+
INFO
|
11283 |
+
INTE
|
11284 |
+
IPAD
|
11285 |
+
IPTV
|
11286 |
+
ISBN
|
11287 |
+
ISDN
|
11288 |
+
ISIS
|
11289 |
+
ISOI
|
11290 |
+
ISRC
|
11291 |
+
ISSN
|
11292 |
+
ISTP
|
11293 |
+
ITER
|
11294 |
+
ITIL
|
11295 |
+
IUCN
|
11296 |
+
Inte
|
11297 |
+
Inve
|
11298 |
+
JACK
|
11299 |
+
JAPA
|
11300 |
+
JAVA
|
11301 |
+
JAZZ
|
11302 |
+
JBOD
|
11303 |
+
JOHN
|
11304 |
+
JOJO
|
11305 |
+
JOKE
|
11306 |
+
JOUR
|
11307 |
+
JPEG
|
11308 |
+
JUMP
|
11309 |
+
JUST
|
11310 |
+
Jack
|
11311 |
+
Jake
|
11312 |
+
Jazz
|
11313 |
+
John
|
11314 |
+
Joke
|
11315 |
+
July
|
11316 |
+
Jump
|
11317 |
+
Jung
|
11318 |
+
KING
|
11319 |
+
KISS
|
11320 |
+
KONA
|
11321 |
+
KOYO
|
11322 |
+
LASI
|
11323 |
+
LAST
|
11324 |
+
LEED
|
11325 |
+
LEEP
|
11326 |
+
LESS
|
11327 |
+
LEVE
|
11328 |
+
LEXU
|
11329 |
+
LIFE
|
11330 |
+
LIKE
|
11331 |
+
LIMI
|
11332 |
+
LINE
|
11333 |
+
LINK
|
11334 |
+
LINU
|
11335 |
+
LIST
|
11336 |
+
LIVE
|
11337 |
+
LLDP
|
11338 |
+
LOCA
|
11339 |
+
LOFT
|
11340 |
+
LOGO
|
11341 |
+
LOLI
|
11342 |
+
LONG
|
11343 |
+
LOOK
|
11344 |
+
LOVE
|
11345 |
+
LPGA
|
11346 |
+
LTPS
|
11347 |
+
LVDS
|
11348 |
+
Ligh
|
11349 |
+
Like
|
11350 |
+
Lily
|
11351 |
+
Lind
|
11352 |
+
Ling
|
11353 |
+
Liqu
|
11354 |
+
Live
|
11355 |
+
Luck
|
11356 |
+
Luke
|
11357 |
+
MACD
|
11358 |
+
MACH
|
11359 |
+
MAGI
|
11360 |
+
MALL
|
11361 |
+
MAMA
|
11362 |
+
MARK
|
11363 |
+
MAST
|
11364 |
+
MATL
|
11365 |
+
MATX
|
11366 |
+
MAYA
|
11367 |
+
MBLA
|
11368 |
+
MEDI
|
11369 |
+
MEGA
|
11370 |
+
MEMS
|
11371 |
+
MERS
|
11372 |
+
META
|
11373 |
+
MIDI
|
11374 |
+
MIDP
|
11375 |
+
MIMO
|
11376 |
+
MINI
|
11377 |
+
MIPS
|
11378 |
+
MISS
|
11379 |
+
MIUI
|
11380 |
+
MMOR
|
11381 |
+
MOBA
|
11382 |
+
MODB
|
11383 |
+
MODE
|
11384 |
+
MOMO
|
11385 |
+
MOOC
|
11386 |
+
MOON
|
11387 |
+
MORE
|
11388 |
+
MOSF
|
11389 |
+
MOTO
|
11390 |
+
MOVI
|
11391 |
+
MPEG
|
11392 |
+
MPLS
|
11393 |
+
MSCI
|
11394 |
+
MSDS
|
11395 |
+
MTBF
|
11396 |
+
MUSI
|
11397 |
+
Mach
|
11398 |
+
Make
|
11399 |
+
Maur
|
11400 |
+
Mazz
|
11401 |
+
NACH
|
11402 |
+
NADH
|
11403 |
+
NADP
|
11404 |
+
NAMC
|
11405 |
+
NAME
|
11406 |
+
NANA
|
11407 |
+
NAND
|
11408 |
+
NASA
|
11409 |
+
NASD
|
11410 |
+
NATO
|
11411 |
+
NAVE
|
11412 |
+
NCAA
|
11413 |
+
NCAP
|
11414 |
+
NCIS
|
11415 |
+
NEDC
|
11416 |
+
NEOP
|
11417 |
+
NERV
|
11418 |
+
NEST
|
11419 |
+
NEWS
|
11420 |
+
NEXT
|
11421 |
+
NICO
|
11422 |
+
NIGH
|
11423 |
+
NIKE
|
11424 |
+
NINE
|
11425 |
+
NOKI
|
11426 |
+
NOTE
|
11427 |
+
NOVA
|
11428 |
+
NSAI
|
11429 |
+
NTFS
|
11430 |
+
NTSC
|
11431 |
+
NULL
|
11432 |
+
NURB
|
11433 |
+
NVID
|
11434 |
+
NYSE
|
11435 |
+
Nove
|
11436 |
+
ODBC
|
11437 |
+
OECD
|
11438 |
+
OFDM
|
11439 |
+
OFFI
|
11440 |
+
OLAP
|
11441 |
+
OLED
|
11442 |
+
ONLI
|
11443 |
+
ONLY
|
11444 |
+
OPEC
|
11445 |
+
OPEN
|
11446 |
+
OPPO
|
11447 |
+
ORAC
|
11448 |
+
ORIC
|
11449 |
+
ORIG
|
11450 |
+
OSPF
|
11451 |
+
OVER
|
11452 |
+
Oper
|
11453 |
+
PACS
|
11454 |
+
PAGE
|
11455 |
+
PARK
|
11456 |
+
PART
|
11457 |
+
PASS
|
11458 |
+
PCMC
|
11459 |
+
PDCA
|
11460 |
+
PEEK
|
11461 |
+
PERC
|
11462 |
+
PERF
|
11463 |
+
PETS
|
11464 |
+
PHEV
|
11465 |
+
PHIL
|
11466 |
+
PHOT
|
11467 |
+
PICC
|
11468 |
+
PIEC
|
11469 |
+
PLAN
|
11470 |
+
PLAY
|
11471 |
+
PLUS
|
11472 |
+
PMMA
|
11473 |
+
PNAS
|
11474 |
+
POLO
|
11475 |
+
POSE
|
11476 |
+
POST
|
11477 |
+
POWE
|
11478 |
+
PPTP
|
11479 |
+
PPTV
|
11480 |
+
PRAD
|
11481 |
+
PROD
|
11482 |
+
PROF
|
11483 |
+
PROJ
|
11484 |
+
PSTN
|
11485 |
+
PTFE
|
11486 |
+
PUNK
|
11487 |
+
PVDF
|
11488 |
+
Pric
|
11489 |
+
Prin
|
11490 |
+
Priv
|
11491 |
+
Priz
|
11492 |
+
Prom
|
11493 |
+
QFII
|
11494 |
+
QVGA
|
11495 |
+
QVOD
|
11496 |
+
QWER
|
11497 |
+
Quic
|
11498 |
+
Quin
|
11499 |
+
Quiz
|
11500 |
+
RADI
|
11501 |
+
RAID
|
11502 |
+
RAIN
|
11503 |
+
REAC
|
11504 |
+
READ
|
11505 |
+
REAL
|
11506 |
+
REIT
|
11507 |
+
RESE
|
11508 |
+
RFID
|
11509 |
+
RIDE
|
11510 |
+
RISC
|
11511 |
+
RMON
|
11512 |
+
RMRM
|
11513 |
+
RMVB
|
11514 |
+
ROAD
|
11515 |
+
ROCK
|
11516 |
+
ROHS
|
11517 |
+
ROOT
|
11518 |
+
ROSE
|
11519 |
+
RTEC
|
11520 |
+
RWBY
|
11521 |
+
Ruby
|
11522 |
+
SAAS
|
11523 |
+
SAMS
|
11524 |
+
SARS
|
11525 |
+
SATA
|
11526 |
+
SCAD
|
11527 |
+
SCAR
|
11528 |
+
SCDM
|
11529 |
+
SCHO
|
11530 |
+
SCIE
|
11531 |
+
SCSI
|
11532 |
+
SDHC
|
11533 |
+
SDMM
|
11534 |
+
SDRA
|
11535 |
+
SDSD
|
11536 |
+
SDXC
|
11537 |
+
SECA
|
11538 |
+
SECC
|
11539 |
+
SECT
|
11540 |
+
SEED
|
11541 |
+
SEGA
|
11542 |
+
SELE
|
11543 |
+
SERV
|
11544 |
+
SEVE
|
11545 |
+
SFDA
|
11546 |
+
SHIF
|
11547 |
+
SHIN
|
11548 |
+
SHOC
|
11549 |
+
SHOP
|
11550 |
+
SHOW
|
11551 |
+
SIDE
|
11552 |
+
SIEM
|
11553 |
+
SING
|
11554 |
+
SIZE
|
11555 |
+
SKIP
|
11556 |
+
SMAP
|
11557 |
+
SMAR
|
11558 |
+
SMIL
|
11559 |
+
SMTP
|
11560 |
+
SNMP
|
11561 |
+
SOAP
|
11562 |
+
SOCK
|
11563 |
+
SOHO
|
11564 |
+
SOLO
|
11565 |
+
SONG
|
11566 |
+
SONY
|
11567 |
+
SOSO
|
11568 |
+
SOUL
|
11569 |
+
SPAC
|
11570 |
+
SPCC
|
11571 |
+
SPDI
|
11572 |
+
SPEC
|
11573 |
+
SPEE
|
11574 |
+
SPIE
|
11575 |
+
SPOR
|
11576 |
+
SPSS
|
11577 |
+
SRAM
|
11578 |
+
SSCI
|
11579 |
+
STAF
|
11580 |
+
STAG
|
11581 |
+
STAR
|
11582 |
+
STAT
|
11583 |
+
STEM
|
11584 |
+
STEP
|
11585 |
+
STER
|
11586 |
+
STOP
|
11587 |
+
STOR
|
11588 |
+
STUD
|
11589 |
+
STYL
|
11590 |
+
SUMM
|
11591 |
+
SUPE
|
11592 |
+
SUSE
|
11593 |
+
SWAT
|
11594 |
+
SWIF
|
11595 |
+
SWOT
|
11596 |
+
SYST
|
11597 |
+
Subj
|
11598 |
+
Sull
|
11599 |
+
Sund
|
11600 |
+
Sung
|
11601 |
+
Supp
|
11602 |
+
TABL
|
11603 |
+
TANK
|
11604 |
+
TCPI
|
11605 |
+
TDMA
|
11606 |
+
TEAM
|
11607 |
+
TECH
|
11608 |
+
TEST
|
11609 |
+
TEXT
|
11610 |
+
TFBO
|
11611 |
+
TFSI
|
11612 |
+
TFTP
|
11613 |
+
THIS
|
11614 |
+
THRE
|
11615 |
+
TIFF
|
11616 |
+
TIME
|
11617 |
+
TIMK
|
11618 |
+
TIPS
|
11619 |
+
TOEF
|
11620 |
+
TOKY
|
11621 |
+
TOSH
|
11622 |
+
TOUC
|
11623 |
+
TOUR
|
11624 |
+
TOWN
|
11625 |
+
TRAC
|
11626 |
+
TRIP
|
11627 |
+
TRIZ
|
11628 |
+
TRUE
|
11629 |
+
TVBS
|
11630 |
+
TVOC
|
11631 |
+
TWIC
|
11632 |
+
TYPE
|
11633 |
+
Ther
|
11634 |
+
Thin
|
11635 |
+
Thom
|
11636 |
+
Thou
|
11637 |
+
UCLA
|
11638 |
+
UHMW
|
11639 |
+
ULTR
|
11640 |
+
UMTS
|
11641 |
+
UNES
|
11642 |
+
UNIT
|
11643 |
+
UNIV
|
11644 |
+
UNIX
|
11645 |
+
Unic
|
11646 |
+
Unit
|
11647 |
+
Univ
|
11648 |
+
VAIO
|
11649 |
+
VCCI
|
11650 |
+
VEGF
|
11651 |
+
VERS
|
11652 |
+
VHDL
|
11653 |
+
VIDE
|
11654 |
+
VIER
|
11655 |
+
VIII
|
11656 |
+
VISA
|
11657 |
+
VISI
|
11658 |
+
VIST
|
11659 |
+
VIVO
|
11660 |
+
VLAN
|
11661 |
+
VLSI
|
11662 |
+
VOCA
|
11663 |
+
VOGU
|
11664 |
+
VOIP
|
11665 |
+
VRay
|
11666 |
+
VSAT
|
11667 |
+
Vick
|
11668 |
+
Vill
|
11669 |
+
WANG
|
11670 |
+
WAPI
|
11671 |
+
WASD
|
11672 |
+
WAVE
|
11673 |
+
WCBA
|
11674 |
+
WCDM
|
11675 |
+
WEEK
|
11676 |
+
WEST
|
11677 |
+
WHAT
|
11678 |
+
WHIT
|
11679 |
+
WIFI
|
11680 |
+
WIND
|
11681 |
+
WITH
|
11682 |
+
WLAN
|
11683 |
+
WORD
|
11684 |
+
WORK
|
11685 |
+
WORL
|
11686 |
+
WQVG
|
11687 |
+
WXGA
|
11688 |
+
Wang
|
11689 |
+
Wher
|
11690 |
+
WiMA
|
11691 |
+
Will
|
11692 |
+
Wind
|
11693 |
+
Wing
|
11694 |
+
XBOX
|
11695 |
+
XBRL
|
11696 |
+
XHTM
|
11697 |
+
XVID
|
11698 |
+
XXXX
|
11699 |
+
YAMA
|
11700 |
+
YANG
|
11701 |
+
YEAH
|
11702 |
+
YONE
|
11703 |
+
YOUN
|
11704 |
+
YOUR
|
11705 |
+
YOYO
|
11706 |
+
Yong
|
11707 |
+
Your
|
11708 |
+
ZAFT
|
11709 |
+
ZARA
|
11710 |
+
ZERO
|
11711 |
+
ZGMF
|
11712 |
+
ZHAN
|
11713 |
+
ZONE
|
11714 |
+
Zhon
|
11715 |
+
Zhou
|
11716 |
+
abby
|
11717 |
+
abou
|
11718 |
+
andr
|
11719 |
+
appl
|
11720 |
+
baby
|
11721 |
+
back
|
11722 |
+
blic
|
11723 |
+
call
|
11724 |
+
char
|
11725 |
+
chic
|
11726 |
+
chin
|
11727 |
+
coff
|
11728 |
+
coll
|
11729 |
+
comb
|
11730 |
+
comm
|
11731 |
+
comp
|
11732 |
+
cond
|
11733 |
+
cons
|
11734 |
+
cont
|
11735 |
+
dick
|
11736 |
+
diff
|
11737 |
+
ding
|
11738 |
+
dock
|
11739 |
+
doin
|
11740 |
+
dong
|
11741 |
+
down
|
11742 |
+
ever
|
11743 |
+
exch
|
11744 |
+
find
|
11745 |
+
foll
|
11746 |
+
four
|
11747 |
+
from
|
11748 |
+
fron
|
11749 |
+
goin
|
11750 |
+
good
|
11751 |
+
goog
|
11752 |
+
gove
|
11753 |
+
hack
|
11754 |
+
hall
|
11755 |
+
hand
|
11756 |
+
hang
|
11757 |
+
happ
|
11758 |
+
have
|
11759 |
+
here
|
11760 |
+
high
|
11761 |
+
home
|
11762 |
+
into
|
11763 |
+
inve
|
11764 |
+
jack
|
11765 |
+
java
|
11766 |
+
jazz
|
11767 |
+
jump
|
11768 |
+
jung
|
11769 |
+
just
|
11770 |
+
know
|
11771 |
+
life
|
11772 |
+
ligh
|
11773 |
+
like
|
11774 |
+
lily
|
11775 |
+
ling
|
11776 |
+
liqu
|
11777 |
+
live
|
11778 |
+
lock
|
11779 |
+
logo
|
11780 |
+
lond
|
11781 |
+
long
|
11782 |
+
look
|
11783 |
+
love
|
11784 |
+
macd
|
11785 |
+
mach
|
11786 |
+
make
|
11787 |
+
mapp
|
11788 |
+
mmer
|
11789 |
+
nove
|
11790 |
+
okay
|
11791 |
+
only
|
11792 |
+
oper
|
11793 |
+
oppo
|
11794 |
+
othe
|
11795 |
+
over
|
11796 |
+
play
|
11797 |
+
pray
|
11798 |
+
pric
|
11799 |
+
prin
|
11800 |
+
priv
|
11801 |
+
priz
|
11802 |
+
prod
|
11803 |
+
prom
|
11804 |
+
quic
|
11805 |
+
real
|
11806 |
+
requ
|
11807 |
+
righ
|
11808 |
+
scho
|
11809 |
+
shou
|
11810 |
+
show
|
11811 |
+
some
|
11812 |
+
star
|
11813 |
+
stat
|
11814 |
+
stay
|
11815 |
+
stom
|
11816 |
+
subj
|
11817 |
+
such
|
11818 |
+
suff
|
11819 |
+
supp
|
11820 |
+
take
|
11821 |
+
than
|
11822 |
+
they
|
11823 |
+
thin
|
11824 |
+
thou
|
11825 |
+
toke
|
11826 |
+
uber
|
11827 |
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unic
|
11828 |
+
univ
|
11829 |
+
upon
|
11830 |
+
usdj
|
11831 |
+
user
|
11832 |
+
usin
|
11833 |
+
vill
|
11834 |
+
vivo
|
11835 |
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wake
|
11836 |
+
wall
|
11837 |
+
wang
|
11838 |
+
want
|
11839 |
+
wave
|
11840 |
+
were
|
11841 |
+
what
|
11842 |
+
when
|
11843 |
+
wifi
|
11844 |
+
will
|
11845 |
+
wind
|
11846 |
+
wing
|
11847 |
+
with
|
11848 |
+
work
|
11849 |
+
xing
|
11850 |
+
xxxx
|
11851 |
+
year
|
11852 |
+
your
|
11853 |
+
zhon
|
11854 |
+
China
|
11855 |
+
Inter
|
11856 |
+
Journ
|
11857 |
+
china
|
11858 |
+
every
|
11859 |
+
inter
|
11860 |
+
iphon
|
11861 |
+
thing
|
11862 |
+
think
|
11863 |
+
where
|
11864 |
+
which
|
11865 |
+
Univer
|
11866 |
+
univer
|
11867 |
+
Windows
|
11868 |
+
windows
|
11869 |
+
##A
|
11870 |
+
##B
|
11871 |
+
##C
|
11872 |
+
##D
|
11873 |
+
##E
|
11874 |
+
##F
|
11875 |
+
##G
|
11876 |
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##H
|
11877 |
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##I
|
11878 |
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##J
|
11879 |
+
##K
|
11880 |
+
##L
|
11881 |
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##M
|
11882 |
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##N
|
11883 |
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##O
|
11884 |
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##P
|
11885 |
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##Q
|
11886 |
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##R
|
11887 |
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##S
|
11888 |
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##T
|
11889 |
+
##U
|
11890 |
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##V
|
11891 |
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##W
|
11892 |
+
##X
|
11893 |
+
##Y
|
11894 |
+
##Z
|
11895 |
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##a
|
11896 |
+
##b
|
11897 |
+
##c
|
11898 |
+
##d
|
11899 |
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##e
|
11900 |
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##f
|
11901 |
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##g
|
11902 |
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##h
|
11903 |
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##i
|
11904 |
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##j
|
11905 |
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##k
|
11906 |
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##l
|
11907 |
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##m
|
11908 |
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##n
|
11909 |
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##o
|
11910 |
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##p
|
11911 |
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##q
|
11912 |
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##r
|
11913 |
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|
11914 |
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##t
|
11915 |
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##u
|
11916 |
+
##v
|
11917 |
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##w
|
11918 |
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##x
|
11919 |
+
##y
|
11920 |
+
##z
|
11921 |
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##AA
|
11922 |
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##AB
|
11923 |
+
##AC
|
11924 |
+
##AD
|
11925 |
+
##AE
|
11926 |
+
##AF
|
11927 |
+
##AG
|
11928 |
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##AH
|
11929 |
+
##AI
|
11930 |
+
##AK
|
11931 |
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##AL
|
11932 |
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##AM
|
11933 |
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##AN
|
11934 |
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##AO
|
11935 |
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##AP
|
11936 |
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##AQ
|
11937 |
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##AR
|
11938 |
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##AS
|
11939 |
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##AT
|
11940 |
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##AV
|
11941 |
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##AW
|
11942 |
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##AX
|
11943 |
+
##AY
|
11944 |
+
##AZ
|
11945 |
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##BA
|
11946 |
+
##BB
|
11947 |
+
##BC
|
11948 |
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##BE
|
11949 |
+
##BG
|
11950 |
+
##BI
|
11951 |
+
##BM
|
11952 |
+
##BN
|
11953 |
+
##BO
|
11954 |
+
##BP
|
11955 |
+
##BR
|
11956 |
+
##BS
|
11957 |
+
##BT
|
11958 |
+
##BU
|
11959 |
+
##BY
|
11960 |
+
##CA
|
11961 |
+
##CB
|
11962 |
+
##CC
|
11963 |
+
##CD
|
11964 |
+
##CE
|
11965 |
+
##CF
|
11966 |
+
##CG
|
11967 |
+
##CH
|
11968 |
+
##CI
|
11969 |
+
##CK
|
11970 |
+
##CL
|
11971 |
+
##CM
|
11972 |
+
##CN
|
11973 |
+
##CO
|
11974 |
+
##CP
|
11975 |
+
##CR
|
11976 |
+
##CS
|
11977 |
+
##CT
|
11978 |
+
##CU
|
11979 |
+
##DA
|
11980 |
+
##DB
|
11981 |
+
##DC
|
11982 |
+
##DD
|
11983 |
+
##DE
|
11984 |
+
##DI
|
11985 |
+
##DL
|
11986 |
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##DM
|
11987 |
+
##DN
|
11988 |
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##DO
|
11989 |
+
##DP
|
11990 |
+
##DR
|
11991 |
+
##DS
|
11992 |
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##DT
|
11993 |
+
##DU
|
11994 |
+
##DX
|
11995 |
+
##DY
|
11996 |
+
##EA
|
11997 |
+
##EB
|
11998 |
+
##EC
|
11999 |
+
##ED
|
12000 |
+
##EE
|
12001 |
+
##EF
|
12002 |
+
##EG
|
12003 |
+
##EI
|
12004 |
+
##EK
|
12005 |
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##EL
|
12006 |
+
##EM
|
12007 |
+
##EN
|
12008 |
+
##EO
|
12009 |
+
##EP
|
12010 |
+
##ER
|
12011 |
+
##ES
|
12012 |
+
##ET
|
12013 |
+
##EV
|
12014 |
+
##EW
|
12015 |
+
##EX
|
12016 |
+
##EY
|
12017 |
+
##FA
|
12018 |
+
##FC
|
12019 |
+
##FD
|
12020 |
+
##FE
|
12021 |
+
##FF
|
12022 |
+
##FI
|
12023 |
+
##FL
|
12024 |
+
##FO
|
12025 |
+
##FP
|
12026 |
+
##FR
|
12027 |
+
##FS
|
12028 |
+
##FT
|
12029 |
+
##FU
|
12030 |
+
##FX
|
12031 |
+
##Fi
|
12032 |
+
##GA
|
12033 |
+
##GC
|
12034 |
+
##GE
|
12035 |
+
##GF
|
12036 |
+
##GH
|
12037 |
+
##GI
|
12038 |
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##GL
|
12039 |
+
##GN
|
12040 |
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##GO
|
12041 |
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##GP
|
12042 |
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##GR
|
12043 |
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##GS
|
12044 |
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##GU
|
12045 |
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##GY
|
12046 |
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##HA
|
12047 |
+
##HC
|
12048 |
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##HD
|
12049 |
+
##HE
|
12050 |
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##HG
|
12051 |
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##HI
|
12052 |
+
##HM
|
12053 |
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##HN
|
12054 |
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##HO
|
12055 |
+
##HP
|
12056 |
+
##HR
|
12057 |
+
##HS
|
12058 |
+
##HT
|
12059 |
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##IA
|
12060 |
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##IB
|
12061 |
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##IC
|
12062 |
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##ID
|
12063 |
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##IE
|
12064 |
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##IF
|
12065 |
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##IG
|
12066 |
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##II
|
12067 |
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##IK
|
12068 |
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##IL
|
12069 |
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##IM
|
12070 |
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##IN
|
12071 |
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##IO
|
12072 |
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##IP
|
12073 |
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##IR
|
12074 |
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##IS
|
12075 |
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##IT
|
12076 |
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##IU
|
12077 |
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##IV
|
12078 |
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##IX
|
12079 |
+
##IZ
|
12080 |
+
##JI
|
12081 |
+
##JO
|
12082 |
+
##Jo
|
12083 |
+
##Ju
|
12084 |
+
##KA
|
12085 |
+
##KE
|
12086 |
+
##KI
|
12087 |
+
##KK
|
12088 |
+
##KO
|
12089 |
+
##KU
|
12090 |
+
##KY
|
12091 |
+
##LA
|
12092 |
+
##LC
|
12093 |
+
##LD
|
12094 |
+
##LE
|
12095 |
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##LF
|
12096 |
+
##LG
|
12097 |
+
##LI
|
12098 |
+
##LK
|
12099 |
+
##LL
|
12100 |
+
##LM
|
12101 |
+
##LO
|
12102 |
+
##LP
|
12103 |
+
##LS
|
12104 |
+
##LT
|
12105 |
+
##LU
|
12106 |
+
##LV
|
12107 |
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##LY
|
12108 |
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##MA
|
12109 |
+
##MB
|
12110 |
+
##MC
|
12111 |
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##MD
|
12112 |
+
##ME
|
12113 |
+
##MF
|
12114 |
+
##MI
|
12115 |
+
##ML
|
12116 |
+
##MM
|
12117 |
+
##MN
|
12118 |
+
##MO
|
12119 |
+
##MP
|
12120 |
+
##MR
|
12121 |
+
##MS
|
12122 |
+
##MT
|
12123 |
+
##MV
|
12124 |
+
##MY
|
12125 |
+
##NA
|
12126 |
+
##NC
|
12127 |
+
##ND
|
12128 |
+
##NE
|
12129 |
+
##NG
|
12130 |
+
##NI
|
12131 |
+
##NJ
|
12132 |
+
##NK
|
12133 |
+
##NN
|
12134 |
+
##NO
|
12135 |
+
##NP
|
12136 |
+
##NS
|
12137 |
+
##NT
|
12138 |
+
##NU
|
12139 |
+
##NX
|
12140 |
+
##NY
|
12141 |
+
##NZ
|
12142 |
+
##OB
|
12143 |
+
##OC
|
12144 |
+
##OD
|
12145 |
+
##OE
|
12146 |
+
##OF
|
12147 |
+
##OG
|
12148 |
+
##OH
|
12149 |
+
##OI
|
12150 |
+
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|
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bert/Erlangshen-MegatronBert-1.3B-Chinese/config.json
ADDED
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{"vocab_size": 21248, "hidden_size": 2048, "num_hidden_layers": 24, "num_attention_heads": 8, "hidden_act": "gelu_new", "intermediate_size": 8192, "hidden_dropout_prob": 0.1, "attention_probs_dropout_prob": 0.1, "max_position_embeddings": 512, "type_vocab_size": 2, "initializer_range": 0.02, "layer_norm_eps": 1e-12, "gradient_checkpointing": false, "position_embedding_type": "absolute", "use_cache": false, "model_type": "megatron-bert"}
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bert/Erlangshen-MegatronBert-1.3B-Chinese/vocab.txt
ADDED
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bert/Erlangshen-MegatronBert-3.9B-Chinese/config.json
ADDED
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{
|
2 |
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"architectures": [
|
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"MegatronBertForMaskedLM"
|
4 |
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],
|
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"vocab_size": 21248,
|
6 |
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"hidden_size": 2560,
|
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"hidden_act": "gelu",
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"intermediate_size": 10240,
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"attention_probs_dropout_prob": 0.1,
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"max_position_embeddings": 512,
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"type_vocab_size": 2,
|
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"initializer_range": 0.02,
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"layer_norm_eps": 1e-12,
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"gradient_checkpointing": false,
|
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"position_embedding_type": "absolute",
|
19 |
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"use_cache": false,
|
20 |
+
"model_type": "megatron-bert"
|
21 |
+
}
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bert/Erlangshen-MegatronBert-3.9B-Chinese/special_tokens_map.json
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{
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"cls_token": "[CLS]",
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"mask_token": "[MASK]",
|
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
|
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+
"unk_token": "[UNK]"
|
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+
}
|
bert/Erlangshen-MegatronBert-3.9B-Chinese/tokenizer_config.json
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{
|
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|
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"cls_token": "[CLS]",
|
4 |
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"do_basic_tokenize": true,
|
5 |
+
"do_lower_case": true,
|
6 |
+
"mask_token": "[MASK]",
|
7 |
+
"name_or_path": "/cognitive_comp/gaoxinyu/hf_hub/Erlangshen-MegatronBert-3.9B",
|
8 |
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"never_split": null,
|
9 |
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"pad_token": "[PAD]",
|
10 |
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"sep_token": "[SEP]",
|
11 |
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"special_tokens_map_file": null,
|
12 |
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"strip_accents": null,
|
13 |
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"tokenize_chinese_chars": true,
|
14 |
+
"tokenizer_class": "BertTokenizer",
|
15 |
+
"unk_token": "[UNK]"
|
16 |
+
}
|
bert/Erlangshen-MegatronBert-3.9B-Chinese/vocab.txt
ADDED
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bert/bert-base-japanese-v3/.gitattributes
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*.7z filter=lfs diff=lfs merge=lfs -text
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*.ot filter=lfs diff=lfs merge=lfs -text
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*.pb filter=lfs diff=lfs merge=lfs -text
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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bert/bert-base-japanese-v3/README.md
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1 |
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---
|
2 |
+
license: apache-2.0
|
3 |
+
datasets:
|
4 |
+
- cc100
|
5 |
+
- wikipedia
|
6 |
+
language:
|
7 |
+
- ja
|
8 |
+
widget:
|
9 |
+
- text: 東北大学で[MASK]の研究をしています。
|
10 |
+
---
|
11 |
+
|
12 |
+
# BERT base Japanese (unidic-lite with whole word masking, CC-100 and jawiki-20230102)
|
13 |
+
|
14 |
+
This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language.
|
15 |
+
|
16 |
+
This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in [unidic-lite](https://pypi.org/project/unidic-lite/) package), followed by the WordPiece subword tokenization.
|
17 |
+
Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective.
|
18 |
+
|
19 |
+
The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/).
|
20 |
+
|
21 |
+
## Model architecture
|
22 |
+
|
23 |
+
The model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads.
|
24 |
+
|
25 |
+
## Training Data
|
26 |
+
|
27 |
+
The model is trained on the Japanese portion of [CC-100 dataset](https://data.statmt.org/cc-100/) and the Japanese version of Wikipedia.
|
28 |
+
For Wikipedia, we generated a text corpus from the [Wikipedia Cirrussearch dump file](https://dumps.wikimedia.org/other/cirrussearch/) as of January 2, 2023.
|
29 |
+
The corpus files generated from CC-100 and Wikipedia are 74.3GB and 4.9GB in size and consist of approximately 392M and 34M sentences, respectively.
|
30 |
+
|
31 |
+
For the purpose of splitting texts into sentences, we used [fugashi](https://github.com/polm/fugashi) with [mecab-ipadic-NEologd](https://github.com/neologd/mecab-ipadic-neologd) dictionary (v0.0.7).
|
32 |
+
|
33 |
+
## Tokenization
|
34 |
+
|
35 |
+
The texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into subwords by the WordPiece algorithm.
|
36 |
+
The vocabulary size is 32768.
|
37 |
+
|
38 |
+
We used [fugashi](https://github.com/polm/fugashi) and [unidic-lite](https://github.com/polm/unidic-lite) packages for the tokenization.
|
39 |
+
|
40 |
+
## Training
|
41 |
+
|
42 |
+
We trained the model first on the CC-100 corpus for 1M steps and then on the Wikipedia corpus for another 1M steps.
|
43 |
+
For training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once.
|
44 |
+
|
45 |
+
For training of each model, we used a v3-8 instance of Cloud TPUs provided by [TPU Research Cloud](https://sites.research.google/trc/about/).
|
46 |
+
|
47 |
+
## Licenses
|
48 |
+
|
49 |
+
The pretrained models are distributed under the Apache License 2.0.
|
50 |
+
|
51 |
+
## Acknowledgments
|
52 |
+
|
53 |
+
This model is trained with Cloud TPUs provided by [TPU Research Cloud](https://sites.research.google/trc/about/) program.
|
bert/bert-base-japanese-v3/config.json
ADDED
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|
|
1 |
+
{
|
2 |
+
"architectures": [
|
3 |
+
"BertForPreTraining"
|
4 |
+
],
|
5 |
+
"attention_probs_dropout_prob": 0.1,
|
6 |
+
"hidden_act": "gelu",
|
7 |
+
"hidden_dropout_prob": 0.1,
|
8 |
+
"hidden_size": 768,
|
9 |
+
"initializer_range": 0.02,
|
10 |
+
"intermediate_size": 3072,
|
11 |
+
"layer_norm_eps": 1e-12,
|
12 |
+
"max_position_embeddings": 512,
|
13 |
+
"model_type": "bert",
|
14 |
+
"num_attention_heads": 12,
|
15 |
+
"num_hidden_layers": 12,
|
16 |
+
"pad_token_id": 0,
|
17 |
+
"type_vocab_size": 2,
|
18 |
+
"vocab_size": 32768
|
19 |
+
}
|
bert/bert-base-japanese-v3/tokenizer_config.json
ADDED
@@ -0,0 +1,10 @@
|
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|
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|
1 |
+
{
|
2 |
+
"tokenizer_class": "BertJapaneseTokenizer",
|
3 |
+
"model_max_length": 512,
|
4 |
+
"do_lower_case": false,
|
5 |
+
"word_tokenizer_type": "mecab",
|
6 |
+
"subword_tokenizer_type": "wordpiece",
|
7 |
+
"mecab_kwargs": {
|
8 |
+
"mecab_dic": "unidic_lite"
|
9 |
+
}
|
10 |
+
}
|
bert/bert-base-japanese-v3/vocab.txt
ADDED
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|
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bert/bert-large-japanese-v2/.gitattributes
ADDED
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*.tflite filter=lfs diff=lfs merge=lfs -text
|
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*.tgz filter=lfs diff=lfs merge=lfs -text
|
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*.wasm filter=lfs diff=lfs merge=lfs -text
|
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*.xz filter=lfs diff=lfs merge=lfs -text
|
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*.zip filter=lfs diff=lfs merge=lfs -text
|
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*.zst filter=lfs diff=lfs merge=lfs -text
|
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*tfevents* filter=lfs diff=lfs merge=lfs -text
|
bert/bert-large-japanese-v2/README.md
ADDED
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1 |
+
---
|
2 |
+
license: apache-2.0
|
3 |
+
datasets:
|
4 |
+
- cc100
|
5 |
+
- wikipedia
|
6 |
+
language:
|
7 |
+
- ja
|
8 |
+
widget:
|
9 |
+
- text: 東北大学で[MASK]の研究をしています。
|
10 |
+
---
|
11 |
+
|
12 |
+
# BERT large Japanese (unidic-lite with whole word masking, CC-100 and jawiki-20230102)
|
13 |
+
|
14 |
+
This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language.
|
15 |
+
|
16 |
+
This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in [unidic-lite](https://pypi.org/project/unidic-lite/) package), followed by the WordPiece subword tokenization.
|
17 |
+
Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective.
|
18 |
+
|
19 |
+
The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/).
|
20 |
+
|
21 |
+
## Model architecture
|
22 |
+
|
23 |
+
The model architecture is the same as the original BERT large model; 24 layers, 1024 dimensions of hidden states, and 16 attention heads.
|
24 |
+
|
25 |
+
## Training Data
|
26 |
+
|
27 |
+
The model is trained on the Japanese portion of [CC-100 dataset](https://data.statmt.org/cc-100/) and the Japanese version of Wikipedia.
|
28 |
+
For Wikipedia, we generated a text corpus from the [Wikipedia Cirrussearch dump file](https://dumps.wikimedia.org/other/cirrussearch/) as of January 2, 2023.
|
29 |
+
The corpus files generated from CC-100 and Wikipedia are 74.3GB and 4.9GB in size and consist of approximately 392M and 34M sentences, respectively.
|
30 |
+
|
31 |
+
For the purpose of splitting texts into sentences, we used [fugashi](https://github.com/polm/fugashi) with [mecab-ipadic-NEologd](https://github.com/neologd/mecab-ipadic-neologd) dictionary (v0.0.7).
|
32 |
+
|
33 |
+
## Tokenization
|
34 |
+
|
35 |
+
The texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into subwords by the WordPiece algorithm.
|
36 |
+
The vocabulary size is 32768.
|
37 |
+
|
38 |
+
We used [fugashi](https://github.com/polm/fugashi) and [unidic-lite](https://github.com/polm/unidic-lite) packages for the tokenization.
|
39 |
+
|
40 |
+
## Training
|
41 |
+
|
42 |
+
We trained the model first on the CC-100 corpus for 1M steps and then on the Wikipedia corpus for another 1M steps.
|
43 |
+
For training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once.
|
44 |
+
|
45 |
+
For training of each model, we used a v3-8 instance of Cloud TPUs provided by [TPU Research Cloud](https://sites.research.google/trc/about/).
|
46 |
+
|
47 |
+
## Licenses
|
48 |
+
|
49 |
+
The pretrained models are distributed under the Apache License 2.0.
|
50 |
+
|
51 |
+
## Acknowledgments
|
52 |
+
|
53 |
+
This model is trained with Cloud TPUs provided by [TPU Research Cloud](https://sites.research.google/trc/about/) program.
|
bert/bert-large-japanese-v2/config.json
ADDED
@@ -0,0 +1,19 @@
|
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|
|
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|
|
|
1 |
+
{
|
2 |
+
"architectures": [
|
3 |
+
"BertForPreTraining"
|
4 |
+
],
|
5 |
+
"attention_probs_dropout_prob": 0.1,
|
6 |
+
"hidden_act": "gelu",
|
7 |
+
"hidden_dropout_prob": 0.1,
|
8 |
+
"hidden_size": 1024,
|
9 |
+
"initializer_range": 0.02,
|
10 |
+
"intermediate_size": 4096,
|
11 |
+
"layer_norm_eps": 1e-12,
|
12 |
+
"max_position_embeddings": 512,
|
13 |
+
"model_type": "bert",
|
14 |
+
"num_attention_heads": 16,
|
15 |
+
"num_hidden_layers": 24,
|
16 |
+
"pad_token_id": 0,
|
17 |
+
"type_vocab_size": 2,
|
18 |
+
"vocab_size": 32768
|
19 |
+
}
|
bert/bert-large-japanese-v2/tokenizer_config.json
ADDED
@@ -0,0 +1,10 @@
|
|
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|
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|
1 |
+
{
|
2 |
+
"tokenizer_class": "BertJapaneseTokenizer",
|
3 |
+
"model_max_length": 512,
|
4 |
+
"do_lower_case": false,
|
5 |
+
"word_tokenizer_type": "mecab",
|
6 |
+
"subword_tokenizer_type": "wordpiece",
|
7 |
+
"mecab_kwargs": {
|
8 |
+
"mecab_dic": "unidic_lite"
|
9 |
+
}
|
10 |
+
}
|
bert/bert-large-japanese-v2/vocab.txt
ADDED
The diff for this file is too large to render.
See raw diff
|
|
bert/bert_models.json
ADDED
@@ -0,0 +1,14 @@
|
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|
|
1 |
+
{
|
2 |
+
"deberta-v2-large-japanese-char-wwm": {
|
3 |
+
"repo_id": "ku-nlp/deberta-v2-large-japanese-char-wwm",
|
4 |
+
"files": ["pytorch_model.bin"]
|
5 |
+
},
|
6 |
+
"chinese-roberta-wwm-ext-large": {
|
7 |
+
"repo_id": "hfl/chinese-roberta-wwm-ext-large",
|
8 |
+
"files": ["pytorch_model.bin"]
|
9 |
+
},
|
10 |
+
"deberta-v3-large": {
|
11 |
+
"repo_id": "microsoft/deberta-v3-large",
|
12 |
+
"files": ["spm.model", "pytorch_model.bin"]
|
13 |
+
}
|
14 |
+
}
|
bert/chinese-roberta-wwm-ext-large/.gitattributes
ADDED
@@ -0,0 +1,9 @@
|
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|
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|
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+
*.bin.* filter=lfs diff=lfs merge=lfs -text
|
2 |
+
*.lfs.* filter=lfs diff=lfs merge=lfs -text
|
3 |
+
*.bin filter=lfs diff=lfs merge=lfs -text
|
4 |
+
*.h5 filter=lfs diff=lfs merge=lfs -text
|
5 |
+
*.tflite filter=lfs diff=lfs merge=lfs -text
|
6 |
+
*.tar.gz filter=lfs diff=lfs merge=lfs -text
|
7 |
+
*.ot filter=lfs diff=lfs merge=lfs -text
|
8 |
+
*.onnx filter=lfs diff=lfs merge=lfs -text
|
9 |
+
*.msgpack filter=lfs diff=lfs merge=lfs -text
|
bert/chinese-roberta-wwm-ext-large/README.md
ADDED
@@ -0,0 +1,57 @@
|
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|
1 |
+
---
|
2 |
+
language:
|
3 |
+
- zh
|
4 |
+
tags:
|
5 |
+
- bert
|
6 |
+
license: "apache-2.0"
|
7 |
+
---
|
8 |
+
|
9 |
+
# Please use 'Bert' related functions to load this model!
|
10 |
+
|
11 |
+
## Chinese BERT with Whole Word Masking
|
12 |
+
For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
|
13 |
+
|
14 |
+
**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**
|
15 |
+
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
|
16 |
+
|
17 |
+
This repository is developed based on:https://github.com/google-research/bert
|
18 |
+
|
19 |
+
You may also interested in,
|
20 |
+
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
|
21 |
+
- Chinese MacBERT: https://github.com/ymcui/MacBERT
|
22 |
+
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
|
23 |
+
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
|
24 |
+
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
|
25 |
+
|
26 |
+
More resources by HFL: https://github.com/ymcui/HFL-Anthology
|
27 |
+
|
28 |
+
## Citation
|
29 |
+
If you find the technical report or resource is useful, please cite the following technical report in your paper.
|
30 |
+
- Primary: https://arxiv.org/abs/2004.13922
|
31 |
+
```
|
32 |
+
@inproceedings{cui-etal-2020-revisiting,
|
33 |
+
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
|
34 |
+
author = "Cui, Yiming and
|
35 |
+
Che, Wanxiang and
|
36 |
+
Liu, Ting and
|
37 |
+
Qin, Bing and
|
38 |
+
Wang, Shijin and
|
39 |
+
Hu, Guoping",
|
40 |
+
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
|
41 |
+
month = nov,
|
42 |
+
year = "2020",
|
43 |
+
address = "Online",
|
44 |
+
publisher = "Association for Computational Linguistics",
|
45 |
+
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
|
46 |
+
pages = "657--668",
|
47 |
+
}
|
48 |
+
```
|
49 |
+
- Secondary: https://arxiv.org/abs/1906.08101
|
50 |
+
```
|
51 |
+
@article{chinese-bert-wwm,
|
52 |
+
title={Pre-Training with Whole Word Masking for Chinese BERT},
|
53 |
+
author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping},
|
54 |
+
journal={arXiv preprint arXiv:1906.08101},
|
55 |
+
year={2019}
|
56 |
+
}
|
57 |
+
```
|
bert/chinese-roberta-wwm-ext-large/added_tokens.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{}
|
bert/chinese-roberta-wwm-ext-large/config.json
ADDED
@@ -0,0 +1,28 @@
|
|
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|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"architectures": [
|
3 |
+
"BertForMaskedLM"
|
4 |
+
],
|
5 |
+
"attention_probs_dropout_prob": 0.1,
|
6 |
+
"bos_token_id": 0,
|
7 |
+
"directionality": "bidi",
|
8 |
+
"eos_token_id": 2,
|
9 |
+
"hidden_act": "gelu",
|
10 |
+
"hidden_dropout_prob": 0.1,
|
11 |
+
"hidden_size": 1024,
|
12 |
+
"initializer_range": 0.02,
|
13 |
+
"intermediate_size": 4096,
|
14 |
+
"layer_norm_eps": 1e-12,
|
15 |
+
"max_position_embeddings": 512,
|
16 |
+
"model_type": "bert",
|
17 |
+
"num_attention_heads": 16,
|
18 |
+
"num_hidden_layers": 24,
|
19 |
+
"output_past": true,
|
20 |
+
"pad_token_id": 0,
|
21 |
+
"pooler_fc_size": 768,
|
22 |
+
"pooler_num_attention_heads": 12,
|
23 |
+
"pooler_num_fc_layers": 3,
|
24 |
+
"pooler_size_per_head": 128,
|
25 |
+
"pooler_type": "first_token_transform",
|
26 |
+
"type_vocab_size": 2,
|
27 |
+
"vocab_size": 21128
|
28 |
+
}
|
bert/chinese-roberta-wwm-ext-large/special_tokens_map.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}
|
bert/chinese-roberta-wwm-ext-large/tokenizer.json
ADDED
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bert/chinese-roberta-wwm-ext-large/tokenizer_config.json
ADDED
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{"init_inputs": []}
|
bert/chinese-roberta-wwm-ext-large/vocab.txt
ADDED
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See raw diff
|
|
bert/deberta-v2-large-japanese-char-wwm/.gitattributes
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*.7z filter=lfs diff=lfs merge=lfs -text
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*.arrow filter=lfs diff=lfs merge=lfs -text
|
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*.bin filter=lfs diff=lfs merge=lfs -text
|
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+
*.bz2 filter=lfs diff=lfs merge=lfs -text
|
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+
*.ckpt filter=lfs diff=lfs merge=lfs -text
|
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+
*.ftz filter=lfs diff=lfs merge=lfs -text
|
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*.gz filter=lfs diff=lfs merge=lfs -text
|
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*.h5 filter=lfs diff=lfs merge=lfs -text
|
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+
*.joblib filter=lfs diff=lfs merge=lfs -text
|
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+
*.lfs.* filter=lfs diff=lfs merge=lfs -text
|
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+
*.mlmodel filter=lfs diff=lfs merge=lfs -text
|
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*.model filter=lfs diff=lfs merge=lfs -text
|
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+
*.msgpack filter=lfs diff=lfs merge=lfs -text
|
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*.npy filter=lfs diff=lfs merge=lfs -text
|
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*.npz filter=lfs diff=lfs merge=lfs -text
|
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*.onnx filter=lfs diff=lfs merge=lfs -text
|
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*.ot filter=lfs diff=lfs merge=lfs -text
|
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*.parquet filter=lfs diff=lfs merge=lfs -text
|
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*.pb filter=lfs diff=lfs merge=lfs -text
|
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+
*.pickle filter=lfs diff=lfs merge=lfs -text
|
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+
*.pkl filter=lfs diff=lfs merge=lfs -text
|
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+
*.pt filter=lfs diff=lfs merge=lfs -text
|
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+
*.pth filter=lfs diff=lfs merge=lfs -text
|
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+
*.rar filter=lfs diff=lfs merge=lfs -text
|
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+
*.safetensors filter=lfs diff=lfs merge=lfs -text
|
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+
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
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+
*.tar.* filter=lfs diff=lfs merge=lfs -text
|
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+
*.tflite filter=lfs diff=lfs merge=lfs -text
|
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*.tgz filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
|
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*.zst filter=lfs diff=lfs merge=lfs -text
|
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*tfevents* filter=lfs diff=lfs merge=lfs -text
|
bert/deberta-v2-large-japanese-char-wwm/README.md
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|
1 |
+
---
|
2 |
+
language: ja
|
3 |
+
license: cc-by-sa-4.0
|
4 |
+
library_name: transformers
|
5 |
+
tags:
|
6 |
+
- deberta
|
7 |
+
- deberta-v2
|
8 |
+
- fill-mask
|
9 |
+
- character
|
10 |
+
- wwm
|
11 |
+
datasets:
|
12 |
+
- wikipedia
|
13 |
+
- cc100
|
14 |
+
- oscar
|
15 |
+
metrics:
|
16 |
+
- accuracy
|
17 |
+
mask_token: "[MASK]"
|
18 |
+
widget:
|
19 |
+
- text: "京都大学で自然言語処理を[MASK][MASK]する。"
|
20 |
+
---
|
21 |
+
|
22 |
+
# Model Card for Japanese character-level DeBERTa V2 large
|
23 |
+
|
24 |
+
## Model description
|
25 |
+
|
26 |
+
This is a Japanese DeBERTa V2 large model pre-trained on Japanese Wikipedia, the Japanese portion of CC-100, and the Japanese portion of OSCAR.
|
27 |
+
This model is trained with character-level tokenization and whole word masking.
|
28 |
+
|
29 |
+
## How to use
|
30 |
+
|
31 |
+
You can use this model for masked language modeling as follows:
|
32 |
+
|
33 |
+
```python
|
34 |
+
from transformers import AutoTokenizer, AutoModelForMaskedLM
|
35 |
+
tokenizer = AutoTokenizer.from_pretrained('ku-nlp/deberta-v2-large-japanese-char-wwm')
|
36 |
+
model = AutoModelForMaskedLM.from_pretrained('ku-nlp/deberta-v2-large-japanese-char-wwm')
|
37 |
+
|
38 |
+
sentence = '京都大学で自然言語処理を[MASK][MASK]する。'
|
39 |
+
encoding = tokenizer(sentence, return_tensors='pt')
|
40 |
+
...
|
41 |
+
```
|
42 |
+
|
43 |
+
You can also fine-tune this model on downstream tasks.
|
44 |
+
|
45 |
+
## Tokenization
|
46 |
+
|
47 |
+
There is no need to tokenize texts in advance, and you can give raw texts to the tokenizer.
|
48 |
+
The texts are tokenized into character-level tokens by [sentencepiece](https://github.com/google/sentencepiece).
|
49 |
+
|
50 |
+
## Training data
|
51 |
+
|
52 |
+
We used the following corpora for pre-training:
|
53 |
+
|
54 |
+
- Japanese Wikipedia (as of 20221020, 3.2GB, 27M sentences, 1.3M documents)
|
55 |
+
- Japanese portion of CC-100 (85GB, 619M sentences, 66M documents)
|
56 |
+
- Japanese portion of OSCAR (54GB, 326M sentences, 25M documents)
|
57 |
+
|
58 |
+
Note that we filtered out documents annotated with "header", "footer", or "noisy" tags in OSCAR.
|
59 |
+
Also note that Japanese Wikipedia was duplicated 10 times to make the total size of the corpus comparable to that of CC-100 and OSCAR. As a result, the total size of the training data is 171GB.
|
60 |
+
|
61 |
+
## Training procedure
|
62 |
+
|
63 |
+
We first segmented texts in the corpora into words using [Juman++ 2.0.0-rc3](https://github.com/ku-nlp/jumanpp/releases/tag/v2.0.0-rc3) for whole word masking.
|
64 |
+
Then, we built a sentencepiece model with 22,012 tokens including all characters that appear in the training corpus.
|
65 |
+
|
66 |
+
We tokenized raw corpora into character-level subwords using the sentencepiece model and trained the Japanese DeBERTa model using [transformers](https://github.com/huggingface/transformers) library.
|
67 |
+
The training took 26 days using 16 NVIDIA A100-SXM4-40GB GPUs.
|
68 |
+
|
69 |
+
The following hyperparameters were used during pre-training:
|
70 |
+
|
71 |
+
- learning_rate: 1e-4
|
72 |
+
- per_device_train_batch_size: 26
|
73 |
+
- distributed_type: multi-GPU
|
74 |
+
- num_devices: 16
|
75 |
+
- gradient_accumulation_steps: 8
|
76 |
+
- total_train_batch_size: 3,328
|
77 |
+
- max_seq_length: 512
|
78 |
+
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
|
79 |
+
- lr_scheduler_type: linear schedule with warmup (lr = 0 at 300k steps)
|
80 |
+
- training_steps: 260,000
|
81 |
+
- warmup_steps: 10,000
|
82 |
+
|
83 |
+
The accuracy of the trained model on the masked language modeling task was 0.795.
|
84 |
+
The evaluation set consists of 5,000 randomly sampled documents from each of the training corpora.
|
85 |
+
|
86 |
+
## Acknowledgments
|
87 |
+
|
88 |
+
This work was supported by Joint Usage/Research Center for Interdisciplinary Large-scale Information Infrastructures (JHPCN) through General Collaboration Project no. jh221004, "Developing a Platform for Constructing and Sharing of Large-Scale Japanese Language Models".
|
89 |
+
For training models, we used the mdx: a platform for the data-driven future.
|
bert/deberta-v2-large-japanese-char-wwm/config.json
ADDED
@@ -0,0 +1,37 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"architectures": [
|
3 |
+
"DebertaV2ForMaskedLM"
|
4 |
+
],
|
5 |
+
"attention_head_size": 64,
|
6 |
+
"attention_probs_dropout_prob": 0.1,
|
7 |
+
"conv_act": "gelu",
|
8 |
+
"conv_kernel_size": 3,
|
9 |
+
"hidden_act": "gelu",
|
10 |
+
"hidden_dropout_prob": 0.1,
|
11 |
+
"hidden_size": 1024,
|
12 |
+
"initializer_range": 0.02,
|
13 |
+
"intermediate_size": 4096,
|
14 |
+
"layer_norm_eps": 1e-07,
|
15 |
+
"max_position_embeddings": 512,
|
16 |
+
"max_relative_positions": -1,
|
17 |
+
"model_type": "deberta-v2",
|
18 |
+
"norm_rel_ebd": "layer_norm",
|
19 |
+
"num_attention_heads": 16,
|
20 |
+
"num_hidden_layers": 24,
|
21 |
+
"pad_token_id": 0,
|
22 |
+
"pooler_dropout": 0,
|
23 |
+
"pooler_hidden_act": "gelu",
|
24 |
+
"pooler_hidden_size": 1024,
|
25 |
+
"pos_att_type": [
|
26 |
+
"p2c",
|
27 |
+
"c2p"
|
28 |
+
],
|
29 |
+
"position_biased_input": false,
|
30 |
+
"position_buckets": 256,
|
31 |
+
"relative_attention": true,
|
32 |
+
"share_att_key": true,
|
33 |
+
"torch_dtype": "float16",
|
34 |
+
"transformers_version": "4.25.1",
|
35 |
+
"type_vocab_size": 0,
|
36 |
+
"vocab_size": 22012
|
37 |
+
}
|
bert/deberta-v2-large-japanese-char-wwm/pytorch_model.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:bf0dab8ad87bd7c22e85ec71e04f2240804fda6d33196157d6b5923af6ea1201
|
3 |
+
size 1318456639
|
bert/deberta-v2-large-japanese-char-wwm/special_tokens_map.json
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"cls_token": "[CLS]",
|
3 |
+
"mask_token": "[MASK]",
|
4 |
+
"pad_token": "[PAD]",
|
5 |
+
"sep_token": "[SEP]",
|
6 |
+
"unk_token": "[UNK]"
|
7 |
+
}
|
bert/deberta-v2-large-japanese-char-wwm/tokenizer_config.json
ADDED
@@ -0,0 +1,19 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
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|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"cls_token": "[CLS]",
|
3 |
+
"do_lower_case": false,
|
4 |
+
"do_subword_tokenize": true,
|
5 |
+
"do_word_tokenize": true,
|
6 |
+
"jumanpp_kwargs": null,
|
7 |
+
"mask_token": "[MASK]",
|
8 |
+
"mecab_kwargs": null,
|
9 |
+
"model_max_length": 1000000000000000019884624838656,
|
10 |
+
"never_split": null,
|
11 |
+
"pad_token": "[PAD]",
|
12 |
+
"sep_token": "[SEP]",
|
13 |
+
"special_tokens_map_file": null,
|
14 |
+
"subword_tokenizer_type": "character",
|
15 |
+
"sudachi_kwargs": null,
|
16 |
+
"tokenizer_class": "BertJapaneseTokenizer",
|
17 |
+
"unk_token": "[UNK]",
|
18 |
+
"word_tokenizer_type": "basic"
|
19 |
+
}
|
bert/deberta-v2-large-japanese-char-wwm/vocab.txt
ADDED
The diff for this file is too large to render.
See raw diff
|
|
bert/deberta-v2-large-japanese/.gitattributes
ADDED
@@ -0,0 +1,34 @@
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|
1 |
+
*.7z filter=lfs diff=lfs merge=lfs -text
|
2 |
+
*.arrow filter=lfs diff=lfs merge=lfs -text
|
3 |
+
*.bin filter=lfs diff=lfs merge=lfs -text
|
4 |
+
*.bz2 filter=lfs diff=lfs merge=lfs -text
|
5 |
+
*.ckpt filter=lfs diff=lfs merge=lfs -text
|
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+
*.ftz filter=lfs diff=lfs merge=lfs -text
|
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+
*.gz filter=lfs diff=lfs merge=lfs -text
|
8 |
+
*.h5 filter=lfs diff=lfs merge=lfs -text
|
9 |
+
*.joblib filter=lfs diff=lfs merge=lfs -text
|
10 |
+
*.lfs.* filter=lfs diff=lfs merge=lfs -text
|
11 |
+
*.mlmodel filter=lfs diff=lfs merge=lfs -text
|
12 |
+
*.model filter=lfs diff=lfs merge=lfs -text
|
13 |
+
*.msgpack filter=lfs diff=lfs merge=lfs -text
|
14 |
+
*.npy filter=lfs diff=lfs merge=lfs -text
|
15 |
+
*.npz filter=lfs diff=lfs merge=lfs -text
|
16 |
+
*.onnx filter=lfs diff=lfs merge=lfs -text
|
17 |
+
*.ot filter=lfs diff=lfs merge=lfs -text
|
18 |
+
*.parquet filter=lfs diff=lfs merge=lfs -text
|
19 |
+
*.pb filter=lfs diff=lfs merge=lfs -text
|
20 |
+
*.pickle filter=lfs diff=lfs merge=lfs -text
|
21 |
+
*.pkl filter=lfs diff=lfs merge=lfs -text
|
22 |
+
*.pt filter=lfs diff=lfs merge=lfs -text
|
23 |
+
*.pth filter=lfs diff=lfs merge=lfs -text
|
24 |
+
*.rar filter=lfs diff=lfs merge=lfs -text
|
25 |
+
*.safetensors filter=lfs diff=lfs merge=lfs -text
|
26 |
+
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
27 |
+
*.tar.* filter=lfs diff=lfs merge=lfs -text
|
28 |
+
*.tflite filter=lfs diff=lfs merge=lfs -text
|
29 |
+
*.tgz filter=lfs diff=lfs merge=lfs -text
|
30 |
+
*.wasm filter=lfs diff=lfs merge=lfs -text
|
31 |
+
*.xz filter=lfs diff=lfs merge=lfs -text
|
32 |
+
*.zip filter=lfs diff=lfs merge=lfs -text
|
33 |
+
*.zst filter=lfs diff=lfs merge=lfs -text
|
34 |
+
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
bert/deberta-v2-large-japanese/README.md
ADDED
@@ -0,0 +1,111 @@
|
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|
|
|
|
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|
|
|
|
|
1 |
+
---
|
2 |
+
language: ja
|
3 |
+
license: cc-by-sa-4.0
|
4 |
+
library_name: transformers
|
5 |
+
tags:
|
6 |
+
- deberta
|
7 |
+
- deberta-v2
|
8 |
+
- fill-mask
|
9 |
+
datasets:
|
10 |
+
- wikipedia
|
11 |
+
- cc100
|
12 |
+
- oscar
|
13 |
+
metrics:
|
14 |
+
- accuracy
|
15 |
+
mask_token: "[MASK]"
|
16 |
+
widget:
|
17 |
+
- text: "京都 大学 で 自然 言語 処理 を [MASK] する 。"
|
18 |
+
---
|
19 |
+
|
20 |
+
# Model Card for Japanese DeBERTa V2 large
|
21 |
+
|
22 |
+
## Model description
|
23 |
+
|
24 |
+
This is a Japanese DeBERTa V2 large model pre-trained on Japanese Wikipedia, the Japanese portion of CC-100, and the
|
25 |
+
Japanese portion of OSCAR.
|
26 |
+
|
27 |
+
## How to use
|
28 |
+
|
29 |
+
You can use this model for masked language modeling as follows:
|
30 |
+
|
31 |
+
```python
|
32 |
+
from transformers import AutoTokenizer, AutoModelForMaskedLM
|
33 |
+
|
34 |
+
tokenizer = AutoTokenizer.from_pretrained('ku-nlp/deberta-v2-large-japanese')
|
35 |
+
model = AutoModelForMaskedLM.from_pretrained('ku-nlp/deberta-v2-large-japanese')
|
36 |
+
|
37 |
+
sentence = '京都 大学 で 自然 言語 処理 を [MASK] する 。' # input should be segmented into words by Juman++ in advance
|
38 |
+
encoding = tokenizer(sentence, return_tensors='pt')
|
39 |
+
...
|
40 |
+
```
|
41 |
+
|
42 |
+
You can also fine-tune this model on downstream tasks.
|
43 |
+
|
44 |
+
## Tokenization
|
45 |
+
|
46 |
+
The input text should be segmented into words by [Juman++](https://github.com/ku-nlp/jumanpp) in
|
47 |
+
advance. [Juman++ 2.0.0-rc3](https://github.com/ku-nlp/jumanpp/releases/tag/v2.0.0-rc3) was used for pre-training. Each
|
48 |
+
word is tokenized into subwords by [sentencepiece](https://github.com/google/sentencepiece).
|
49 |
+
|
50 |
+
## Training data
|
51 |
+
|
52 |
+
We used the following corpora for pre-training:
|
53 |
+
|
54 |
+
- Japanese Wikipedia (as of 20221020, 3.2GB, 27M sentences, 1.3M documents)
|
55 |
+
- Japanese portion of CC-100 (85GB, 619M sentences, 66M documents)
|
56 |
+
- Japanese portion of OSCAR (54GB, 326M sentences, 25M documents)
|
57 |
+
|
58 |
+
Note that we filtered out documents annotated with "header", "footer", or "noisy" tags in OSCAR.
|
59 |
+
Also note that Japanese Wikipedia was duplicated 10 times to make the total size of the corpus comparable to that of
|
60 |
+
CC-100 and OSCAR. As a result, the total size of the training data is 171GB.
|
61 |
+
|
62 |
+
## Training procedure
|
63 |
+
|
64 |
+
We first segmented texts in the corpora into words using [Juman++](https://github.com/ku-nlp/jumanpp).
|
65 |
+
Then, we built a sentencepiece model with 32000 tokens including words ([JumanDIC](https://github.com/ku-nlp/JumanDIC))
|
66 |
+
and subwords induced by the unigram language model of [sentencepiece](https://github.com/google/sentencepiece).
|
67 |
+
|
68 |
+
We tokenized the segmented corpora into subwords using the sentencepiece model and trained the Japanese DeBERTa model
|
69 |
+
using [transformers](https://github.com/huggingface/transformers) library.
|
70 |
+
The training took 36 days using 8 NVIDIA A100-SXM4-40GB GPUs.
|
71 |
+
|
72 |
+
The following hyperparameters were used during pre-training:
|
73 |
+
|
74 |
+
- learning_rate: 1e-4
|
75 |
+
- per_device_train_batch_size: 18
|
76 |
+
- distributed_type: multi-GPU
|
77 |
+
- num_devices: 8
|
78 |
+
- gradient_accumulation_steps: 16
|
79 |
+
- total_train_batch_size: 2,304
|
80 |
+
- max_seq_length: 512
|
81 |
+
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
|
82 |
+
- lr_scheduler_type: linear schedule with warmup
|
83 |
+
- training_steps: 300,000
|
84 |
+
- warmup_steps: 10,000
|
85 |
+
|
86 |
+
The accuracy of the trained model on the masked language modeling task was 0.799.
|
87 |
+
The evaluation set consists of 5,000 randomly sampled documents from each of the training corpora.
|
88 |
+
|
89 |
+
## Fine-tuning on NLU tasks
|
90 |
+
|
91 |
+
We fine-tuned the following models and evaluated them on the dev set of JGLUE.
|
92 |
+
We tuned learning rate and training epochs for each model and task
|
93 |
+
following [the JGLUE paper](https://www.jstage.jst.go.jp/article/jnlp/30/1/30_63/_pdf/-char/ja).
|
94 |
+
|
95 |
+
| Model | MARC-ja/acc | JSTS/pearson | JSTS/spearman | JNLI/acc | JSQuAD/EM | JSQuAD/F1 | JComQA/acc |
|
96 |
+
|-------------------------------|-------------|--------------|---------------|----------|-----------|-----------|------------|
|
97 |
+
| Waseda RoBERTa base | 0.965 | 0.913 | 0.876 | 0.905 | 0.853 | 0.916 | 0.853 |
|
98 |
+
| Waseda RoBERTa large (seq512) | 0.969 | 0.925 | 0.890 | 0.928 | 0.910 | 0.955 | 0.900 |
|
99 |
+
| LUKE Japanese base* | 0.965 | 0.916 | 0.877 | 0.912 | - | - | 0.842 |
|
100 |
+
| LUKE Japanese large* | 0.965 | 0.932 | 0.902 | 0.927 | - | - | 0.893 |
|
101 |
+
| DeBERTaV2 base | 0.970 | 0.922 | 0.886 | 0.922 | 0.899 | 0.951 | 0.873 |
|
102 |
+
| DeBERTaV2 large | 0.968 | 0.925 | 0.892 | 0.924 | 0.912 | 0.959 | 0.890 |
|
103 |
+
|
104 |
+
*The scores of LUKE are from [the official repository](https://github.com/studio-ousia/luke).
|
105 |
+
|
106 |
+
## Acknowledgments
|
107 |
+
|
108 |
+
This work was supported by Joint Usage/Research Center for Interdisciplinary Large-scale Information Infrastructures (
|
109 |
+
JHPCN) through General Collaboration Project no. jh221004, "Developing a Platform for Constructing and Sharing of
|
110 |
+
Large-Scale Japanese Language Models".
|
111 |
+
For training models, we used the mdx: a platform for the data-driven future.
|
bert/deberta-v2-large-japanese/config.json
ADDED
@@ -0,0 +1,38 @@
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|
1 |
+
{
|
2 |
+
"_name_or_path": "configs/deberta_v2_large.json",
|
3 |
+
"architectures": [
|
4 |
+
"DebertaV2ForMaskedLM"
|
5 |
+
],
|
6 |
+
"attention_head_size": 64,
|
7 |
+
"attention_probs_dropout_prob": 0.1,
|
8 |
+
"conv_act": "gelu",
|
9 |
+
"conv_kernel_size": 3,
|
10 |
+
"hidden_act": "gelu",
|
11 |
+
"hidden_dropout_prob": 0.1,
|
12 |
+
"hidden_size": 1024,
|
13 |
+
"initializer_range": 0.02,
|
14 |
+
"intermediate_size": 4096,
|
15 |
+
"layer_norm_eps": 1e-07,
|
16 |
+
"max_position_embeddings": 512,
|
17 |
+
"max_relative_positions": -1,
|
18 |
+
"model_type": "deberta-v2",
|
19 |
+
"norm_rel_ebd": "layer_norm",
|
20 |
+
"num_attention_heads": 16,
|
21 |
+
"num_hidden_layers": 24,
|
22 |
+
"pad_token_id": 0,
|
23 |
+
"pooler_dropout": 0,
|
24 |
+
"pooler_hidden_act": "gelu",
|
25 |
+
"pooler_hidden_size": 1024,
|
26 |
+
"pos_att_type": [
|
27 |
+
"p2c",
|
28 |
+
"c2p"
|
29 |
+
],
|
30 |
+
"position_biased_input": false,
|
31 |
+
"position_buckets": 256,
|
32 |
+
"relative_attention": true,
|
33 |
+
"share_att_key": true,
|
34 |
+
"torch_dtype": "float32",
|
35 |
+
"transformers_version": "4.23.1",
|
36 |
+
"type_vocab_size": 0,
|
37 |
+
"vocab_size": 32000
|
38 |
+
}
|
bert/deberta-v2-large-japanese/special_tokens_map.json
ADDED
@@ -0,0 +1,9 @@
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|
1 |
+
{
|
2 |
+
"bos_token": "[CLS]",
|
3 |
+
"cls_token": "[CLS]",
|
4 |
+
"eos_token": "[SEP]",
|
5 |
+
"mask_token": "[MASK]",
|
6 |
+
"pad_token": "[PAD]",
|
7 |
+
"sep_token": "[SEP]",
|
8 |
+
"unk_token": "[UNK]"
|
9 |
+
}
|
bert/deberta-v2-large-japanese/tokenizer.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|
bert/deberta-v2-large-japanese/tokenizer_config.json
ADDED
@@ -0,0 +1,15 @@
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|
1 |
+
{
|
2 |
+
"bos_token": "[CLS]",
|
3 |
+
"cls_token": "[CLS]",
|
4 |
+
"do_lower_case": false,
|
5 |
+
"eos_token": "[SEP]",
|
6 |
+
"keep_accents": true,
|
7 |
+
"mask_token": "[MASK]",
|
8 |
+
"pad_token": "[PAD]",
|
9 |
+
"sep_token": "[SEP]",
|
10 |
+
"sp_model_kwargs": {},
|
11 |
+
"special_tokens_map_file": null,
|
12 |
+
"split_by_punct": false,
|
13 |
+
"tokenizer_class": "DebertaV2Tokenizer",
|
14 |
+
"unk_token": "[UNK]"
|
15 |
+
}
|
bert/deberta-v3-large/.gitattributes
ADDED
@@ -0,0 +1,27 @@
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|
1 |
+
*.7z filter=lfs diff=lfs merge=lfs -text
|
2 |
+
*.arrow filter=lfs diff=lfs merge=lfs -text
|
3 |
+
*.bin filter=lfs diff=lfs merge=lfs -text
|
4 |
+
*.bin.* filter=lfs diff=lfs merge=lfs -text
|
5 |
+
*.bz2 filter=lfs diff=lfs merge=lfs -text
|
6 |
+
*.ftz filter=lfs diff=lfs merge=lfs -text
|
7 |
+
*.gz filter=lfs diff=lfs merge=lfs -text
|
8 |
+
*.h5 filter=lfs diff=lfs merge=lfs -text
|
9 |
+
*.joblib filter=lfs diff=lfs merge=lfs -text
|
10 |
+
*.lfs.* filter=lfs diff=lfs merge=lfs -text
|
11 |
+
*.model filter=lfs diff=lfs merge=lfs -text
|
12 |
+
*.msgpack filter=lfs diff=lfs merge=lfs -text
|
13 |
+
*.onnx filter=lfs diff=lfs merge=lfs -text
|
14 |
+
*.ot filter=lfs diff=lfs merge=lfs -text
|
15 |
+
*.parquet filter=lfs diff=lfs merge=lfs -text
|
16 |
+
*.pb filter=lfs diff=lfs merge=lfs -text
|
17 |
+
*.pt filter=lfs diff=lfs merge=lfs -text
|
18 |
+
*.pth filter=lfs diff=lfs merge=lfs -text
|
19 |
+
*.rar filter=lfs diff=lfs merge=lfs -text
|
20 |
+
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
21 |
+
*.tar.* filter=lfs diff=lfs merge=lfs -text
|
22 |
+
*.tflite filter=lfs diff=lfs merge=lfs -text
|
23 |
+
*.tgz filter=lfs diff=lfs merge=lfs -text
|
24 |
+
*.xz filter=lfs diff=lfs merge=lfs -text
|
25 |
+
*.zip filter=lfs diff=lfs merge=lfs -text
|
26 |
+
*.zstandard filter=lfs diff=lfs merge=lfs -text
|
27 |
+
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
bert/deberta-v3-large/README.md
ADDED
@@ -0,0 +1,93 @@
|
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|
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|
|
|
|
|
|
1 |
+
---
|
2 |
+
language: en
|
3 |
+
tags:
|
4 |
+
- deberta
|
5 |
+
- deberta-v3
|
6 |
+
- fill-mask
|
7 |
+
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
|
8 |
+
license: mit
|
9 |
+
---
|
10 |
+
|
11 |
+
## DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
|
12 |
+
|
13 |
+
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority of NLU tasks with 80GB training data.
|
14 |
+
|
15 |
+
In [DeBERTa V3](https://arxiv.org/abs/2111.09543), we further improved the efficiency of DeBERTa using ELECTRA-Style pre-training with Gradient Disentangled Embedding Sharing. Compared to DeBERTa, our V3 version significantly improves the model performance on downstream tasks. You can find more technique details about the new model from our [paper](https://arxiv.org/abs/2111.09543).
|
16 |
+
|
17 |
+
Please check the [official repository](https://github.com/microsoft/DeBERTa) for more implementation details and updates.
|
18 |
+
|
19 |
+
The DeBERTa V3 large model comes with 24 layers and a hidden size of 1024. It has 304M backbone parameters with a vocabulary containing 128K tokens which introduces 131M parameters in the Embedding layer. This model was trained using the 160GB data as DeBERTa V2.
|
20 |
+
|
21 |
+
|
22 |
+
#### Fine-tuning on NLU tasks
|
23 |
+
|
24 |
+
We present the dev results on SQuAD 2.0 and MNLI tasks.
|
25 |
+
|
26 |
+
| Model |Vocabulary(K)|Backbone #Params(M)| SQuAD 2.0(F1/EM) | MNLI-m/mm(ACC)|
|
27 |
+
|-------------------|----------|-------------------|-----------|----------|
|
28 |
+
| RoBERTa-large |50 |304 | 89.4/86.5 | 90.2 |
|
29 |
+
| XLNet-large |32 |- | 90.6/87.9 | 90.8 |
|
30 |
+
| DeBERTa-large |50 |- | 90.7/88.0 | 91.3 |
|
31 |
+
| **DeBERTa-v3-large**|128|304 | **91.5/89.0**| **91.8/91.9**|
|
32 |
+
|
33 |
+
|
34 |
+
#### Fine-tuning with HF transformers
|
35 |
+
|
36 |
+
```bash
|
37 |
+
#!/bin/bash
|
38 |
+
|
39 |
+
cd transformers/examples/pytorch/text-classification/
|
40 |
+
|
41 |
+
pip install datasets
|
42 |
+
export TASK_NAME=mnli
|
43 |
+
|
44 |
+
output_dir="ds_results"
|
45 |
+
|
46 |
+
num_gpus=8
|
47 |
+
|
48 |
+
batch_size=8
|
49 |
+
|
50 |
+
python -m torch.distributed.launch --nproc_per_node=${num_gpus} \
|
51 |
+
run_glue.py \
|
52 |
+
--model_name_or_path microsoft/deberta-v3-large \
|
53 |
+
--task_name $TASK_NAME \
|
54 |
+
--do_train \
|
55 |
+
--do_eval \
|
56 |
+
--evaluation_strategy steps \
|
57 |
+
--max_seq_length 256 \
|
58 |
+
--warmup_steps 50 \
|
59 |
+
--per_device_train_batch_size ${batch_size} \
|
60 |
+
--learning_rate 6e-6 \
|
61 |
+
--num_train_epochs 2 \
|
62 |
+
--output_dir $output_dir \
|
63 |
+
--overwrite_output_dir \
|
64 |
+
--logging_steps 1000 \
|
65 |
+
--logging_dir $output_dir
|
66 |
+
|
67 |
+
```
|
68 |
+
|
69 |
+
### Citation
|
70 |
+
|
71 |
+
If you find DeBERTa useful for your work, please cite the following papers:
|
72 |
+
|
73 |
+
``` latex
|
74 |
+
@misc{he2021debertav3,
|
75 |
+
title={DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing},
|
76 |
+
author={Pengcheng He and Jianfeng Gao and Weizhu Chen},
|
77 |
+
year={2021},
|
78 |
+
eprint={2111.09543},
|
79 |
+
archivePrefix={arXiv},
|
80 |
+
primaryClass={cs.CL}
|
81 |
+
}
|
82 |
+
```
|
83 |
+
|
84 |
+
``` latex
|
85 |
+
@inproceedings{
|
86 |
+
he2021deberta,
|
87 |
+
title={DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION},
|
88 |
+
author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen},
|
89 |
+
booktitle={International Conference on Learning Representations},
|
90 |
+
year={2021},
|
91 |
+
url={https://openreview.net/forum?id=XPZIaotutsD}
|
92 |
+
}
|
93 |
+
```
|
bert/deberta-v3-large/config.json
ADDED
@@ -0,0 +1,22 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"model_type": "deberta-v2",
|
3 |
+
"attention_probs_dropout_prob": 0.1,
|
4 |
+
"hidden_act": "gelu",
|
5 |
+
"hidden_dropout_prob": 0.1,
|
6 |
+
"hidden_size": 1024,
|
7 |
+
"initializer_range": 0.02,
|
8 |
+
"intermediate_size": 4096,
|
9 |
+
"max_position_embeddings": 512,
|
10 |
+
"relative_attention": true,
|
11 |
+
"position_buckets": 256,
|
12 |
+
"norm_rel_ebd": "layer_norm",
|
13 |
+
"share_att_key": true,
|
14 |
+
"pos_att_type": "p2c|c2p",
|
15 |
+
"layer_norm_eps": 1e-7,
|
16 |
+
"max_relative_positions": -1,
|
17 |
+
"position_biased_input": false,
|
18 |
+
"num_attention_heads": 16,
|
19 |
+
"num_hidden_layers": 24,
|
20 |
+
"type_vocab_size": 0,
|
21 |
+
"vocab_size": 128100
|
22 |
+
}
|
bert/deberta-v3-large/generator_config.json
ADDED
@@ -0,0 +1,22 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"model_type": "deberta-v2",
|
3 |
+
"attention_probs_dropout_prob": 0.1,
|
4 |
+
"hidden_act": "gelu",
|
5 |
+
"hidden_dropout_prob": 0.1,
|
6 |
+
"hidden_size": 1024,
|
7 |
+
"initializer_range": 0.02,
|
8 |
+
"intermediate_size": 4096,
|
9 |
+
"max_position_embeddings": 512,
|
10 |
+
"relative_attention": true,
|
11 |
+
"position_buckets": 256,
|
12 |
+
"norm_rel_ebd": "layer_norm",
|
13 |
+
"share_att_key": true,
|
14 |
+
"pos_att_type": "p2c|c2p",
|
15 |
+
"layer_norm_eps": 1e-7,
|
16 |
+
"max_relative_positions": -1,
|
17 |
+
"position_biased_input": false,
|
18 |
+
"num_attention_heads": 16,
|
19 |
+
"num_hidden_layers": 12,
|
20 |
+
"type_vocab_size": 0,
|
21 |
+
"vocab_size": 128100
|
22 |
+
}
|