diff --git a/transformer_tutorial/BERT_tutorial_and_attention_visualization/BERT_Training.py b/transformer_tutorial/BERT_tutorial_and_attention_visualization/BERT_Training.py new file mode 100644 index 0000000..6ce87b5 --- /dev/null +++ b/transformer_tutorial/BERT_tutorial_and_attention_visualization/BERT_Training.py @@ -0,0 +1,287 @@ +from torch.optim import Adam +from torch.utils.data import DataLoader + +from dataset.wiki_dataset import BERTDataset +from models.bert_model import * +import tqdm +import pandas as pd +import numpy as np + + +config = {} +config["train_corpus_path"] = "./pretraining_data/wiki_dataset/test_wiki.txt" +config["test_corpus_path"] = "./pretraining_data/wiki_dataset/test_wiki.txt" +config["word2idx_path"] = "./pretraining_data/wiki_dataset/bert_word2idx_extend.json" +config["output_path"] = "./output_wiki_bert" + +config["batch_size"] = 1 +config["max_seq_len"] = 200 +config["vocab_size"] = 32162 +config["lr"] = 2e-6 +config["num_workers"] = 0 + +class Pretrainer: + def __init__(self, bert_model, + vocab_size, + max_seq_len, + batch_size, + lr, + with_cuda=True, + ): + # 词量, 注意在这里实际字(词)汇量 = vocab_size - 20, + # 因为前20个token用来做一些特殊功能, 如padding等等 + self.vocab_size = vocab_size + self.batch_size = batch_size + # 学习率 + self.lr = lr + # 是否使用GPU + cuda_condition = torch.cuda.is_available() and with_cuda + self.device = torch.device("cuda:0" if cuda_condition else "cpu") + # 限定的单句最大长度 + self.max_seq_len = max_seq_len + # 初始化超参数的配置 + bertconfig = BertConfig(vocab_size_or_config_json_file=config["vocab_size"]) + # 初始化bert模型 + self.bert_model = bert_model(config=bertconfig) + self.bert_model.to(self.device) + # 初始化训练数据集 + train_dataset = BERTDataset(corpus_path=config["train_corpus_path"], + word2idx_path=config["word2idx_path"], + seq_len=self.max_seq_len, + hidden_dim=bertconfig.hidden_size, + on_memory=False, + ) + # 初始化训练dataloader + self.train_dataloader = DataLoader(train_dataset, + batch_size=self.batch_size, + num_workers=config["num_workers"], + collate_fn=lambda x: x) + # 初始化测试数据集 + test_dataset = BERTDataset(corpus_path=config["test_corpus_path"], + word2idx_path=config["word2idx_path"], + seq_len=self.max_seq_len, + hidden_dim=bertconfig.hidden_size, + on_memory=True, + ) + # 初始化测试dataloader + self.test_dataloader = DataLoader(test_dataset, batch_size=self.batch_size, + num_workers=config["num_workers"], + collate_fn=lambda x: x) + # 初始化positional encoding + self.positional_enc = self.init_positional_encoding(hidden_dim=bertconfig.hidden_size, + max_seq_len=self.max_seq_len) + # 拓展positional encoding的维度为[1, max_seq_len, hidden_size] + self.positional_enc = torch.unsqueeze(self.positional_enc, dim=0) + + # 列举需要优化的参数并传入优化器 + optim_parameters = list(self.bert_model.parameters()) + self.optimizer = torch.optim.Adam(optim_parameters, lr=self.lr) + + print("Total Parameters:", sum([p.nelement() for p in self.bert_model.parameters()])) + + def init_positional_encoding(self, hidden_dim, max_seq_len): + position_enc = np.array([ + [pos / np.power(10000, 2 * i / hidden_dim) for i in range(hidden_dim)] + if pos != 0 else np.zeros(hidden_dim) for pos in range(max_seq_len)]) + + position_enc[1:, 0::2] = np.sin(position_enc[1:, 0::2]) # dim 2i + position_enc[1:, 1::2] = np.cos(position_enc[1:, 1::2]) # dim 2i+1 + denominator = np.sqrt(np.sum(position_enc**2, axis=1, keepdims=True)) + position_enc = position_enc / (denominator + 1e-8) + position_enc = torch.from_numpy(position_enc).type(torch.FloatTensor) + return position_enc + + def test(self, epoch, df_path="./output_wiki_bert/df_log.pickle"): + self.bert_model.eval() + with torch.no_grad(): + return self.iteration(epoch, self.test_dataloader, train=False, df_path=df_path) + + def load_model(self, model, dir_path="./output"): + # 加载模型 + checkpoint_dir = self.find_most_recent_state_dict(dir_path) + checkpoint = torch.load(checkpoint_dir) + model.load_state_dict(checkpoint["model_state_dict"], strict=False) + torch.cuda.empty_cache() + model.to(self.device) + print("{} loaded for training!".format(checkpoint_dir)) + + def train(self, epoch, df_path="./output_wiki_bert/df_log.pickle"): + self.bert_model.train() + self.iteration(epoch, self.train_dataloader, train=True, df_path=df_path) + + def compute_loss(self, predictions, labels, num_class=2, ignore_index=None): + if ignore_index is None: + loss_func = CrossEntropyLoss() + else: + loss_func = CrossEntropyLoss(ignore_index=ignore_index) + return loss_func(predictions.view(-1, num_class), labels.view(-1)) + + def get_mlm_accuracy(self, predictions, labels): + predictions = torch.argmax(predictions, dim=-1, keepdim=False) + mask = (labels > 0).to(self.device) + mlm_accuracy = torch.sum((predictions == labels) * mask).float() + mlm_accuracy /= (torch.sum(mask).float() + 1e-8) + return mlm_accuracy.item() + + def padding(self, output_dic_lis): + bert_input = [i["bert_input"] for i in output_dic_lis] + bert_label = [i["bert_label"] for i in output_dic_lis] + segment_label = [i["segment_label"] for i in output_dic_lis] + bert_input = torch.nn.utils.rnn.pad_sequence(bert_input, batch_first=True) + bert_label = torch.nn.utils.rnn.pad_sequence(bert_label, batch_first=True) + segment_label = torch.nn.utils.rnn.pad_sequence(segment_label, batch_first=True) + is_next = torch.cat([i["is_next"] for i in output_dic_lis]) + return {"bert_input": bert_input, + "bert_label": bert_label, + "segment_label": segment_label, + "is_next": is_next} + + + + def iteration(self, epoch, data_loader, train=True, df_path="./output_wiki_bert/df_log.pickle"): + if not os.path.isfile(df_path) and epoch != 0: + raise RuntimeError("log DataFrame path not found and can't create a new one because we're not training from scratch!") + if not os.path.isfile(df_path) and epoch == 0: + df = pd.DataFrame(columns=["epoch", "train_next_sen_loss", "train_mlm_loss", + "train_next_sen_acc", "train_mlm_acc", + "test_next_sen_loss", "test_mlm_loss", + "test_next_sen_acc", "test_mlm_acc" + ]) + df.to_pickle(df_path) + print("log DataFrame created!") + + str_code = "train" if train else "test" + + # Setting the tqdm progress bar + data_iter = tqdm.tqdm(enumerate(data_loader), + desc="EP_%s:%d" % (str_code, epoch), + total=len(data_loader), + bar_format="{l_bar}{r_bar}") + + total_next_sen_loss = 0 + total_mlm_loss = 0 + total_next_sen_acc = 0 + total_mlm_acc = 0 + total_element = 0 + + for i, data in data_iter: + # print('IDX of data_iter:', i) + data = self.padding(data) + # 0. batch_data will be sent into the device(GPU or cpu) + data = {key: value.to(self.device) for key, value in data.items()} + positional_enc = self.positional_enc[:, :data["bert_input"].size()[-1], :].to(self.device) + + # 1. forward the next_sentence_prediction and masked_lm model + mlm_preds, next_sen_preds = self.bert_model.forward(input_ids=data["bert_input"], + positional_enc=positional_enc, + token_type_ids=data["segment_label"]) + + mlm_acc = self.get_mlm_accuracy(mlm_preds, data["bert_label"]) + next_sen_acc = next_sen_preds.argmax(dim=-1, keepdim=False).eq(data["is_next"]).sum().item() + mlm_loss = self.compute_loss(mlm_preds, data["bert_label"], self.vocab_size, ignore_index=0) + next_sen_loss = self.compute_loss(next_sen_preds, data["is_next"]) + loss = mlm_loss + next_sen_loss + + + # 3. backward and optimization only in train + if train: + self.optimizer.zero_grad() + loss.backward() + # for param in self.model.parameters(): + # print(param.grad.data.sum()) + self.optimizer.step() + + + total_next_sen_loss += next_sen_loss.item() + total_mlm_loss += mlm_loss.item() + total_next_sen_acc += next_sen_acc + total_element += data["is_next"].nelement() + total_mlm_acc += mlm_acc + + if train: + log_dic = { + "epoch": epoch, + "train_next_sen_loss": total_next_sen_loss / (i + 1), + "train_mlm_loss": total_mlm_loss / (i + 1), + "train_next_sen_acc": total_next_sen_acc / total_element, + "train_mlm_acc": total_mlm_acc / (i + 1), + "test_next_sen_loss": 0, "test_mlm_loss": 0, + "test_next_sen_acc": 0, "test_mlm_acc": 0 + } + + else: + log_dic = { + "epoch": epoch, + "test_next_sen_loss": total_next_sen_loss / (i + 1), + "test_mlm_loss": total_mlm_loss / (i + 1), + "test_next_sen_acc": total_next_sen_acc / total_element, + "test_mlm_acc": total_mlm_acc / (i + 1), + "train_next_sen_loss": 0, "train_mlm_loss": 0, + "train_next_sen_acc": 0, "train_mlm_acc": 0 + } + + + if i % 10 == 0: + data_iter.write(str({k: v for k, v in log_dic.items() if v != 0 and k != "epoch"})) + + if train: + df = pd.read_pickle(df_path) + df = df.append([log_dic]) + df.reset_index(inplace=True, drop=True) + df.to_pickle(df_path) + else: + log_dic = {k: v for k, v in log_dic.items() if v != 0 and k != "epoch"} + df = pd.read_pickle(df_path) + df.reset_index(inplace=True, drop=True) + for k, v in log_dic.items(): + df.at[epoch, k] = v + df.to_pickle(df_path) + return float(log_dic["test_next_sen_loss"])+float(log_dic["test_mlm_loss"]) + + def find_most_recent_state_dict(self, dir_path): + dic_lis = [i for i in os.listdir(dir_path)] + if len(dic_lis) == 0: + raise FileNotFoundError("can not find any state dict in {}!".format(dir_path)) + dic_lis = [i for i in dic_lis if "model" in i] + dic_lis = sorted(dic_lis, key=lambda k: int(k.split(".")[-1])) + return dir_path + "/" + dic_lis[-1] + + def save_state_dict(self, model, epoch, dir_path="./output", file_path="bert.model"): + if not os.path.exists(dir_path): + os.mkdir(dir_path) + save_path = dir_path+ "/" + file_path + ".epoch.{}".format(str(epoch)) + model.to("cpu") + torch.save({"model_state_dict": model.state_dict()}, save_path) + print("{} saved!".format(save_path)) + model.to(self.device) + + +if __name__ == '__main__': + def init_trainer(dynamic_lr, load_model=False): + trainer = Pretrainer(BertForPreTraining, + vocab_size=config["vocab_size"], + max_seq_len=config["max_seq_len"], + batch_size=config["batch_size"], + lr=dynamic_lr, + with_cuda=True) + if load_model: + trainer.load_model(trainer.bert_model, dir_path=config["output_path"]) + return trainer + + + start_epoch = 3 + train_epoches = 1 + trainer = init_trainer(config["lr"], load_model=True) + # if train from scratch + all_loss = [] + threshold = 0 + patient = 10 + best_f1 = 0 + dynamic_lr = config["lr"] + for epoch in range(start_epoch, start_epoch + train_epoches): + print("train with learning rate {}".format(str(dynamic_lr))) + trainer.train(epoch) + + trainer.save_state_dict(trainer.bert_model, epoch, dir_path=config["output_path"], + file_path="bert.model") + trainer.test(epoch) \ No newline at end of file diff --git a/transformer_tutorial/BERT_tutorial_and_attention_visualization/SimHei.ttf b/transformer_tutorial/BERT_tutorial_and_attention_visualization/SimHei.ttf new file mode 100644 index 0000000..60a4819 Binary files /dev/null and b/transformer_tutorial/BERT_tutorial_and_attention_visualization/SimHei.ttf differ diff --git a/transformer_tutorial/BERT_tutorial_and_attention_visualization/attention_visualization.py b/transformer_tutorial/BERT_tutorial_and_attention_visualization/attention_visualization.py new file mode 100644 index 0000000..32d9547 --- /dev/null +++ b/transformer_tutorial/BERT_tutorial_and_attention_visualization/attention_visualization.py @@ -0,0 +1,133 @@ +import torch +from models.bert_model import BertModel, BertConfig +from dataset.inference_dataloader import preprocessing +import warnings +import json +import math +import os +import configparser +import matplotlib.pyplot as plt +from matplotlib.font_manager import FontProperties +font = FontProperties(fname='./SimHei.ttf') + + +class Pretrainer: + def __init__(self, + max_seq_len, + batch_size, + with_cuda=False, + ): + # 加载配置文件 + config_ = configparser.ConfigParser() + config_.read("./model_config.ini") + self.config = config_["DEFAULT"] + # 词量, 注意在这里实际字(词)汇量 = vocab_size - 20, + # 因为前20个token用来做一些特殊功能, 如padding等等 + self.vocab_size = int(self.config["vocab_size"]) + self.batch_size = batch_size + # 是否使用GPU + cuda_condition = torch.cuda.is_available() and with_cuda + self.device = torch.device("cuda:0" if cuda_condition else "cpu") + # 限定的单句最大长度 + self.max_seq_len = max_seq_len + # 初始化超参数的配置 + bertconfig = BertConfig(vocab_size_or_config_json_file=self.vocab_size) + # 初始化bert模型 + self.bert_model = BertModel(config=bertconfig) + self.bert_model.to(self.device) + # 加载字典 + self.word2idx = self.load_dic(self.config["word2idx_path"]) + # 初始化预处理器 + self.process_batch = preprocessing(hidden_dim=bertconfig.hidden_size, + max_positions=max_seq_len, + word2idx=self.word2idx) + # 加载BERT预训练模型 + self.load_model(self.bert_model, dir_path=self.config["state_dict_dir"]) + # disable dropout layers + self.bert_model.eval() + + def load_dic(self, path): + with open(path, "r", encoding="utf-8") as f: + return json.load(f) + + def load_model(self, model, dir_path="./output"): + # 加载模型 + checkpoint_dir = self.find_most_recent_state_dict(dir_path) + checkpoint = torch.load(checkpoint_dir) + # 不加载masked language model 和 next sentence 的参数 + checkpoint["model_state_dict"] = {k[5:]: v for k, v in checkpoint["model_state_dict"].items() + if k[:4] == "bert"} + model.load_state_dict(checkpoint["model_state_dict"], strict=True) + torch.cuda.empty_cache() + model.to(self.device) + print("{} loaded for evaluation!".format(checkpoint_dir)) + + + def __call__(self, text_list, batch_size=1): + """ + :param text_list: + :param batch_size: 为了注意力矩阵的可视化, batch_size只能为1, 即单句 + :return: + """ + # 异常判断 + if isinstance(text_list, str): + text_list = [text_list, ] + len_ = len(text_list) + text_list = [i for i in text_list if len(i) != 0] + if len(text_list) == 0: + raise NotImplementedError("输入的文本全部为空, 长度为0!") + if len(text_list) < len_: + warnings.warn("输入的文本中有长度为0的句子, 它们将被忽略掉!") + + # max_seq_len=self.max_seq_len+2 因为要留出cls和sep的位置 + max_seq_len = max([len(i) for i in text_list]) + # 预处理, 获取batch + texts_tokens, positional_enc = \ + self.process_batch(text_list, max_seq_len=max_seq_len) + # 准备positional encoding + positional_enc = torch.unsqueeze(positional_enc, dim=0).to(self.device) + + # 正向 + n_batches = math.ceil(len(texts_tokens) / batch_size) + + # 数据按mini batch切片过正向, 这里为了可视化所以吧batch size设为1 + for i in range(n_batches): + start = i * batch_size + end = start + batch_size + # 切片 + texts_tokens_ = texts_tokens[start: end].to(self.device) + attention_matrices = self.bert_model.forward(input_ids=texts_tokens_, + positional_enc=positional_enc, + get_attention_matrices=True) + # 因为batch size=1所以直接返回每层的注意力矩阵 + return [i.detach().numpy() for i in attention_matrices] + + + + def find_most_recent_state_dict(self, dir_path): + # 找到模型存储的最新的state_dict路径 + dic_lis = [i for i in os.listdir(dir_path)] + if len(dic_lis) == 0: + raise FileNotFoundError("can not find any state dict in {}!".format(dir_path)) + dic_lis = [i for i in dic_lis if "model" in i] + dic_lis = sorted(dic_lis, key=lambda k: int(k.split(".")[-1])) + return dir_path + "/" + dic_lis[-1] + + def plot_attention(self, text, attention_matrices, layer_num, head_num): + labels = [i + " " for i in list(text)] + labels = ["#CLS# ", ] + labels + ["#SEP# ", ] + plt.figure(figsize=(8, 8)) + plt.imshow(attention_matrices[layer_num][0][head_num]) + plt.yticks(range(len(labels)), labels, fontproperties=font, fontsize=18) + plt.xticks(range(len(labels)), labels, fontproperties=font, fontsize=18) + plt.show() + + +if __name__ == '__main__': + model = Pretrainer(max_seq_len=256, + batch_size=1, + ) + text = "为什么要上班" + # text = "历史上的今天发生了什么事？" + attention_matrices = model(text) + model.plot_attention(text, attention_matrices, layer_num=2, head_num=1) \ No newline at end of file diff --git a/transformer_tutorial/BERT_tutorial_and_attention_visualization/corpus/bert_word2idx_extend.json b/transformer_tutorial/BERT_tutorial_and_attention_visualization/corpus/bert_word2idx_extend.json new file mode 100644 index 0000000..1cc6005 --- /dev/null +++ b/transformer_tutorial/BERT_tutorial_and_attention_visualization/corpus/bert_word2idx_extend.json @@ -0,0 +1 @@ +{"积": 20, "极": 21, "开": 22, "展": 23, "国": 24, "际": 25, "交": 26, "流": 27, "与": 28, "合": 29, "作": 30, "等": 31, "八": 32, "项": 33, "政": 34, "策": 35, "措": 36, "施": 37, "。": 38, "提": 39, "要": 40, "：": 41, "本": 42, "章": 43, "介": 44, "绍": 45, "了": 46, "临": 47, "床": 48, "药": 49, "理": 50, "学": 51, "的": 52, "概": 53, "念": 54, "、": 55, "发": 56, "况": 57, "循": 58, "证": 59, "医": 60, "和": 61, "转": 62, "化": 63, "现": 64, "代": 65, "模": 66, "式": 67, "对": 68, "影": 69, "响": 70, "主": 71, "研": 72, "究": 73, "内": 74, "容": 75, "及": 76, "职": 77, "能": 78, "，": 79, "并": 80, "围": 81, "绕": 82, "着": 83, "新": 84, "评": 85, "价": 86, "简": 87, "述": 88, "试": 89, "验": 90, "设": 91, "计": 92, "方": 93, "法": 94, "此": 95, "外": 96, "伦": 97, "求": 98, "(": 99, "c": 100, "l": 101, "i": 102, "n": 103, "a": 104, "p": 105, "h": 106, "r": 107, "m": 108, "o": 109, "g": 110, "y": 111, "clinical": 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211, "世": 212, "纪": 213, "3": 214, "年": 215, "最": 216, "近": 217, "几": 218, "得": 219, "迅": 220, "速": 221, "逐": 222, "渐": 223, "形": 224, "成": 225, "独": 226, "立": 227, "来": 228, "大": 229, "从": 230, "青": 231, "霉": 232, "素": 233, "到": 234, "磺": 235, "胺": 236, "类": 237, "功": 238, "感": 239, "染": 240, "患": 241, "者": 242, "们": 243, "启": 244, "蒙": 245, "认": 246, "识": 247, "1": 248, "9": 249, "4": 250, "7": 251, "美": 252, "首": 253, "次": 254, "授": 255, "予": 256, "表": 257, "d": 258, "harry": 259, "cold": 260, "教": 261, "院": 262, "士": 263, "5": 264, "j": 265, "k": 266, "s": 267, "john": 268, "hopkins": 269, "建": 270, "第": 271, "个": 272, "室": 273, "讲": 274, "课": 275, "随": 276, "瑞": 277, "典": 278, "日": 279, "许": 280, "多": 281, "欧": 282, "家": 283, "纷": 284, "构": 285, "6": 286, "震": 287, "惊": 288, "界": 289, "沙": 290, "利": 291, "度": 292, "事": 293, "件": 294, "视": 295, "加": 296, "强": 297, "专": 298, "业": 299, "员": 300, "培": 301, "训": 302, "工": 303, "七": 304, "届": 305, "卫": 306, "生": 307, "会": 308, "w": 309, 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3337, "sr": 3338, "alt": 3339, "ig": 3340, "titus": 3341, "sive": 3342, "弧": 3343, "paco": 3344, "ulcer": 3345, "gastric": 3346, "schwartz": 3347, "proton": 3348, "pump": 3349, "pylorus": 3350, "hp": 3351, "gas": 3352, "enz": 3353, "lumi": 3354, "氰": 3355, "niza": 3356, "扁": 3357, "mep": 3358, "raz": 3359, "lan": 3360, "sopra": 3361, "pan": 3362, "rabe": 3363, "苔": 3364, "prostaglandin": 3365, "inflammatory": 3366, "said": 3367, "oli": 3368, "astro": 3369, "reflux": 3370, "gerd": 3371, "烂": 3372, "nerd": 3373, "bowel": 3374, "bd": 3375, "crohn": 3376, "asp": 3377, "磨": 3378, "裹": 3379, "秦": 3380, "sala": 3381, "mesa": 3382, "drag": 3383, "col": 3384, "penta": 3385, "ak": 3386, "bude": 3387, "aza": 3388, "opr": 3389, "mt": 3390, "clos": 3391, "por": 3392, "竟": 3393, "oddi": 3394, "演": 3395, "熊": 3396, "che": 3397, "node": 3398, "holi": 3399, "lit": 3400, "dc": 3401, "cdc": 3402, "涤": 3403, "腑": 3404, "凉": 3405, "蔬": 3406, "茵": 3407, "栀": 3408, "芪": 3409, "丹": 3410, "苓": 3411, "peni": 3412, "cilla": 3413, "vp": 3414, "soma": 3415, "indo": 3416, "nt": 3417, "isdn": 3418, "mn": 3419, "opt": 3420, "nari": 3421, "ndr": 3422, "xy": 3423, "ket": 3424, "clop": 3425, "ramus": 3426, "勃": 3427, "prost": 3428, "栗": 3429, "盆": 3430, "骑": 3431, "benign": 3432, "hyperplasia": 3433, "bph": 3434, "testosterone": 3435, "nadph": 3436, "erectile": 3437, "dysfunction": 3438, "imp": 3439, "膨": 3440, "nitric": 3441, "oxide": 3442, "intestinal": 3443, "ester": 3444, "choline": 3445, "anc": 3446, "搭": 3447, "玻": 3448, "璃": 3449, "fil": 3450, "tada": 3451, "亨": 3452, "刀": 3453, "抹": 3454, "筋": 3455, "mino": 3456, "肟": 3457, "睛": 3458, "minimal": 3459, "inhibitory": 3460, "eric": 3461, "dal": 3462, "mbc": 3463, "antibiotic": 3464, "pae": 3465, "枝": 3466, "徒": 3467, "峡": 3468, "penicillin": 3469, "thine": 3470, "ba": 3471, "campi": 3472, "贺": 3473, "piper": 3474, "locus": 3475, "ll": 3476, "mez": 3477, "梭": 3478, "瑟": 3479, "梅": 3480, "瓶": 3481, "halo": 3482, "spor": 3483, "alex": 3484, "oxime": 3485, "fot": 3486, "iam": 3487, "axe": 3488, "cef": 3489, "bl": 3490, "mpc": 3491, "taxi": 3492, "ft": 3493, "dime": 3494, "fix": 3495, "ime": 3496, "pod": 3497, "柠": 3498, "檬": 3499, "rome": 3500, "epi": 3501, "mrsa": 3502, "taro": 3503, "pr": 3504, "pb": 3505, "nem": 3506, "pani": 3507, "tape": 3508, "fox": 3509, "ote": 3510, "tan": 3511, "军": 3512, "lat": 3513, "amo": 3514, "xef": 3515, "flo": 3516, "ef": 3517, "lactam": 3518, "bac": 3519, "clav": 3520, "ulan": 3521, "ta": 3522, "zob": 3523, "far": 3524, "kana": 3525, "neo": 3526, "omo": 3527, "rib": 3528, "pectin": 3529, "bra": 3530, "疽": 3531, "耶": 3532, "釉": 3533, "砜": 3534, "macro": 3535, "rox": 3536, "romy": 3537, "jos": 3538, "clar": 3539, "dec": 3540, "spira": 3541, "炭": 3542, "猩": 3543, "蜂": 3544, "窝": 3545, "鹦": 3546, "鹉": 3547, "疖": 3548, "痈": 3549, "癣": 3550, "linda": 3551, "荚": 3552, "艰": 3553, "nor": 3554, "tei": 3555, "plan": 3556, "list": 3557, "efl": 3558, "lev": 3559, "gat": 3560, "旅": 3561, "fame": 3562, "prime": 3563, "奴": 3564, "tin": 3565, "daz": 3566, "贾": 3567, "鞭": 3568, "dsp": 3569, "橘": 3570, "aid": 3571, "sm": 3572, "tb": 3573, "amp": 3574, "hot": 3575, "lipid": 3576, "complex": 3577, "dispersion": 3578, "bcd": 3579, "ambl": 3580, "lamb": 3581, "洞": 3582, "趾": 3583, "壳": 3584, "匿": 3585, "腮": 3586, "sars": 3587, "猴": 3588, "痘": 3589, "tk": 3590, "城": 3591, "hcv": 3592, "hsv": 3593, "ara": 3594, "eb": 3595, "acv": 3596, "贲": 3597, "瘟": 3598, "rsv": 3599, "雅": 3600, "peg": 3601, "fc": 3602, "ag": 3603, "掺": 3604, "hb": 3605, "nm": 3606, "wh": 3607, "ccc": 3608, "log": 3609, "cleo": 3610, "nucleotide": 3611, "reverse": 3612, "transcriptase": 3613, "nrt": 3614, "protease": 3615, "te": 3616, "gras": 3617, "entry": 3618, "fusion": 3619, "dda": 3620, "nn": 3621, "rti": 3622, "gag": 3623, "pol": 3624, "茚": 3625, "rito": 3626, "navi": 3627, "芦": 3628, "alte": 3629, "grav": 3630, "纺": 3631, "锤": 3632, "cell": 3633, "circle": 3634, "specific": 3635, "nsa": 3636, "cf": 3637, "rescue": 3638, "腭": 3639, "雍": 3640, "rt": 3641, "xo": 3642, "red": 3643, "鳞": 3644, "霜": 3645, "敷": 3646, "tp": 3647, "cpa": 3648, "vcr": 3649, "ddp": 3650, "琼": 3651, "danse": 3652, "tron": 3653, "mu": 3654, "蕈": 3655, "珙": 3656, "桐": 3657, "adm": 3658, "hrt": 3659, "blast": 3660, "crist": 3661, "vb": 3662, "睑": 3663, "tax": 3664, "gist": 3665, "lot": 3666, "cot": 3667, "睫": 3668, "raf": 3669, "ve": 3670, "mek": 3671, "rk": 3672, "屑": 3673, "igg": 3674, "adc": 3675, "rbb": 3676, "lbc": 3677, "chop": 3678, "衄": 3679, "砷": 3680, "砒": 3681, "uri": 3682, "thu": 3683, "steroid": 3684, "immunity": 3685, "md": 3686, "割": 3687, "arthritis": 3688, "systemic": 3689, "lupus": 3690, "erythematosus": 3691, "cox": 3692, "biological": 3693, "窥": 3694, "锐": 3695, "瘢": 3696, "痕": 3697, "川": 3698, "崎": 3699, "拔": 3700, "sid": 3701, "csa": 3702, "蓖": 3703, "pvc": 3704, "fm": 3705, "κ": 3706, "rp": 3707, "bly": 3708, "nh": 3709, "nhl": 3710, "cvp": 3711, "fli": 3712, "ance": 3713, "rce": 3714, "adal": 3715, "imu": 3716, "cert": 3717, "zuma": 3718, "gol": 3719, "戈": 3720, "goli": 3721, "mum": 3722, "ab": 3723, "jia": 3724, "芍": 3725, "藤": 3726, "con": 3727, "igm": 3728, "tim": 3729, "sz": 3730, "膝": 3731, "hbc": 3732, "槐": 3733, "菇": 3734, "歧": 3735, "扶": 3736, "蔽": 3737, "allergy": 3738, "wa": 3739, "ige": 3740, "猛": 3741, "宠": 3742, "乡": 3743, "gel": 3744, "coombs": 3745, "cytotoxic": 3746, "nk": 3747, "nia": 3748, "削": 3749, "thus": 3750, "mediated": 3751, "hypersensitivity": 3752, "hen": 3753, "nira": 3754, "male": 3755, "nadine": 3756, "ceti": 3757, "赞": 3758, "stine": 3759, "voc": 3760, "eda": 3761, "atom": 3762, "allegra": 3763, "mdl": 3764, "nora": 3765, "ste": 3766, "船": 3767, "hoe": 3768, "chest": 3769, "marion": 3770, "roussel": 3771, "豚": 3772, "cam": 3773, "activator": 3774, "ap": 3775, "cort": 3776, "pla": 3777, "synthase": 3778, "iga": 3779, "夸": 3780, "water": 3781, "soluble": 3782, "粮": 3783, "flavin": 3784, "fad": 3785, "哆": 3786, "ure": 3787, "番": 3788, "辣": 3789, "椒": 3790, "枣": 3791, "fat": 3792, "ret": 3793, "胡": 3794, "萝": 3795, "卜": 3796, "rar": 3797, "烫": 3798, "痤": 3799, "ero": 3800, "搦": 3801, "玉": 3802, "皲": 3803, "硒": 3804, "nacl": 3805, "base": 3806, "aqua": 3807, "qp": 3808, "adh": 3809, "parathyroid": 3810, "press": 3811, "eth": 3812, "cry": 3813, "tora": 3814, "probe": 3815, "cid": 3816, "roth": 3817, "dap": 3818, "宗": 3819, "quine": 3820, "wi": 3821, "star": 3822, "milo": 3823, "porter": 3824, "taz": 3825, "dia": 3826, "dor": 3827, "brin": 3828, "攀": 3829, "kcl": 3830, "shock": 3831, "intra": 3832, "vascular": 3833, "coagulation": 3834, "潘": 3835, "kpa": 3836, "guan": 3837, "thane": 3838, "俱": 3839, "drop": 3840, "hon": 3841, "chloride": 3842, "vogt": 3843, "alium": 3844, "gik": 3845, "朊": 3846, "prion": 3847, "virus": 3848, "chlamydia": 3849, "bacteria": 3850, "fungus": 3851, "parasite": 3852, "helm": 3853, "昆": 3854, "medical": 3855, "insect": 3856, "infectious": 3857, "灾": 3858, "犬": 3859, "虐": 3860, "彼": 3861, "dy": 3862, "acte": 3863, "rio": 3864, "conditional": 3865, "pathogen": 3866, "cov": 3867, "碰": 3868, "hospital": 3869, "acquired": 3870, "com": 3871, "community": 3872, "spectrum": 3873, "immunization": 3874, "active": 3875, "passive": 3876, "covert": 3877, "sub": 3878, "恙": 3879, "latent": 3880, "city": 3881, "invasive": 3882, "ness": 3883, "蚴": 3884, "toxin": 3885, "endo": 3886, "ioc": 3887, "quantity": 3888, "bacillus": 3889, "cal": 3890, "mette": 3891, "gu": 3892, "é": 3893, "neum": 3894, "onic": 3895, "plague": 3896, "逃": 3897, "zy": 3898, "bron": 3899, "ect": 3900, "cytokine": 3901, "nuclear": 3902, "interleukin": 3903, "tumor": 3904, "necrosis": 3905, "interferon": 3906, "macrophage": 3907, "colony": 3908, "immunoglobulin": 3909, "ops": 3910, "gd": 3911, "position": 3912, "invasion": 3913, "痂": 3914, "location": 3915, "body": 3916, "route": 3917, "叮": 3918, "source": 3919, "transmission": 3920, "壤": 3921, "洁": 3922, "恋": 3923, "侣": 3924, "蚊": 3925, "虱": 3926, "蚤": 3927, "蛉": 3928, "蜱": 3929, "螨": 3930, "莱": 3931, "姆": 3932, "horizontal": 3933, "vertical": 3934, "父": 3935, "congenital": 3936, "natural": 3937, "旱": 3938, "social": 3939, "cj": 3940, "vc": 3941, "jd": 3942, "疯": 3943, "feature": 3944, "outbreak": 3945, "epidemic": 3946, "sequel": 3947, "腋": 3948, "fever": 3949, "rash": 3950, "anthem": 3951, "kop": 3952, "spot": 3953, "mac": 3954, "ule": 3955, "凸": 3956, "pap": 3957, "玫": 3958, "瑰": 3959, "rose": 3960, "acul": 3961, "apu": 3962, "opa": 3963, "pula": 3964, "瘀": 3965, "pete": 3966, "chia": 3967, "hy": 3968, "char": 3969, "mild": 3970, "typical": 3971, "severe": 3972, "ulm": 3973, "ant": 3974, "泔": 3975, "ordinary": 3976, "laboratory": 3977, "examination": 3978, "墨": 3979, "绦": 3980, "孵": 3981, "agglutination": 3982, "linked": 3983, "immuno": 3984, "bent": 3985, "elisa": 3986, "fluorescent": 3987, "antibody": 3988, "radio": 3989, "flow": 3990, "cyto": 3991, "try": 3992, "molecular": 3993, "blot": 3994, "northern": 3995, "polymerase": 3996, "chain": 3997, "situ": 3998, "gene": 3999, "chip": 4000, "detection": 4001, "western": 4002, "wb": 4003, "broncho": 4004, "copy": 4005, "astros": 4006, "colon": 4007, "magnetic": 4008, "resonance": 4009, "imaging": 4010, "tomography": 4011, "angiography": 4012, "biopsy": 4013, "bono": 4014, "tio": 4015, "logic": 4016, "菲": 4017, "蒿": 4018, "灸": 4019, "bust": 4020, "ion": 4021, "physical": 4022, "rehabilitation": 4023, "蒜": 4024, "腥": 4025, "prevention": 4026, "卸": 4027, "岗": 4028, "炊": 4029, "宰": 4030, "捕": 4031, "纱": 4032, "蝇": 4033, "juv": 4034, "viral": 4035, "hepatitis": 4036, "gb": 4037, "transfusion": 4038, "transmitted": 4039, "tv": 4040, "fein": 4041, "stone": 4042, "pico": 4043, "ⅵ": 4044, "猿": 4045, "狨": 4046, "狒": 4047, "河": 4048, "猕": 4049, "alexander": 4050, "vero": 4051, "berg": 4052, "antigen": 4053, "dane": 4054, "surface": 4055, "libert": 4056, "ortho": 4057, "ground": 4058, "squirrel": 4059, "鸭": 4060, "duck": 4061, "avi": 4062, "core": 4063, "蒸": 4064, "汽": 4065, "reading": 4066, "frame": 4067, "orf": 4068, "dw": 4069, "ay": 4070, "ayr": 4071, "江": 4072, "trans": 4073, "carcinoma": 4074, "idd": 4075, "closed": 4076, "circular": 4077, "conversion": 4078, "煮": 4079, "沸": 4080, "vr": 4081, "quasi": 4082, "simm": 4083, "港": 4084, "hd": 4085, "oz": 4086, "圆": 4087, "疆": 4088, "哥": 4089, "铯": 4090, "玩": 4091, "蚶": 4092, "剃": 4093, "刷": 4094, "俄": 4095, "tl": 4096, "asl": 4097, "degeneration": 4098, "eos": 4099, "phil": 4100, "focal": 4101, "pn": 4102, "bn": 4103, "ffe": 4104, "matrix": 4105, "塌": 4106, "hepatic": 4107, "encephalopathy": 4108, "renal": 4109, "叩": 4110, "溏": 4111, "蜘": 4112, "痣": 4113, "pta": 4114, "翼": 4115, "#": 4116, "static": 4117, "gamma": 4118, "tid": 4119, "alkaline": 4120, "phosphatase": 4121, "kp": 4122, "bile": 4123, "tba": 4124, "cho": 4125, "child": 4126, "pugh": 4127, "alb": 4128, "bil": 4129, "bros": 4130, "yalu": 4131, "roni": 4132, "collagen": 4133, "lam": 4134, "international": 4135, "normalized": 4136, "afp": 4137, "琐": 4138, "nested": 4139, "彩": 4140, "蛤": 4141, "蜊": 4142, "kno": 4143, "dell": 4144, "hai": 4145, "壶": 4146, "ih": 4147, "凯": 4148, "弗": 4149, "kay": 4150, "fleischer": 4151, "墩": 4152, "芝": 4153, "ul": 4154, "emt": 4155, "udine": 4156, "ten": 4157, "ovi": 4158, "uci": 4159, "tabi": 4160, "amd": 4161, "oxo": 4162, "rac": 4163, "alam": 4164, "def": 4165, "big": 4166, "miu": 4167, "腻": 4168, "cim": 4169, "tila": 4170, "邵": 4171, "short": 4172, "wide": 4173, "bishop": 4174, "洪": 4175, "涛": 4176, "saif": 4177, "亥": 4178, "沃": 4179, "cali": 4180, "civ": 4181, "dae": 4182, "夷": 4183, "hawaii": 4184, "郡": 4185, "montgomery": 4186, "county": 4187, "雪": 4188, "snow": 4189, "mountain": 4190, "ditch": 4191, "ling": 4192, "陶": 4193, "south": 4194, "arn": 4195, "荒": 4196, "desert": 4197, "storm": 4198, "toronto": 4199, "凹": 4200, "echo": 4201, "toro": 4202, "dim": 4203, "pest": 4204, "池": 4205, "deny": 4206, "sp": 4207, "汤": 4208, "page": 4209, "袋": 4210, "牝": 4211, "polio": 4212, "rida": 4213, "entero": 4214, "锰": 4215, "挑": 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4816, "rha": 4817, "gie": 4818, "pomona": 4819, "nicola": 4820, "grip": 4821, "pot": 4822, "osa": 4823, "australis": 4824, "mad": 4825, "爪": 4826, "哇": 4827, "java": 4828, "田": 4829, "渔": 4830, "spire": 4831, "塘": 4832, "拴": 4833, "嬉": 4834, "戏": 4835, "syphilis": 4836, "pone": 4837, "pallidum": 4838, "cha": 4839, "tale": 4840, "treponema": 4841, "tac": 4842, "eae": 4843, "genus": 4844, "蛇": 4845, "sy": 4846, "philo": 4847, "gum": 4848, "疳": 4849, "凿": 4850, "rl": 4851, "姻": 4852, "兄": 4853, "弟": 4854, "姐": 4855, "妹": 4856, "裨": 4857, "jari": 4858, "she": 4859, "螫": 4860, "滚": 4861, "lyme": 4862, "bol": 4863, "elia": 4864, "eri": 4865, "涅": 4866, "狄": 4867, "wright": 4868, "肘": 4869, "捻": 4870, "disp": 4871, "泽": 4872, "arcot": 4873, "moe": 4874, "廋": 4875, "钡": 4876, "屡": 4877, "sec": 4878, "liver": 4879, "abscess": 4880, "malaria": 4881, "mosquito": 4882, "plasmodium": 4883, "stage": 4884, "poro": 4885, "mero": 4886, "sch": 4887, "brady": 4888, "female": 4889, "game": 4890, "cyte": 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"钵": 11424, "jellyfish": 11425, "agricultural": 11426, "秧": 11427, "耘": 11428, "pomp": 11429, "holy": 11430, "rtica": 11431, "oedema": 11432, "bork": 11433, "solar": 11434, "aest": 11435, "lina": 11436, "stu": 11437, "bner": 11438, "tate": 11439, "rode": 11440, "folium": 11441, "riat": 11442, "leb": 11443, "ube": 11444, "kz": 11445, "elf": 11446, "sweet": 11447, "sli": 11448, "kilo": 11449, "banker": 11450, "sting": 11451, "bull": 11452, "pfa": 11453, "glob": 11454, "diane": 11455, "铒": 11456, "seb": 11457, "hid": 11458, "sweat": 11459, "caro": 11460, "bash": 11461, "gz": 11462, "钕": 11463, "钇": 11464, "ano": 11465, "filum": 11466, "edit": 11467, "jeb": 11468, "deb": 11469, "hailey": 11470, "tard": 11471, "dean": 11472, "thoma": 11473, "nev": 11474, "tren": 11475, "aunay": 11476, "strawberry": 11477, "merritt": 11478, "hutchinson": 11479, "mulberry": 11480, "busch": 11481, "zal": 11482, "琦": 11483, "sce": 11484, "schwa": 11485, "幵": 11486, "bruch": 11487, "cup": 11488, "闱": 11489, "krause": 11490, "zinn": 11491, "缤": 11492, "shield": 11493, "阄": 11494, "geography": 11495, "jae": 11496, "octopus": 11497, "splitting": 11498, "rand": 11499, "ttler": 11500, "farnsworth": 11501, "rayleigh": 11502, "kohl": 11503, "week": 11504, "worth": 11505, "arden": 11506, "taka": 11507, "cgt": 11508, "oscillator": 11509, "marcus": 11510, "gunn": 11511, "ruby": 11512, "zeiss": 11513, "shaffer": 11514, "queer": 11515, "kov": 11516, "contour": 11517, "纡": 11518, "cdi": 11519, "sai": 11520, "mink": 11521, "nfl": 11522, "horde": 11523, "zion": 11524, "霰": 11525, "mora": 11526, "wine": 11527, "lash": 11528, "pion": 11529, "lago": 11530, "epic": 11531, "haro": 11532, "dist": 11533, "imation": 11534, "mik": 11535, "rima": 11536, "riley": 11537, "pun": 11538, "osd": 11539, "蕨": 11540, "rant": 11541, "chom": 11542, "hay": 11543, "pingu": 11544, "赦": 11545, "cement": 11546, "nebula": 11547, "lpc": 11548, "hsk": 11549, "ori": 11550, "terri": 11551, "mee": 11552, "print": 11553, "fuchs": 11554, "mun": 11555, "tter": 11556, "haze": 11557, "ossi": 11558, "lsch": 11559, "kondo": 11560, "timo": 11561, "uno": 11562, "argon": 11563, "molten": 11564, "ahmed": 11565, "reese": 11566, "chandler": 11567, "veit": 11568, "syne": 11569, "acca": 11570, "yana": 11571, "霞": 11572, "masquerade": 11573, "eger": 11574, "dorf": 11575, "vitr": 11576, "tinos": 11577, "wagner": 11578, "irvine": 11579, "mcpherson": 11580, "jansen": 11581, "rao": 11582, "grid": 11583, "菁": 11584, "csc": 11585, "ave": 11586, "gma": 11587, "erd": 11588, "corium": 11589, "reh": 11590, "uht": 11591, "hoff": 11592, "buried": 11593, "empty": 11594, "amplitude": 11595, "irregular": 11596, "oblique": 11597, "ikon": 11598, "streak": 11599, "madd": 11600, "isley": 11601, "jackson": 11602, "fused": 11603, "fcc": 11604, "nra": 11605, "spectacle": 11606, "addition": 11607, "distance": 11608, "sik": 11609, "tome": 11610, "glare": 11611, "彗": 11612, "claw": 11613, "psr": 11614, "horia": 11615, "manifest": 11616, "cover": 11617, "biel": 11618, "chow": 11619, "fellow": 11620, "rect": 11621, "eso": 11622, "knapp": 11623, "nerva": 11624, "fade": 11625, "duane": 11626, "orden": 11627, "kes": 11628, "呱": 11629, "foster": 11630, "microp": 11631, "haber": 11632, "drp": 11633, "cher": 11634, "pepper": 11635, "rim": 11636, "vex": 11637, "amina": 11638, "pill": 11639, "csr": 11640, "safe": 11641, "improvement": 11642, "accessible": 11643, "galileo": 11644, "nerv": 11645, "insula": 11646, "dien": 11647, "hemi": 11648, "raymond": 11649, "cest": 11650, "locked": 11651, "benedikt": 11652, "lia": 11653, "lear": 11654, "abdu": 11655, "dying": 11656, "rosso": 11657, "limo": 11658, "gonda": 11659, "sop": 11660, "confusion": 11661, "deco": 11662, "pall": 11663, "muti": 11664, "vigil": 11665, "abu": 11666, "ional": 11667, "ideo": 11668, "melo": 11669, "construction": 11670, "headache": 11671, "knife": 11672, "gait": 11673, "撅": 11674, "magnum": 11675, "扒": 11676, "coordination": 11677, "chok": 11678, "heffer": 11679, "shy": 11680, "deus": 11681, "cken": 11682, "umbra": 11683, "opc": 11684, "quantum": 11685, "squid": 11686, "reversal": 11687, "click": 11688, "kym": 11689, "rne": 11690, "eca": 11691, "rune": 11692, "direction": 11693, "krab": 11694, "mori": 11695, "leigh": 11696, "edition": 11697, "fhm": 11698, "trig": 11699, "moh": 11700, "legia": 11701, "dog": 11702, "toast": 11703, "hachi": 11704, "lander": 11705, "tld": 11706, "khan": 11707, "executive": 11708, "suo": 11709, "wisconsin": 11710, "nca": 11711, "toni": 11712, "alien": 11713, "tbm": 11714, "rrr": 11715, "mitte": 11716, "amer": 11717, "rotate": 11718, "hod": 11719, "moda": 11720, "fam": 11721, "lter": 11722, "winger": 11723, "vlc": 11724, "lore": 11725, "uch": 11726, "pink": 11727, "masked": 11728, "propulsion": 11729, "leg": 11730, "zm": 11731, "suri": 11732, "sagi": 11733, "capone": 11734, "aim": 11735, "rivas": 11736, "galant": 11737, "samuel": 11738, "莪": 11739, "lott": 11740, "deficit": 11741, "interesting": 11742, "hunting": 11743, "gain": 11744, "撇": 11745, "marked": 11746, "sega": 11747, "iran": 11748, "phd": 11749, "kli": 11750, "ppel": 11751, "fei": 11752, "gilles": 11753, "dive": 11754, "crane": 11755, "hippo": 11756, "ese": 11757, "rasmussen": 11758, "barnett": 11759, "dmc": 11760, "eux": 11761, "segue": 11762, "europ": 11763, "symmetric": 11764, "sumner": 11765, "automatic": 11766, "lal": 11767, "fax": 11768, "miso": 11769, "tyler": 11770, "girdle": 11771, "edm": 11772, "emer": 11773, "yoshi": 11774, "isaac": 11775, "cong": 11776, "luft": 11777, "杞": 11778, "clarke": 11779, "pell": 11780, "spg": 11781, "larsson": 11782, "merlin": 11783, "awan": 11784, "bourne": 11785, "siro": 11786, "rasa": 11787, "little": 11788, "spr": 11789, "kleine": 11790, "daytime": 11791, "msl": 11792, "plm": 11793, "anna": 11794, "psn": 11795, "psychiatry": 11796, "psychosis": 11797, "continuum": 11798, "decade": 11799, "macrop": 11800, "饺": 11801, "♀": 11802, "巿": 11803, "delusion": 11804, "遐": 11805, "蹦": 11806, "唉": 11807, "volition": 11808, "佻": 11809, "schneider": 11810, "emil": 11811, "alois": 11812, "judd": 11813, "煞": 11814, "brief": 11815, "burke": 11816, "chat": 11817, "mey": 11818, "quil": 11819, "milwaukee": 11820, "aura": 11821, "prod": 11822, "tab": 11823, "harmful": 11824, "milner": 11825, "rotten": 11826, "enka": 11827, "lora": 11828, "flash": 11829, "rush": 11830, "iur": 11831, "disulfide": 11832, "france": 11833, "cker": 11834, "eure": 11835, "dao": 11836, "pursuit": 11837, "tracking": 11838, "lifetime": 11839, "nice": 11840, "excellence": 11841, "bipolar": 11842, "鹏": 11843, "manic": 11844, "昵": 11845, "雯": 11846, "panic": 11847, "costa": 11848, "phobia": 11849, "bow": 11850, "ocd": 11851, "yale": 11852, "beard": 11853, "kenyon": 11854, "slater": 11855, "揪": 11856, "globus": 11857, "maintenance": 11858, "omni": 11859, "wake": 11860, "terror": 11861, "nightmare": 11862, "orgasm": 11863, "adjustment": 11864, "flashback": 11865, "schmid": 11866, "rnberg": 11867, "氓": 11868, "眷": 11869, "铤": 11870, "maria": 11871, "kert": 11872, "佞": 11873, "quan": 11874, "swanson": 11875, "sho": 11876, "pcl": 11877, "violence": 11878, "tree": 11879, "introduction": 11880, "thought": 11881, "teaching": 11882, "拽": 11883, "shi": 11884, "attempt": 11885, "liaison": 11886, "scholastic": 11887, "skill": 11888, "clancy": 11889, "registry": 11890, "conduct": 11891, "嗤": 11892, "habit": 11893, "noo": 11894, "norad": 11895, "moc": 11896, "influence": 11897, "manipulation": 11898, "acquisition": 11899, "禅": 11900, "hoffman": 11901, "erik": 11902, "fritz": 11903, "opening": 11904, "listening": 11905, "孽": 11906, "轶": 11907, "trance": 11908, "咏": 11909, "forensic": 11910, "homicide": 11911, "criminal": 11912, "serving": 11913, "sentence": 11914, "legislation": 11915, "civil": 11916, "proxy": 11917, "advance": 11918, "directive": 11919, "philippe": 11920, "prison": 11921, "court": 11922, "焘": 11923, "baha": 11924, "坤": 11925, "used": 11926, "pharynx": 11927, "peacock": 11928, "dorsum": 11929, "basis": 11930, "colum": 11931, "aperture": 11932, "angular": 11933, "crib": 11934, "urbi": 11935, "lunar": 11936, "岔": 11937, "ssel": 11938, "auric": 11939, "petros": 11940, "vidi": 11941, "caldwell": 11942, "labyrinth": 11943, "agger": 11944, "cork": 11945, "bottle": 11946, "powered": 11947, "concha": 11948, "paradox": 11949, "lber": 11950, "blow": 11951, "bath": 11952, "plug": 11953, "poll": 11954, "spt": 11955, "analogue": 11956, "kart": 11957, "荔": 11958, "sse": 11959, "冼": 11960, "俟": 11961, "paper": 11962, "fes": 11963, "puffy": 11964, "beri": 11965, "nigra": 11966, "虞": 11967, "denke": 11968, "stewart": 11969, "nitz": 11970, "leiter": 11971, "orz": 11972, "messer": 11973, "铉": 11974, "焯": 11975, "wig": 11976, "ktp": 11977, "torus": 11978, "faux": 11979, "latina": 11980, "lingua": 11981, "oe": 11982, "valle": 11983, "triangular": 11984, "silla": 11985, "throat": 11986, "侨": 11987, "晟": 11988, "哔": 11989, "rer": 11990, "pillar": 11991, "vocal": 11992, "corn": 11993, "frag": 11994, "nne": 11995, "rache": 11996, "啜": 11997, "圭": 11998, "tucker": 11999, "rsl": 12000, "pitch": 12001, "carina": 12002, "lai": 12003, "charge": 12004, "inner": 12005, "petro": 12006, "rivi": 12007, "pyramid": 12008, "porus": 12009, "hoi": 12010, "mba": 12011, "trag": 12012, "aur": 12013, "sure": 12014, "attic": 12015, "umb": 12016, "oval": 12017, "sura": 12018, "pneumatic": 12019, "scala": 12020, "bast": 12021, "uel": 12022, "偕": 12023, "translator": 12024, "嘘": 12025, "polit": 12026, "tuning": 12027, "audio": 12028, "bk": 12029, "reception": 12030, "aes": 12031, "distortion": 12032, "mitt": 12033, "impedance": 12034, "boa": 12035, "ebert": 12036, "tull": 12037, "sot": 12038, "pike": 12039, "gaze": 12040, "enver": 12041, "town": 12042, "xu": 12043, "gush": 12044, "erich": 12045, "glue": 12046, "噼": 12047, "啪": 12048, "捺": 12049, "gate": 12050, "veil": 12051, "hier": 12052, "eur": 12053, "perl": 12054, "lem": 12055, "grow": 12056, "赝": 12057, "piston": 12058, "sloane": 12059, "prosper": 12060, "hara": 12061, "lacus": 12062, "dix": 12063, "oda": 12064, "nimo": 12065, "rtha": 12066, "deaf": 12067, "lner": 12068, "usher": 12069, "richards": 12070, "bor": 12071, "founder": 12072, "stefano": 12073, "partially": 12074, "implant": 12075, "processor": 12076, "speak": 12077, "durr": 12078, "loudness": 12079, "coin": 12080, "ginkgo": 12081, "bilo": 12082, "vernon": 12083, "zim": 12084, "rumi": 12085, "skeleton": 12086, "rebel": 12087, "咝": 12088, "frisch": 12089, "unna": 12090, "桔": 12091, "诃": 12092, "煅": 12093, "茱": 12094, "萸": 12095, "嵒": 12096, "á": 12097, "philip": 12098, "holland": 12099, "oldham": 12100, "daw": 12101, "bernardino": 12102, "janet": 12103, "lane": 12104, "herman": 12105, "wy": 12106, "geoffrey": 12107, "percent": 12108, "yama": 12109, "iwa": 12110, "emu": 12111, "fred": 12112, "atomic": 12113, "hst": 12114, "rsen": 12115, "nth": 12116, "recognized": 12117, "berman": 12118, "next": 12119, "analyse": 12120, "job": 12121, "rhodes": 12122, "ncc": 12123, "sko": 12124, "fuji": 12125, "intelligent": 12126, "mbi": 12127, "guided": 12128, "karn": 12129, "eastern": 12130, "cooperative": 12131, "zub": 12132, "cist": 12133, "bloc": 12134, "trilogy": 12135, "concurrent": 12136, "herc": 12137, "suv": 12138, "vio": 12139, "conform": 12140, "lars": 12141, "lek": 12142, "bragg": 12143, "bai": 12144, "cristi": 12145, "otec": 12146, "cape": 12147, "atc": 12148, "tez": 12149, "sora": 12150, "vor": 12151, "proven": 12152, "rosenberg": 12153, "schreiber": 12154, "dunn": 12155, "tito": 12156, "treme": 12157, "jx": 12158, "cove": 12159, "fig": 12160, "alem": 12161, "brent": 12162, "mae": 12163, "brit": 12164, "umo": 12165, "vande": 12166, "tani": 12167, "aurora": 12168, "sert": 12169, "huggins": 12170, "aroma": 12171, "traditional": 12172, "墟": 12173, "瘕": 12174, "瘿": 12175, "蛰": 12176, "鳖": 12177, "轩": 12178, "喦": 12179, "瀛": 12180, "覃": 12181, "犀": 12182, "绛": 12183, "otto": 12184, "heinrich": 12185, "ree": 12186, "aspen": 12187, "generated": 12188, "supplement": 12189, "cvc": 12190, "渶": 12191, "nell": 12192, "osho": 12193, "jimmie": 12194, "langley": 12195, "partition": 12196, "doh": 12197, "slope": 12198, "taco": 12199, "satu": 12200, "sibi": 12201, "spare": 12202, "extensive": 12203, "bidi": 12204, "euler": 12205, "sami": 12206, "vamp": 12207, "carpus": 12208, "amanita": 12209, "cur": 12210, "syst": 12211, "tabu": 12212, "flos": 12213, "moni": 12214, "hyo": 12215, "poli": 12216, "tang": 12217, "utica": 12218, "hom": 12219, "lenz": 12220, "phan": 12221, "mede": 12222, "gon": 12223, "rano": 12224, "methane": 12225, "evo": 12226, "maze": 12227, "anl": 12228, "lasting": 12229, "ince": 12230, "nite": 12231, "dines": 12232, "desi": 12233, "zod": 12234, "zack": 12235, "lge": 12236, "hughes": 12237, "dam": 12238, "moller": 12239, "eau": 12240, "merck": 12241, "euphoria": 12242, "ipc": 12243, "dolan": 12244, "stad": 12245, "tram": 12246, "palma": 12247, "tart": 12248, "salsa": 12249, "turan": 12250, "mont": 12251, "keen": 12252, "close": 12253, "cono": 12254, "kur": 12255, "kali": 12256, "miner": 12257, "alkyl": 12258, "gallop": 12259, "amil": 12260, "cnh": 12261, "nj": 12262, "kap": 12263, "herg": 12264, "exile": 12265, "pooh": 12266, "poo": 12267, "moderate": 12268, "nith": 12269, "laz": 12270, "naz": 12271, "lester": 12272, "demi": 12273, "monaco": 12274, "malo": 12275, "evening": 12276, "logi": 12277, "effort": 12278, "also": 12279, "known": 12280, "silt": 12281, "exam": 12282, "蒎": 12283, "ogre": 12284, "zag": 12285, "sant": 12286, "rada": 12287, "aquino": 12288, "poet": 12289, "ramos": 12290, "dino": 12291, "kad": 12292, "xfe": 12293, "arran": 12294, "tadi": 12295, "tib": 12296, "ives": 12297, "bud": 12298, "tablet": 12299, "葆": 12300, "tif": 12301, "erdo": 12302, "aluminum": 12303, "ralf": 12304, "marz": 12305, "薁": 12306, "azul": 12307, "tanna": 12308, "cody": 12309, "senna": 12310, "hron": 12311, "ethan": 12312, "estro": 12313, "prove": 12314, "ien": 12315, "mtu": 12316, "tsi": 12317, "uber": 12318, "division": 12319, "bj": 12320, "aure": 12321, "heta": 12322, "fec": 12323, "loth": 12324, "dole": 12325, "lorne": 12326, "tien": 12327, "tami": 12328, "pron": 12329, "nam": 12330, "una": 12331, "zina": 12332, "obit": 12333, "ando": 12334, "dir": 12335, "samy": 12336, "rok": 12337, "sterling": 12338, "winthrop": 12339, "gare": 12340, "closing": 12341, "parc": 12342, "aac": 12343, "︰": 12344, "ridin": 12345, "penetration": 12346, "goi": 12347, "cocktail": 12348, "lacy": 12349, "nil": 12350, "gris": 12351, "cane": 12352, "oricon": 12353, "closer": 12354, "psone": 12355, "茜": 12356, "ipo": 12357, "xor": 12358, "taxus": 12359, "zev": 12360, "tibi": 12361, "avast": 12362, "cade": 12363, "珂": 12364, "tent": 12365, "zac": 12366, "tima": 12367, "capri": 12368, "lica": 12369, "irn": 12370, "芙": 12371, "trigger": 12372, "vinson": 12373, "matin": 12374, "elena": 12375, "frequent": 12376, "逛": 12377, "complaint": 12378, "taking": 12379, "inspection": 12380, "percussion": 12381, "cult": 12382, "甩": 12383, "eun": 12384, "嶙": 12385, "峋": 12386, "voice": 12387, "愕": 12388, "炯": 12389, "spider": 12390, "tower": 12391, "croce": 12392, "expansion": 12393, "friction": 12394, "garland": 12395, "rocco": 12396, "rale": 12397, "zhi": 12398, "coda": 12399, "thrill": 12400, "fourth": 12401, "murmur": 12402, "crescendo": 12403, "dropped": 12404, "frog": 12405, "astral": 12406, "hook": 12407, "splash": 12408, "ruler": 12409, "潺": 12410, "滕": 12411, "garten": 12412, "proc": 12413, "gibb": 12414, "lord": 12415, "jenn": 12416, "routine": 12417, "stab": 12418, "alder": 12419, "reilly": 12420, "finch": 12421, "packed": 12422, "bass": 12423, "nomo": 12424, "sudan": 12425, "dnc": 12426, "gbc": 12427, "beth": 12428, "wiener": 12429, "maca": 12430, "umi": 12431, "laurel": 12432, "clot": 12433, "brien": 12434, "annex": 12435, "claus": 12436, "produce": 12437, "rotor": 12438, "corporation": 12439, "楠": 12440, "zoo": 12441, "piss": 12442, "meld": 12443, "jen": 12444, "astm": 12445, "roly": 12446, "wai": 12447, "odo": 12448, "resin": 12449, "ufc": 12450, "walden": 12451, "rosette": 12452, "erft": 12453, "aar": 12454, "gry": 12455, "mex": 12456, "kirby": 12457, "bauer": 12458, "gion": 12459, "phoenix": 12460, "abbott": 12461, "hava": 12462, "dva": 12463, "gba": 12464, "hutch": 12465, "instrument": 12466, "roda": 12467, "ssi": 12468, "jeffrey": 12469, "dipole": 12470, "佷": 12471, "emin": 12472, "biz": 12473, "mah": 12474, "norman": 12475, "mason": 12476, "sbn": 12477, "潦": 12478, "inference": 12479, "turkey": 12480, "vim": 12481, "tru": 12482, "opportunity": 12483, "when": 12484, "盎": 12485, "demo": 12486, "brass": 12487, "mathematical": 12488, "theoretical": 12489, "bureau": 12490, "migrant": 12491, "prentice": 12492, "≠": 12493, "篑": 12494, "dsps": 12495, "monica": 12496, "weekly": 12497, "sentinel": 12498, "yellow": 12499, "熄": 12500, "markov": 12501, "theorem": 12502, "丨": 12503, "□": 12504, "○": 12505, "publication": 12506, "disaster": 12507, "lds": 12508, "schedule": 12509, "mant": 12510, "ottawa": 12511, "charter": 12512, "innovation": 12513, "clemente": 12514, "arrange": 12515, "smart": 12516, "tsn": 12517, "mainstream": 12518, "stream": 12519, "advice": 12520, "protect": 12521, "offer": 12522, "warn": 12523, "raise": 12524, "iaea": 12525, "楂": 12526, "pagoda": 12527, "煨": 12528, "饯": 12529, "氽": 12530, "炙": 12531, "fitness": 12532, "sport": 12533, "mets": 12534, "annual": 12535, "mary": 12536, "expanded": 12537, "豺": 12538, "malle": 12539, "swine": 12540, "犊": 12541, "smoking": 12542, "毙": 12543, "rain": 12544, "itai": 12545, "榆": 12546, "沽": 12547, "沧": 12548, "酥": 12549, "employment": 12550, "aft": 12551, "veterinary": 12552, "codex": 12553, "acceptable": 12554, "daily": 12555, "adi": 12556, "illegal": 12557, "sau": 12558, "npa": 12559, "鲣": 12560, "鲭": 12561, "谣": 12562, "aspect": 12563, "creating": 12564, "corporate": 12565, "assurance": 12566, "campaign": 12567, "profit": 12568, "insurance": 12569, "fee": 12570, "budget": 12571, "reason": 12572, "裙": 12573, "梏": 12574, "〔": 12575, "〕": 12576, "keeper": 12577, "security": 12578, "millennium": 12579, "\n": 12580, "「": 12581, "」": 12582, "寨": 12583, "喽": 12584, "彪": 12585, "（": 12586, "）": 12587, "侠": 12588, "祇": 12589, "－": 12590, "cupid": 12591, "vikram": 12592, "vampire": 12593, "bel": 12594, "dragon": 12595, "娥": 12596, "ruth": 12597, "leblanc": 12598, "∀": 12599, "{": 12600, "}": 12601, "xf": 12602, "∃": 12603, "坝": 12604, "驹": 12605, "梓": 12606, "chao": 12607, "archive": 12608, "agri": 12609, "htm": 12610, "荳": 12611, "hong": 12612, "kong": 12613, "bean": 12614, "璇": 12615, "霖": 12616, "锣": 12617, "呎": 12618, "僭": 12619, "서": 12620, "울": 12621, "挝": 12622, "丕": 12623, "莅": 12624, "圹": 12625, "阮": 12626, "狡": 12627, "诩": 12628, "馥": 12629, "‧": 12630, "峩": 12631, "戎": 12632, "隼": 12633, "伫": 12634, "圀": 12635, "弑": 12636, "诰": 12637, "constable": 12638, "england": 12639, "悍": 12640, "﹕": 12641, "２": 12642, "０": 12643, "蚝": 12644, "梿": 12645, "．": 12646, "徕": 12647, "『": 12648, "』": 12649, "馔": 12650, "唛": 12651, "鸳": 12652, "鸯": 12653, "衞": 12654, "撷": 12655, "linux": 12656, "stuff": 12657, "ascend": 12658, "libri": 12659, "viewer": 12660, "converter": 12661, "ew": 12662, "／": 12663, "police": 12664, "headquarters": 12665, "kph": 12666, "逵": 12667, "destiny": 12668, "beyonc": 12669, "knowles": 12670, "tavi": 12671, "mathew": 12672, "girl": 12673, "tina": 12674, "elektra": 12675, "columbia": 12676, "brewer": 12677, "killing": 12678, "remix": 12679, "billboard": 12680, "窠": 12681, "boo": 12682, "kevin": 12683, "briggs": 12684, "writing": 12685, "simmons": 12686, "stay": 12687, "michelle": 12688, "旭": 12689, "霸": 12690, "carmen": 12691, "kars": 12692, "骢": 12693, "usk": 12694, "think": 12695, "讫": 12696, "sony": 12697, "埼": 12698, "wiz": 12699, "町": 12700, "shuttle": 12701, "瓯": 12702, "file": 12703, "udf": 12704, "zh": 12705, "vanderbilt": 12706, "whitney": 12707, "rockefeller": 12708, "laura": 12709, "investor": 12710, "abb": 12711, "riot": 12712, "ralph": 12713, "flanders": 12714, "％": 12715, "partner": 12716, "charlie": 12717, "waite": 12718, "venture": 12719, "technicolor": 12720, "maid": 12721, "draper": 12722, "sutter": 12723, "fairchild": 12724, "semiconductor": 12725, "associate": 12726, "road": 12727, "perkins": 12728, "sequoia": 12729, "clara": 12730, "eris": 12731, "晩": 12732, "袍": 12733, "tvb": 12734, "heng": 12735, "於": 12736, "邨": 12737, "ftp": 12738, "宦": 12739, "舖": 12740, "荃": 12741, "rejoice": 12742, "焗": 12743, "蓉": 12744, "qatar": 12745, "miz": 12746, "compagnie": 12747, "limon": 12748, "ｘ": 12749, "bates": 12750, "bei": 12751, "rana": 12752, "iana": 12753, "～": 12754, "莆": 12755, "凰": 12756, "阁": 12757, "砌": 12758, "杪": 12759, "嵩": 12760, "赣": 12761, "榫": 12762, "chapman": 12763, "淦": 12764, "乍": 12765, "岑": 12766, "婿": 12767, "靖": 12768, "谥": 12769, "祐": 12770, "濶": 12771, "墀": 12772, "槎": 12773, "宴": 12774, "榭": 12775, "谭": 12776, "麟": 12777, "炘": 12778, "阎": 12779, "钧": 12780, "樵": 12781, "鑫": 12782, "砚": 12783, "伶": 12784, "畹": 12785, "祠": 12786, "jump": 12787, "lazy": 12788, "ㄴ": 12789, "ㅇ": 12790, "녀": 12791, "여": 12792, "な": 12793, "だ": 12794, "ncaa": 12795, "player": 12796, "award": 12797, "刽": 12798, "鹈": 12799, "崖": 12800, "篆": 12801, "邕": 12802, "猷": 12803, "陇": 12804, "晖": 12805, "暠": 12806, "荥": 12807, "寅": 12808, "戍": 12809, "臧": 12810, "寮": 12811, "僊": 12812, "觐": 12813, "駉": 12814, "尉": 12815, "绥": 12816, "懿": 12817, "align": 12818, "笙": 12819, "媛": 12820, "叱": 12821, "咤": 12822, "袁": 12823, "progress": 12824, "charisma": 12825, "umd": 12826, "expo": 12827, "aber": 12828, "interactive": 12829, "story": 12830, "š": 12831, "hannah": 12832, "hah": 12833, "amma": 12834, "苇": 12835, "queen": 12836, "heaven": 12837, "temple": 12838, "usu": 12839, "uzi": 12840, "enki": 12841, "hul": 12842, "璀": 12843, "璨": 12844, "pala": 12845, "curb": 12846, "jara": 12847, "lula": 12848, "朔": 12849, "嫡": 12850, "畿": 12851, "ewe": 12852, "bye": 12853, "gustav": 12854, "nacht": 12855, "彝": 12856, "祁": 12857, "琯": 12858, "璐": 12859, "佚": 12860, "阙": 12861, "毖": 12862, "帙": 12863, "沅": 12864, "ß": 12865, "schie": 12866, "镖": 12867, "bernd": 12868, "zwei": 12869, "auf": 12870, "砲": 12871, "蜀": 12872, "琮": 12873, "凫": 12874, "翡": 12875, "渚": 12876, "璧": 12877, "栩": 12878, "獠": 12879, "瑗": 12880, "cpu": 12881, "fetch": 12882, "execute": 12883, "闵": 12884, "−": 12885, "筿": 12886, "掳": 12887, "妃": 12888, "擒": 12889, "悌": 12890, "菈": 12891, "琉": 12892, "蔷": 12893, "薇": 12894, "捎": 12895, "弩": 12896, "赃": 12897, "冤": 12898, "轿": 12899, "谍": 12900, "book": 12901, "瀬": 12902, "琵": 12903, "娣": 12904, "笠": 12905, "巳": 12906, "冢": 12907, "玆": 12908, "糸": 12909, "頼": 12910, "敕": 12911, "堀": 12912, "み": 12913, "毬": 12914, "嶋": 12915, "菩": 12916, "筱": 12917, "lulu": 12918, "昙": 12919, "攫": 12920, "耆": 12921, "栋": 12922, "valign": 12923, "cec": 12924, "friend": 12925, "rend": 12926, "yen": 12927, "嚎": 12928, "趟": 12929, "钞": 12930, "巅": 12931, "遑": 12932, "rimo": 12933, "kaw": 12934, "暌": 12935, "淑": 12936, "waik": 12937, "itf": 12938, "鏖": 12939, "跻": 12940, "跆": 12941, "桦": 12942, "malek": 12943, "蕙": 12944, "urna": 12945, "saab": 12946, "琛": 12947, "逋": 12948, "滉": 12949, "俚": 12950, "cbd": 12951, "骊": 12952, "惺": 12953, "屐": 12954, "禧": 12955, "jy": 12956, "gothic": 12957, "麂": 12958, "羌": 12959, "冇": 12960, "happening": 12961, "笈": 12962, "music": 12963, "羚": 12964, "奕": 12965, "嬅": 12966, "脍": 12967, "shall": 12968, "𪸩": 12969, "悼": 12970, "钗": 12971, "isbn": 12972, "moonlight": 12973, "烽": 12974, "phone": 12975, "lala": 12976, "ビ": 12977, "ス": 12978, "コ": 12979, "※": 12980, "〜": 12981, "・": 12982, "factory": 12983, "祺": 12984, "rotunda": 12985, "ð": 12986, "unicode": 12987, "廖": 12988, "尧": 12989, "邑": 12990, "娶": 12991, "棨": 12992, "勖": 12993, "皓": 12994, "溥": 12995, "崤": 12996, "琪": 12997, "隽": 12998, "elise": 12999, "macy": 13000, "dream": 13001, "闺": 13002, "佘": 13003, "angela": 13004, "ゑ": 13005, "び": 13006, "す": 13007, "渋": 13008, "恵": 13009, "き": 13010, "ʰ": 13011, "ʷ": 13012, "̚": 13013, "ʃ": 13014, "vacation": 13015, "ʼ": 13016, "ქ": 13017, "კ": 13018, "椿": 13019, "壹": 13020, "sept": 13021, "jeff": 13022, "helen": 13023, "telephone": 13024, "rubber": 13025, "nadia": 13026, "malcolm": 13027, "found": 13028, "chocolate": 13029, "濑": 13030, "suica": 13031, "icoca": 13032, "jan": 13033, "coen": 13034, "capella": 13035, "pulau": 13036, "bang": 13037, "hendrik": 13038, "銮": 13039, "骅": 13040, "kio": 13041, "斌": 13042, "埔": 13043, "玮": 13044, "wah": 13045, "埗": 13046, "钰": 13047, "porn": 13048, "牠": 13049, "黍": 13050, "莞": 13051, "抨": 13052, "colour": 13053, "嫦": 13054, "骝": 13055, "拚": 13056, "绰": 13057, "彬": 13058, "仝": 13059, "邝": 13060, "arbeit": 13061, "macht": 13062, "frei": 13063, "stratos": 13064, "giulio": 13065, "patri": 13066, "zio": 13067, "italian": 13068, "suzuki": 13069, "汝": 13070, "稗": 13071, "艘": 13072, "柬": 13073, "regime": 13074, "chiayi": 13075, "眺": 13076, "淍": 13077, "汐": 13078, "坍": 13079, "褫": 13080, "岱": 13081, "叡": 13082, "焜": 13083, "czk": 13084, "boot": 13085, "arena": 13086, "坳": 13087, "聂": 13088, "hui": 13089, "峒": 13090, "筝": 13091, "磐": 13092, "gur": 13093, "barbara": 13094, "喰": 13095, "manuel": 13096, "naka": 13097, "vcd": 13098, "菫": 13099, "鄣": 13100, "込": 13101, "嬬": 13102, "あ": 13103, "づ": 13104, "ま": 13105, "ば": 13106, "し": 13107, "璠": 13108, "翥": 13109, "庵": 13110, "汾": 13111, "婺": 13112, "誊": 13113, "锺": 13114, "observer": 13115, " ": 13116, "缈": 13117, "钪": 13118, "gev": 13119, "msv": 13120, "nasa": 13121, "cosmic": 13122, "铧": 13123, "naver": 13124, "nhn": 13125, "圜": 13126, "ultimate": 13127, "wav": 13128, "advancement": 13129, "卯": 13130, "colspan": 13131, "script": 13132, "ɡ": 13133, "ʒ": 13134, "ŋ": 13135, "singing": 13136, "ginger": 13137, "绽": 13138, "蕤": 13139, "皐": 13140, "酉": 13141, "戌": 13142, "ä": 13143, "honeycomb": 13144, "葭": 13145, "electronics": 13146, "nics": 13147, "tev": 13148, "cern": 13149, "洮": 13150, "昴": 13151, "罄": 13152, "逞": 13153, "恣": 13154, "苑": 13155, "龠": 13156, "籥": 13157, "曦": 13158, "jordan": 13159, "pumping": 13160, "station": 13161, "wong": 13162, "proposed": 13163, "flushing": 13164, "kun": 13165, "tong": 13166, "river": 13167, "kowloon": 13168, "scheme": 13169, "cemetery": 13170, "霄": 13171, "merry": 13172, "youtuber": 13173, "once": 13174, "edwin": 13175, "telecom": 13176, "碁": 13177, "gsm": 13178, "锾": 13179, "慵": 13180, "吒": 13181, "俪": 13182, "²": 13183, "匡": 13184, "旼": 13185, "盉": 13186, "罍": 13187, "缛": 13188, "蕃": 13189, "帖": 13190, "坌": 13191, "罟": 13192, "戸": 13193, "ciel": 13194, "feat": 13195, "ù": 13196, "vous": 13197, "啦": 13198, "alto": 13199, "nyo": 13200, "澎": 13201, "倭": 13202, "寇": 13203, "倶": 13204, "礁": 13205, "î": 13206, "notre": 13207, "miami": 13208, "beach": 13209, "囗": 13210, "垡": 13211, "皋": 13212, "蚡": 13213, "濞": 13214, "绾": 13215, "诛": 13216, "劾": 13217, "渭": 13218, "笞": 13219, "饬": 13220, "衙": 13221, "楣": 13222, "５": 13223, "１": 13224, "７": 13225, "６": 13226, "roosevelt": 13227, "倣": 13228, "埕": 13229, "taipei": 13230, "俶": 13231, "嶂": 13232, "暝": 13233, "籁": 13234, "ㄉ": 13235, "ㄚ": 13236, "ˋ": 13237, "　": 13238, "ㄈ": 13239, "ㄨ": 13240, "à": 13241, "ū": 13242, "蔺": 13243, "闾": 13244, "谏": 13245, "瓩": 13246, "擞": 13247, "竝": 13248, "pyrex": 13249, "rung": 13250, "栞": 13251, "front": 13252, "hello": 13253, "festival": 13254, "party": 13255, "ハ": 13256, "ロ": 13257, "プ": 13258, "fantasy": 13259, "allegro": 13260, "alameda": 13261, "鄂": 13262, "孚": 13263, "裥": 13264, "龢": 13265, "漳": 13266, "麾": 13267, "皖": 13268, "ntt": 13269, "docomo": 13270, "cdma": 13271, "涿": 13272, "cuo": 13273, "nowiki": 13274, "cucl": 13275, "த": 13276, "ம": 13277, "ி": 13278, "்": 13279, "ī": 13280, "ila": 13281, "ṅ": 13282, "ல": 13283, "க": 13284, "ை": 13285, "ltte": 13286, "gana": 13287, "gangster": 13288, "ceylon": 13289, "tamil": 13290, "congress": 13291, "ayaka": 13292, "tiger": 13293, "interim": 13294, "governing": 13295, "authority": 13296, "federal": 13297, "territorial": 13298, "integrity": 13299, "raja": 13300, "rudra": 13301, "桢": 13302, "煊": 13303, "邺": 13304, "耒": 13305, "颐": 13306, "ɕ": 13307, "iau": 13308, "kuan": 13309, "yan": 13310, "kau": 13311, "rotte": 13312, "caco": 13313, "chong": 13314, "investment": 13315, "ㄹ": 13316, "rang": 13317, "eng": 13318, "lix": 13319, "리": 13320, "이": 13321, "ら": 13322, "坩": 13323, "埚": 13324, "pacific": 13325, "broadband": 13326, "wireless": 13327, "telecommunication": 13328, "adsl": 13329, "bix": 13330, "cctld": 13331, "info": 13332, "dns": 13333, "express": 13334, "cebit": 13335, "浔": 13336, "讷": 13337, "grand": 13338, "prize": 13339, "镲": 13340, "lhc": 13341, "bunch": 13342, "psb": 13343, "alice": 13344, "totem": 13345, "atlas": 13346, "@": 13347, "boinc": 13348, "odd": 13349, "恺": 13350, "calvin": 13351, "teacher": 13352, "隃": 13353, "bartlett": 13354, "lux": 13355, "render": 13356, "shade": 13357, "pixar": 13358, "觑": 13359, "佑": 13360, "ά": 13361, "ο": 13362, "ς": 13363, "ɨ": 13364, "ʉ": 13365, "ɯ": 13366, "ɣ": 13367, "ɬ": 13368, "ʁ": 13369, "ʕ": 13370, "ɦ": 13371, "泗": 13372, "歴": 13373, "mie": 13374, "business": 13375, "匜": 13376, "阡": 13377, "犸": 13378, "accelerator": 13379, "炬": 13380, "boomerang": 13381, "kid": 13382, "り": 13383, "ゅ": 13384, "う": 13385, "さ": 13386, "お": 13387, "に": 13388, "よ": 13389, "る": 13390, "処": 13391, "か": 13392, "た": 13393, "の": 13394, "ミ": 13395, "ッ": 13396, "ク": 13397, "绫": 13398, "刁": 13399, "砟": 13400, "richmond": 13401, "brae": 13402, "airport": 13403, "boy": 13404, "playstation": 13405, "portable": 13406, "gamecube": 13407, "xbox": 13408, "wii": 13409, "online": 13410, "evolve": 13411, "gamasutra": 13412, "稔": 13413, "ufo": 13414, "shiro": 13415, "cgi": 13416, "alfa": 13417, "crow": 13418, "koki": 13419, "ign": 13420, "trailer": 13421, "渤": 13422, "驎": 13423, "夤": 13424, "bah": 13425, "png": 13426, "ó": 13427, "̄": 13428, "͘": 13429, "醢": 13430, "tah": 13431, "dana": 13432, "dreamcast": 13433, "磡": 13434, "筲": 13435, "urban": 13436, "racing": 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14822, "チ": 14823, "ほ": 14824, "っ": 14825, "モ": 14826, "カ": 14827, "レ": 14828, "れ": 14829, "ち": 14830, "software": 14831, "硏": 14832, "sata": 14833, "scsi": 14834, "raid": 14835, "premier": 14836, "administrator": 14837, "enterprise": 14838, "ひ": 14839, "ゃ": 14840, "stardust": 14841, "yai": 14842, "壝": 14843, "棂": 14844, "瘗": 14845, "燎": 14846, "栱": 14847, "玺": 14848, "迳": 14849, "爰": 14850, "≒": 14851, "苾": 14852, "豳": 14853, "泾": 14854, "steam": 14855, "locomotive": 14856, "amtrak": 14857, "soda": 14858, "姪": 14859, "digg": 14860, "unix": 14861, "regiment": 14862, "volunteer": 14863, "啹": 14864, "仃": 14865, "artillery": 14866, "徜": 14867, "quad": 14868, "controller": 14869, "motorola": 14870, "powerpc": 14871, "rtos": 14872, "imap": 14873, "smtp": 14874, "outlook": 14875, "mozilla": 14876, "thunderbird": 14877, "becky": 14878, "koo": 14879, "mime": 14880, "hotmail": 14881, "歙": 14882, "骠": 14883, "侄": 14884, "厩": 14885, "november": 14886, "icao": 14887, "airline": 14888, "southwest": 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"berliner": 15221, "sauro": 15222, "fortress": 15223, "erling": 15224, "firenze": 15225, "mess": 15226, "api": 15227, "riko": 15228, "muhammad": 15229, "ざ": 15230, "ね": 15231, "﨑": 15232, "tung": 15233, "chung": 15234, "cable": 15235, "bittorrent": 15236, "ascii": 15237, "marty": 15238, "usenet": 15239, "م": 15240, "ح": 15241, "د": 15242, "ش": 15243, "ه": 15244, "rosh": 15245, "觊": 15246, "觎": 15247, "syed": 15248, "hussain": 15249, "hassan": 15250, "蹂": 15251, "躏": 15252, "badminton": 15253, "４": 15254, "ヤ": 15255, "嫔": 15256, "鸩": 15257, "commodore": 15258, "webb": 15259, "掞": 15260, "鳝": 15261, "砀": 15262, "虢": 15263, "邢": 15264, "陉": 15265, "donald": 15266, "箧": 15267, "badger": 15268, "琊": 15269, "谗": 15270, "掎": 15271, "纾": 15272, "羯": 15273, "忿": 15274, "啖": 15275, "rip": 15276, "postscript": 15277, "谄": 15278, "関": 15279, "祂": 15280, "耜": 15281, "忻": 15282, "create": 15283, "伎": 15284, "funk": 15285, "sleeping": 15286, "bear": 15287, "away": 15288, "そ": 15289, "elly": 15290, 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15427, "alvin": 15428, "vauxhall": 15429, "gti": 15430, "驷": 15431, "凼": 15432, "trent": 15433, "ascent": 15434, "mount": 15435, "cruise": 15436, "underwood": 15437, "meadow": 15438, "harper": 15439, "atlantic": 15440, "elder": 15441, "glacier": 15442, "louisa": 15443, "muir": 15444, "鹫": 15445, "carolyn": 15446, "merchant": 15447, "sierra": 15448, "terence": 15449, "tso": 15450, "holly": 15451, "mook": 15452, "brendan": 15453, "hansen": 15454, "dimitri": 15455, "kom": 15456, "niel": 15457, "gyu": 15458, "yakov": 15459, "door": 15460, "bx": 15461, "fels": 15462, "dak": 15463, "ｂ": 15464, "arma": 15465, "olympic": 15466, "簷": 15467, "翱": 15468, "espn": 15469, "macau": 15470, "吿": 15471, "tuna": 15472, "mbar": 15473, "doom": 15474, "atar": 15475, "ㄌ": 15476, "ㄟ": 15477, "ㄙ": 15478, "斫": 15479, "锹": 15480, "wired": 15481, "standby": 15482, "pcie": 15483, "configuration": 15484, "packet": 15485, "ipx": 15486, "novell": 15487, "netware": 15488, "toolbar": 15489, "folding": 15490, "shake": 15491, "shaker": 15492, "hank": 15493, "jon": 15494, "xen": 15495, "springer": 15496, "sdos": 15497, "meri": 15498, "铐": 15499, "煲": 15500, "kampf": 15501, "恫": 15502, "indus": 15503, "willy": 15504, "neue": 15505, "kestrel": 15506, "eft": 15507, "bmw": 15508, "flap": 15509, "fuel": 15510, "weser": 15511, "zeppelin": 15512, "flame": 15513, "supercharger": 15514, "wolfgang": 15515, "kano": 15516, "quartier": 15517, "luftwaffe": 15518, "bruno": 15519, "hrer": 15520, "czy": 15521, "wec": 15522, "æ": 15523, "afr": 15524, "groupe": 15525, "chasse": 15526, "sei": 15527, "dallas": 15528, "jack": 15529, "mantle": 15530, "chatham": 15531, "tuff": 15532, "dido": 15533, "ajax": 15534, "juno": 15535, "gloucester": 15536, "greyhound": 15537, "yash": 15538, "acht": 15539, "fry": 15540, "utc": 15541, "phoebe": 15542, "俐": 15543, "鲗": 15544, "dickson": 15545, "茗": 15546, "sunday": 15547, "唁": 15548, "pavel": 15549, "ř": 15550, "ovsk": 15551, "ý": 15552, "schule": 15553, "franti": 15554, "schulz": 15555, "leipzig": 15556, "conservatory": 15557, "oskar": 15558, "grill": 15559, "ů": 15560, "gabriela": 15561, "＊": 15562, "reliable": 15563, "kira": 15564, "ciao": 15565, "ship": 15566, "旳": 15567, "靳": 15568, "楹": 15569, "崂": 15570, "sebastiano": 15571, "francesco": 15572, "cosplay": 15573, "asset": 15574, "wiki": 15575, "wikimedia": 15576, "wikipedia": 15577, "os": 15578, "moz": 15579, "ت": 15580, "ط": 15581, "ئ": 15582, "و": 15583, "س": 15584, "坜": 15585, "暸": 15586, "𫚭": 15587, "treasury": 15588, "animated": 15589, "粲": 15590, "嘏": 15591, "狎": 15592, "stirling": 15593, "mohammed": 15594, "hadid": 15595, "foul": 15596, "斓": 15597, "folly": 15598, "jpg": 15599, "疍": 15600, "astor": 15601, "岘": 15602, "paramount": 15603, "parade": 15604, "滢": 15605, "铳": 15606, "玑": 15607, "烨": 15608, "戛": 15609, "暻": 15610, "bamba": 15611, "stevie": 15612, "wonder": 15613, "marvin": 15614, "gaye": 15615, "clapton": 15616, "brian": 15617, "would": 15618, "muse": 15619, "cheap": 15620, "荑": 15621, 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"india": 15750, "codec": 15751, "showtime": 15752, "institutional": 15753, "certified": 15754, "accountant": 15755, "uniform": 15756, "logistics": 15757, "depart": 15758, "gott": 15759, "noh": 15760, "狠": 15761, "［": 15762, "］": 15763, "tiffany": 15764, "葳": 15765, "妣": 15766, "疃": 15767, "苻": 15768, "漒": 15769, "罴": 15770, "秣": 15771, "岷": 15772, "𪻺": 15773, "氐": 15774, "阗": 15775, "罽": 15776, "鄯": 15777, "牦": 15778, "芈": 15779, "艮": 15780, "珖": 15781, "mace": 15782, "snail": 15783, "paso": 15784, "dras": 15785, "guthrie": 15786, "yun": 15787, "wali": 15788, "yu": 15789, "llah": 15790, "愍": 15791, "⇔": 15792, "炤": 15793, "庾": 15794, "飡": 15795, "𪟝": 15796, "䃅": 15797, "胤": 15798, "邽": 15799, "雒": 15800, "朏": 15801, "兮": 15802, "⊆": 15803, "祗": 15804, "邛": 15805, "郫": 15806, "阆": 15807, "撄": 15808, "赟": 15809, "阏": 15810, "tony": 15811, "炅": 15812, "巍": 15813, "幡": 15814, "钲": 15815, "锽": 15816, "翦": 15817, "迩": 15818, "颃": 15819, "眕": 15820, "tak": 15821, "svt": 15822, "thales": 15823, "gonz": 15824, "г": 15825, "nak": 15826, "炜": 15827, "泅": 15828, "锏": 15829, "tech": 15830, "hilton": 15831, "resort": 15832, "robinson": 15833, "擧": 15834, "驒": 15835, "淸": 15836, "charged": 15837, "weapon": 15838, "sdi": 15839, "颙": 15840, "img": 15841, "hora": 15842, "徴": 15843, "coming": 15844, "forth": 15845, "nw": 15846, "椁": 15847, "bce": 15848, "晸": 15849, "钫": 15850, "畈": 15851, "垭": 15852, "沔": 15853, "坨": 15854, "袄": 15855, "antoine": 15856, "davy": 15857, "huil": 15858, "recreational": 15859, "innovative": 15860, "郅": 15861, "​": 15862, "consumer": 15863, "ر": 15864, "ص": 15865, "ف": 15866, "崆": 15867, "infinity": 15868, "岖": 15869, "茔": 15870, "韫": 15871, "rohr": 15872, "panzer": 15873, "granat": 15874, "chos": 15875, "tenne": 15876, "wick": 15877, "mission": 15878, "were": 15879, "哐": 15880, "coverage": 15881, "using": 15882, "between": 15883, "becomes": 15884, "隠": 15885, "阋": 15886, "julio": 15887, "ngel": 15888, "ndez": 15889, "소": 15890, "행": 15891, "성": 15892, "pluto": 15893, "harold": 15894, "planetary": 15895, "gonzalo": 15896, "拏": 15897, "瞥": 15898, "酢": 15899, "雎": 15900, "姒": 15901, "惣": 15902, "淯": 15903, "旸": 15904, "bleach": 15905, "flower": 15906, "bianco": 15907, "happy": 15908, "goodbye": 15909, "feel": 15910, "yamaguchi": 15911, "tomorrow": 15912, "hino": 15913, "remember": 15914, "thank": 15915, "teen": 15916, "understand": 15917, "truth": 15918, "ノ": 15919, "laugh": 15920, "mint": 15921, "loved": 15922, "yesterday": 15923, "yeah": 15924, "oui": 15925, "pony": 15926, "too": 15927, "much": 15928, "gloria": 15929, "holiday": 15930, "your": 15931, "paradise": 15932, "vevo": 15933, "staff": 15934, "ヌ": 15935, "suzanne": 15936, "ballad": 15937, "sunshine": 15938, "氹": 15939, "鲇": 15940, "菅": 15941, "◎": 15942, "偌": 15943, "wang": 15944, "delf": 15945, "窖": 15946, "blau": 15947, "涞": 15948, "牤": 15949, "犷": 15950, "辎": 15951, "瑯": 15952, "峤": 15953, "鲲": 15954, "鯓": 15955, "脔": 15956, "bertha": 15957, "圮": 15958, "璈": 15959, "崧": 15960, "缎": 15961, "tainan": 15962, "鞆": 15963, "蚂": 15964, "toyota": 15965, "doremi": 15966, "砬": 15967, "gara": 15968, "kuta": 15969, "baobab": 15970, "湉": 15971, "廨": 15972, "荩": 15973, "瘐": 15974, "kbo": 15975, "iwan": 15976, "dav": 15977, "atz": 15978, "tatar": 15979, "稙": 15980, "粑": 15981, "ɤ": 15982, "˧": 15983, "agaric": 15984, "matt": 15985, "dev": 15986, "scott": 15987, "adrian": 15988, "clifford": 15989, "trident": 15990, "gecko": 15991, "webkit": 15992, "presto": 15993, "tasman": 15994, "dll": 15995, "travis": 15996, "patrol": 15997, "feeder": 15998, "subway": 15999, "bowie": 16000, "preacher": 16001, "armada": 16002, "weller": 16003, "bryan": 16004, "sailor": 16005, "darkness": 16006, "diy": 16007, "klein": 16008, "pagerank": 16009, "approximation": 16010, "tish": 16011, "francisco": 16012, "fernandes": 16013, "vak": 16014, "hsh": 16015, "osr": 16016, "熜": 16017, "忤": 16018, "寘": 16019, "𫔍": 16020, "鐩": 16021, "驸": 16022, "唬": 16023, "夙": 16024, "祧": 16025, "璁": 16026, "珤": 16027, "鄢": 16028, "韬": 16029, "鬻": 16030, "嬖": 16031, "鄱": 16032, "ب": 16033, "ي": 16034, "ع": 16035, "ل": 16036, "أ": 16037, "髻": 16038, "siam": 16039, "rollo": 16040, "û": 16041, "evil": 16042, "share": 16043, "ʾ": 16044, "amuse": 16045, "arra": 16046, "newell": 16047, "guinea": 16048, "darin": 16049, "erst": 16050, "troy": 16051, "glau": 16052, "molina": 16053, "bravo": 16054, "溎": 16055, "餽": 16056, "dudley": 16057, "埈": 16058, "鋆": 16059, "翚": 16060, "lance": 16061, "fighting": 16062, "ambient": 16063, "箩": 16064, "goz": 16065, "ぎ": 16066, "ダ": 16067, "ヲ": 16068, "ア": 16069, "ツ": 16070, "郷": 16071, "pelt": 16072, "thun": 16073, "lossless": 16074, "encoding": 16075, "nema": 16076, "cbe": 16077, "concepci": 16078, "villa": 16079, "vici": 16080, "hodgson": 16081, "tack": 16082, "尢": 16083, "zee": 16084, "伉": 16085, "labour": 16086, "赙": 16087, "gerard": 16088, "howe": 16089, "alma": 16090, "brook": 16091, "nylon": 16092, "boll": 16093, "骕": 16094, "宓": 16095, "堃": 16096, "歆": 16097, "茍": 16098, "侪": 16099, "雩": 16100, "yue": 16101, "chun": 16102, "glen": 16103, "almond": 16104, "sophie": 16105, "johanna": 16106, "lha": 16107, "macintosh": 16108, "desktop": 16109, "dock": 16110, "encryption": 16111, "nextstep": 16112, "篓": 16113, "spotlight": 16114, "command": 16115, "leopard": 16116, "look": 16117, "unified": 16118, "theme": 16119, "俨": 16120, "irt": 16121, "柘": 16122, "league": 16123, "pitcher": 16124, "rodr": 16125, "slur": 16126, "豊": 16127, "矧": 16128, "蠊": 16129, "german": 16130, "faster": 16131, "residential": 16132, "growing": 16133, "develop": 16134, "until": 16135, "begin": 16136, "fully": 16137, "about": 16138, "soon": 16139, "within": 16140, "few": 16141, "later": 16142, "chestnut": 16143, "keel": 16144, "along": 16145, "where": 16146, "curl": 16147, "slightly": 16148, "towards": 16149, "nymph": 16150, "while": 16151, "attached": 16152, "majority": 16153, "before": 16154, "becoming": 16155, "nearly": 16156, "successful": 16157, "establishing": 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"setup": 16219, "糗": 16220, "olson": 16221, "baseball": 16222, "sanchez": 16223, "pendolino": 16224, "linx": 16225, "nsb": 16226, "dsb": 16227, "tgv": 16228, "puff": 16229, "pork": 16230, "elan": 16231, "washington": 16232, "colorado": 16233, "walton": 16234, "鳔": 16235, "summit": 16236, "㓥": 16237, "塅": 16238, "sprinter": 16239, "utah": 16240, "馓": 16241, "镬": 16242, "connect": 16243, "jeanne": 16244, "gautier": 16245, "prades": 16246, "鹣": 16247, "鲽": 16248, "nter": 16249, "rcio": 16250, "piano": 16251, "greatest": 16252, "novel": 16253, "brus": 16254, "dmitri": 16255, "lapin": 16256, "roll": 16257, "coating": 16258, "vermont": 16259, "奭": 16260, "仫": 16261, "texas": 16262, "saul": 16263, "linus": 16264, "schroeder": 16265, "怅": 16266, "rhode": 16267, "畑": 16268, "畠": 16269, "埂": 16270, "剐": 16271, "曈": 16272, "燐": 16273, "埸": 16274, "鵺": 16275, "alfred": 16276, "jingle": 16277, "hotel": 16278, "ballet": 16279, "isha": 16280, "delaware": 16281, "fletcher": 16282, "祢": 16283, 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"nord": 18355, "camb": 18356, "kingsley": 18357, "combe": 18358, "parlement": 18359, "ronde": 18360, "rue": 18361, "gue": 18362, "padre": 18363, "almeida": 18364, "bobo": 18365, "淖": 18366, "岌": 18367, "fraunhofer": 18368, "dresden": 18369, "sweden": 18370, "hdtv": 18371, "deux": 18372, "neuf": 18373, "sachsen": 18374, "herzog": 18375, "jj": 18376, "cabo": 18377, "qed": 18378, "quo": 18379, "erat": 18380, "へ": 18381, "笹": 18382, "trick": 18383, "abba": 18384, "manufacturer": 18385, "invent": 18386, "呙": 18387, "wear": 18388, "ghq": 18389, "熠": 18390, "laude": 18391, "summa": 18392, "magna": 18393, "slot": 18394, "fey": 18395, "liberty": 18396, "rost": 18397, "𫍯": 18398, "湳": 18399, "다": 18400, "า": 18401, "ด": 18402, "pki": 18403, "zhu": 18404, "ु": 18405, "芃": 18406, "buick": 18407, "lacrosse": 18408, "蝼": 18409, "‥": 18410, "逹": 18411, "lorenz": 18412, "evolutionary": 18413, "mayr": 18414, "rik": 18415, "augsburg": 18416, "essen": 18417, "tten": 18418, "reichenbach": 18419, "buz": 18420, "faber": 18421, "poland": 18422, "scania": 18423, "caetano": 18424, "milang": 18425, "吆": 18426, "deuce": 18427, "珺": 18428, "沣": 18429, "霭": 18430, "boone": 18431, "isberg": 18432, "¹": 18433, "aide": 18434, "wandering": 18435, "farewell": 18436, "carrasco": 18437, "tanz": 18438, "flor": 18439, "kahn": 18440, "shack": 18441, "lust": 18442, "simo": 18443, "illustrious": 18444, "susanna": 18445, "cheung": 18446, "collective": 18447, "衹": 18448, "andrei": 18449, "jpl": 18450, "skytrax": 18451, "reuters": 18452, "airbus": 18453, "䜣": 18454, "绠": 18455, "ụ": 18456, "comedy": 18457, "噏": 18458, "姗": 18459, "贌": 18460, "weld": 18461, "iida": 18462, "骐": 18463, "versatile": 18464, "dohc": 18465, "tronic": 18466, "hdd": 18467, "nismo": 18468, "tune": 18469, "gts": 18470, "pulsar": 18471, "sedan": 18472, "push": 18473, "juke": 18474, "dci": 18475, "automotive": 18476, "germania": 18477, "rttemberg": 18478, "芗": 18479, "homburg": 18480, "hadrian": 18481, "pius": 18482, "ahl": 18483, "grab": 18484, "mauer": 18485, "nieder": 18486, "hun": 18487, "zug": 18488, "ober": 18489, "milt": 18490, "altstadt": 18491, "jag": 18492, "hire": 18493, "bingen": 18494, "lheim": 18495, "statt": 18496, "禛": 18497, "羑": 18498, "爻": 18499, "math": 18500, "焌": 18501, "dawes": 18502, "暐": 18503, "绂": 18504, "衿": 18505, "蠲": 18506, "诳": 18507, "禟": 18508, "珽": 18509, "煦": 18510, "gymnasium": 18511, "helene": 18512, "zeitschrift": 18513, "chemie": 18514, "bred": 18515, "boden": 18516, "reso": 18517, "噍": 18518, "哖": 18519, "纫": 18520, "螟": 18521, "rnik": 18522, "lumo": 18523, "oise": 18524, "瑷": 18525, "磬": 18526, "汩": 18527, "mouri": 18528, "amalie": 18529, "bourbon": 18530, "sicily": 18531, "劢": 18532, "ferdinand": 18533, "descendant": 18534, "beatrix": 18535, "vreux": 18536, "died": 18537, "psu": 18538, "frederic": 18539, "audition": 18540, "梃": 18541, "鼋": 18542, "dylan": 18543, "lisp": 18544, "garbage": 18545, "soup": 18546, "enable": 18547, "lily": 18548, "helicopter": 18549, "lough": 18550, "lockheed": 18551, "electra": 18552, "constellation": 18553, "臃": 18554, "mille": 18555, "fils": 18556, "minos": 18557, "prag": 18558, "jef": 18559, "estado": 18560, "espa": 18561, "dios": 18562, "parma": 18563, "nassau": 18564, "stationary": 18565, "翕": 18566, "檎": 18567, "virgin": 18568, "yoshida": 18569, "marvelous": 18570, "marble": 18571, "blank": 18572, "kashima": 18573, "谲": 18574, "sunset": 18575, "刄": 18576, "shower": 18577, "aru": 18578, "pimp": 18579, "session": 18580, "mellow": 18581, "traveling": 18582, "supreme": 18583, "vivid": 18584, "呷": 18585, "benji": 18586, "hmv": 18587, "fcs": 18588, "hfc": 18589, "wacker": 18590, "henrietta": 18591, "beverly": 18592, "crusade": 18593, "hope": 18594, "ned": 18595, "ṇ": 18596, "buddha": 18597, "ṛ": 18598, "啾": 18599, "amazon": 18600, "chrome": 18601, "compete": 18602, "韺": 18603, "缒": 18604, "汜": 18605, "訚": 18606, "羸": 18607, "尪": 18608, "郛": 18609, "splendid": 18610, "軚": 18611, "鿕": 18612, "sandy": 18613, "zebra": 18614, "carpio": 18615, "casper": 18616, "invention": 18617, "trek": 18618, "starship": 18619, "galactic": 18620, "ṭ": 18621, "憍": 18622, "eden": 18623, "ronald": 18624, "braga": 18625, "疴": 18626, "豢": 18627, "馊": 18628, "winston": 18629, "russell": 18630, "palmer": 18631, "觋": 18632, "naomi": 18633, "gonzo": 18634, "dynamical": 18635, "giuseppe": 18636, "luciano": 18637, "vincenzo": 18638, "nino": 18639, "orbi": 18640, "蓦": 18641, "cora": 18642, "vecchio": 18643, "asked": 18644, "longing": 18645, "passed": 18646, "forgotten": 18647, "truman": 18648, "expect": 18649, "涑": 18650, "嫄": 18651, "镆": 18652, "燔": 18653, "渑": 18654, "猗": 18655, "痧": 18656, "岢": 18657, "aves": 18658, "cristina": 18659, "莜": 18660, "畤": 18661, "虒": 18662, "ไ": 18663, "ท": 18664, "ย": 18665, "ร": 18666, "ั": 18667, "ก": 18668, "sasha": 18669, "gibbs": 18670, "yoyo": 18671, "frost": 18672, "sow": 18673, "fowler": 18674, "kih": 18675, "lud": 18676, "ervin": 18677, "clint": 18678, "tyrone": 18679, "fool": 18680, "isaiah": 18681, "limp": 18682, "soho": 18683, "batch": 18684, "reported": 18685, "tanner": 18686, "傧": 18687, "siegel": 18688, "lisa": 18689, "隰": 18690, "饸": 18691, "饹": 18692, "kerala": 18693, "jing": 18694, "sank": 18695, "milford": 18696, "iota": 18697, "send": 18698, "shaping": 18699, "pung": 18700, "akita": 18701, "taw": 18702, "oban": 18703, "鹪": 18704, "鹩": 18705, "daman": 18706, "penang": 18707, "marathon": 18708, "morley": 18709, "marr": 18710, "lyric": 18711, "rosemary": 18712, "domenico": 18713, "verg": 18714, "gary": 18715, "hail": 18716, "claudio": 18717, "massimo": 18718, "parking": 18719, "motorsport": 18720, "mercedes": 18721, "motogp": 18722, "applause": 18723, "vicente": 18724, "guez": 18725, "lez": 18726, "kore": 18727, "seventh": 18728, "𫟼": 18729, "tibet": 18730, "britain": 18731, "russia": 18732, "engage": 18733, "negotiation": 18734, "except": 18735, "楫": 18736, "抟": 18737, "摅": 18738, "笕": 18739, "malone": 18740, "ô": 18741, "梣": 18742, "€": 18743, "颔": 18744, "apter": 18745, "䌹": 18746, "elz": 18747, "塡": 18748, "霾": 18749, "饕": 18750, "撘": 18751, "roque": 18752, "quez": 18753, "saharan": 18754, "nek": 18755, "idris": 18756, "ibn": 18757, "lih": 18758, "hajj": 18759, "bakr": 18760, "sultan": 18761, "rafa": 18762, "wana": 18763, "jil": 18764, "dick": 18765, "鼍": 18766, "largo": 18767, "jesus": 18768, "kcr": 18769, "ˈ": 18770, "ɪ": 18771, "ː": 18772, "sog": 18773, "machu": 18774, "quintus": 18775, "hort": 18776, "runt": 18777, "petr": 18778, "concordia": 18779, "bona": 18780, "lucius": 18781, "cornelius": 18782, "albus": 18783, "publius": 18784, "alea": 18785, "rufus": 18786, "sola": 18787, "chili": 18788, "susi": 18789, "agora": 18790, "dura": 18791, "basileus": 18792, "megas": 18793, "secure": 18794, "洭": 18795, "borland": 18796, "driven": 18797, "omg": 18798, "uml": 18799, "pamela": 18800, "sien": 18801, "blackout": 18802, "snowman": 18803, "jerk": 18804, "stephanie": 18805, "shoe": 18806, "tarzan": 18807, "perk": 18808, "leslie": 18809, "elizabeth": 18810, "bitch": 18811, "婊": 18812, "gato": 18813, "च": 18814, "ग": 18815, "प": 18816, "rah": 18817, "爨": 18818, "郸": 18819, "馑": 18820, "苌": 18821, "槁": 18822, "鷟": 18823, "鄠": 18824, "摭": 18825, "舂": 18826, "绖": 18827, "詈": 18828, "剳": 18829, "缣": 18830, "橚": 18831, "熺": 18832, "獍": 18833, "鄤": 18834, "殍": 18835, "枵": 18836, "噫": 18837, "８": 18838, "婓": 18839, "gcmg": 18840, "brunel": 18841, "哙": 18842, "隗": 18843, "郾": 18844, "octo": 18845, "煖": 18846, "chem": 18847, "∕": 18848, "olive": 18849, "螭": 18850, "bola": 18851, "lynn": 18852, "conway": 18853, "vlsi": 18854, "darpa": 18855, "canyon": 18856, "harley": 18857, "hdi": 18858, "chopper": 18859, "motorcycle": 18860, "dyna": 18861, "touring": 18862, "keung": 18863, "倬": 18864, "ز": 18865, "ј": 18866, "zit": 18867, "ṣ": 18868, "bha": 18869, "祩": 18870, "忉": 18871, "皎": 18872, "sufi": 18873, "karam": 18874, "legend": 18875, "lounge": 18876, "馄": 18877, "饨": 18878, "selim": 18879, "mahl": 18880, "xxi": 18881, "studi": 18882, "mikhail": 18883, "reader": 18884, "mathematics": 18885, "milestone": 18886, "wendell": 18887, "williston": 18888, "othniel": 18889, "鬣": 18890, "fantasia": 18891, "º": 18892, "onard": 18893, "cultural": 18894, "singapore": 18895, "lsm": 18896, "utm": 18897, "crank": 18898, "kamen": 18899, "nost": 18900, "㎡": 18901, "northeast": 18902, "鲵": 18903, "ejb": 18904, "lij": 18905, "nuit": 18906, "maximilian": 18907, "franziska": 18908, "hedwig": 18909, "jn": 18910, "signed": 18911, "haru": 18912, "kobayashi": 18913, "艏": 18914, "tully": 18915, "琚": 18916, "𫖯": 18917, "mister": 18918, "courtenay": 18919, "hind": 18920, "iut": 18921, "vancouver": 18922, "裒": 18923, "聃": 18924, "bloomberg": 18925, "ǔ": 18926, "ifpi": 18927, "珹": 18928, "sela": 18929, "dyke": 18930, "dith": 18931, "miriam": 18932, "mikl": 18933, "jeu": 18934, "inuit": 18935, "wilmington": 18936, "barth": 18937, "encyclop": 18938, "britannica": 18939, "榎": 18940, "charlene": 18941, "went": 18942, "leong": 18943, "kwok": 18944, "going": 18945, "cmg": 18946, "kcmg": 18947, "sovereign": 18948, "fourier": 18949, "convolution": 18950, "nami": 18951, "mend": 18952, "shih": 18953, "啁": 18954, "cutoff": 18955, "髀": 18956, "珓": 18957, "tft": 18958, "wdf": 18959, "仉": 18960, "jaya": 18961, "prakash": 18962, "narayan": 18963, "kumar": 18964, "埴": 18965, "kwun": 18966, "tsai": 18967, "제": 18968, "티": 18969, "ites": 18970, "tonne": 18971, "cradle": 18972, "further": 18973, "graeme": 18974, "痳": 18975, "dominus": 18976, "martian": 18977, "麋": 18978, "archbishop": 18979, "谌": 18980, "＃": 18981, "default": 18982, "argument": 18983, "alias": 18984, "krak": 18985, "乗": 18986, "÷": 18987, "sterreich": 18988, "sankt": 18989, "jive": 18990, "覇": 18991, "greco": 18992, "cobra": 18993, "keroro": 18994, "jabber": 18995, "xmpp": 18996, "sigh": 18997, "synergy": 18998, "▲": 18999, "│": 19000, "szl": 19001, "ń": 19002, "zyk": 19003, "strau": 19004, "qo": 19005, "creator": 19006, "dwan": 19007, "lant": 19008, "wink": 19009, "燿": 19010, "mix": 19011, "cervus": 19012, "formosan": 19013, "sika": 19014, "deer": 19015, "抡": 19016, "jew": 19017, "lauda": 19018, "鹨": 19019, "昝": 19020, "陟": 19021, "赊": 19022, "梆": 19023, "馍": 19024, "湟": 19025, "hak": 19026, "掦": 19027, "ʔ": 19028, "flynn": 19029, "娉": 19030, "ykk": 19031, "濯": 19032, "provide": 19033, "provided": 19034, "hur": 19035, "黩": 19036, "硖": 19037, "壆": 19038, "hdmi": 19039, "gentle": 19040, "cutie": 19041, "chase": 19042, "ryu": 19043, "hett": 19044, "rainy": 19045, "promise": 19046, "sapporo": 19047, "kumi": 19048, "nagoya": 19049, "someday": 19050, "wish": 19051, "happiness": 19052, "blis": 19053, "juicy": 19054, "搧": 19055, "deluxe": 19056, "rainbow": 19057, "utada": 19058, "hikaru": 19059, "bounce": 19060, "premium": 19061, "anytime": 19062, "crusader": 19063, "ヒ": 19064, "楽": 19065, "osaka": 19066, "fanclub": 19067, "blitz": 19068, "taboo": 19069, "ballroom": 19070, "alive": 19071, "universe": 19072, "cheryl": 19073, "gossip": 19074, "outside": 19075, "official": 19076, "eternity": 19077, "deja": 19078, "diva": 19079, "bonus": 19080, "birthday": 19081, "esque": 19082, "uul": 19083, "inspired": 19084, "pica": 19085, "kenji": 19086, "darling": 19087, "fatima": 19088, "supported": 19089, "scape": 19090, "hush": 19091, "countdown": 19092, "bridget": 19093, "visa": 19094, "nude": 19095, "穰": 19096, "鞮": 19097, "豨": 19098, "tourist": 19099, "polyphony": 19100, "hang": 19101, "陔": 19102, "lawson": 19103, "tait": 19104, "笪": 19105, "ksa": 19106, "awb": 19107, "thaw": 19108, "fay": 19109, "qat": 19110, "franken": 19111, "bison": 19112, "sabre": 19113, "naw": 19114, "hsbc": 19115, "beef": 19116, "ahlen": 19117, "manche": 19118, "luther": 19119, "隷": 19120, "posed": 19121, "fucking": 19122, "tatu": 19123, "ume": 19124, "kudo": 19125, "watanabe": 19126, "boris": 19127, "dangerous": 19128, "viktoria": 19129, "disabled": 19130, "steinberg": 19131, "veronica": 19132, "jess": 19133, "eternal": 19134, "foe": 19135, "interscope": 19136, "robe": 19137, "udi": 19138, "invalid": 19139, "﹔": 19140, "繋": 19141, "姶": 19142, "tenor": 19143, "andante": 19144, "moderato": 19145, "cresc": 19146, "maes": 19147, "dolce": 19148, "ledger": 19149, "longo": 19150, "bardi": 19151, "epik": 19152, "augustus": 19153, "gable": 19154, "adobe": 19155, "trill": 19156, "premiere": 19157, "creative": 19158, "booth": 19159, "cinema": 19160, "frontier": 19161, "bourgogne": 19162, "ouch": 19163, "aoc": 19164, "arrondissement": 19165, "dijon": 19166, "chef": 19167, "lieu": 19168, "communaut": 19169, "jura": 19170, "rieur": 19171, "divi": 19172, "verdun": 19173, "duch": 19174, "fon": 19175, "moyen": 19176, "salar": 19177, "nivea": 19178, "aix": 19179, "porte": 19180, "neuve": 19181, "fco": 19182, "ligue": 19183, "塽": 19184, "shaun": 19185, "鸾": 19186, "bonnie": 19187, "roundtable": 19188, "consultancy": 19189, "betty": 19190, "aya": 19191, "elgin": 19192, "vanguard": 19193, "breaker": 19194, "gaur": 19195, "striker": 19196, "dragoon": 19197, "armament": 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"cursor": 25379, "xslt": 25380, "mashup": 25381, "json": 25382, "silverlight": 25383, "rear": 25384, "voith": 25385, "ail": 25386, "aluminium": 25387, "yorkshire": 25388, "ensign": 25389, "mungo": 25390, "islander": 25391, "indigenous": 25392, "nwa": 25393, "infantry": 25394, "battalion": 25395, "woodward": 25396, "naya": 25397, "zenith": 25398, "湝": 25399, "exists": 25400, "subaru": 25401, "clean": 25402, "replay": 25403, "flac": 25404, "mahi": 25405, "taung": 25406, "ike": 25407, "mako": 25408, "焼": 25409, "gennaro": 25410, "juliette": 25411, "blossom": 25412, "teaux": 25413, "鎓": 25414, "𡒉": 25415, "protectorate": 25416, "kimi": 25417, "endowment": 25418, "buckley": 25419, "bogen": 25420, "baguette": 25421, "powder": 25422, "bastard": 25423, "parisi": 25424, "dracula": 25425, "milla": 25426, "submarine": 25427, "suck": 25428, "risen": 25429, "mensch": 25430, "kassa": 25431, "haile": 25432, "giorgi": 25433, "steward": 25434, "spence": 25435, "toki": 25436, "wq": 25437, "pastor": 25438, "fandom": 25439, "wwv": 25440, "caj": 25441, "carolus": 25442, "batu": 25443, "kimberley": 25444, "艶": 25445, "dart": 25446, "wed": 25447, "urbana": 25448, "transparent": 25449, "mgo": 25450, "mcintosh": 25451, "kenwood": 25452, "aidan": 25453, "ayers": 25454, "kaede": 25455, "怿": 25456, "艸": 25457, "権": 25458, "醂": 25459, "toda": 25460, "犨": 25461, "shooter": 25462, "nkr": 25463, "geographical": 25464, "rinaldo": 25465, "laksa": 25466, "lakh": 25467, "pantai": 25468, "timur": 25469, "sarawak": 25470, "kampung": 25471, "kuala": 25472, "johor": 25473, "bahru": 25474, "salle": 25475, "reboot": 25476, "delete": 25477, "odori": 25478, "rchen": 25479, "bilder": 25480, "friendly": 25481, "nock": 25482, "serif": 25483, "bitstream": 25484, "graphite": 25485, "justification": 25486, "sinfonia": 25487, "feldman": 25488, "반": 25489, "임": 25490, "유": 25491, "lehmann": 25492, "ammann": 25493, "trinidad": 25494, "combine": 25495, "december": 25496, "fairview": 25497, "intuit": 25498, 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25559, "辵": 25560, "囲": 25561, "basilica": 25562, "sint": 25563, "domination": 25564, "kiva": 25565, "tmf": 25566, "cingular": 25567, "kane": 25568, "llano": 25569, "jamaica": 25570, "♥": 25571, "𬓑": 25572, "檊": 25573, "ouden": 25574, "veen": 25575, "witte": 25576, "weer": 25577, "daft": 25578, "kenna": 25579, "cypher": 25580, "踼": 25581, "獴": 25582, "rker": 25583, "stork": 25584, "mga": 25585, "gramma": 25586, "adair": 25587, "oia": 25588, "tonk": 25589, "goro": 25590, "helle": 25591, "崱": 25592, "artifact": 25593, "breakfast": 25594, "yoshiki": 25595, "violet": 25596, "岞": 25597, "崄": 25598, "耷": 25599, "pucci": 25600, "rammstein": 25601, "iste": 25602, "vau": 25603, "coul": 25604, "unti": 25605, "camden": 25606, "euclid": 25607, "indra": 25608, "cadillac": 25609, "lasalle": 25610, "turret": 25611, "fleetwood": 25612, "escalade": 25613, "luxury": 25614, "cras": 25615, "浟": 25616, "筇": 25617, "垤": 25618, "赕": 25619, "鹇": 25620, "脿": 25621, "咢": 25622, "筌": 25623, "enso": 25624, "gabe": 25625, "reddy": 25626, "mistake": 25627, "speaking": 25628, "mindstorms": 25629, "nxt": 25630, "episcopal": 25631, "chennai": 25632, "淓": 25633, "hyde": 25634, "pcg": 25635, "wicket": 25636, "lame": 25637, "copter": 25638, "peach": 25639, "locale": 25640, "departure": 25641, "previous": 25642, "failed": 25643, "awarded": 25644, "according": 25645, "reply": 25646, "additional": 25647, "whether": 25648, "whenever": 25649, "provides": 25650, "detail": 25651, "ordered": 25652, "added": 25653, "additionally": 25654, "solve": 25655, "featured": 25656, "airplane": 25657, "runway": 25658, "landing": 25659, "unlike": 25660, "unless": 25661, "heading": 25662, "gameplay": 25663, "similar": 25664, "hyacinth": 25665, "parrot": 25666, "grus": 25667, "acacia": 25668, "pohl": 25669, "isidor": 25670, "shallow": 25671, "vegan": 25672, "茈": 25673, "邆": 25674, "嶍": 25675, "佧": 25676, "闫": 25677, "mustang": 25678, "𫐐": 25679, "堌": 25680, "alberta": 25681, "堽": 25682, "寗": 25683, "浉": 25684, "estadio": 25685, "ferre": 25686, "adolfo": 25687, "wanderer": 25688, "𪹚": 25689, "trooper": 25690, "churchill": 25691, "humboldt": 25692, "eureka": 25693, "瑍": 25694, "ambulance": 25695, "钶": 25696, "rowe": 25697, "𫊸": 25698, "ncsa": 25699, "rooney": 25700, "rube": 25701, "whip": 25702, "karim": 25703, "zimmer": 25704, "mien": 25705, "椟": 25706, "marais": 25707, "箬": 25708, "layton": 25709, "cartier": 25710, "chanel": 25711, "arizona": 25712, "panama": 25713, "tout": 25714, "bikini": 25715, "menace": 25716, "angry": 25717, "瑊": 25718, "voiced": 25719, "jetty": 25720, "浠": 25721, "delia": 25722, "approved": 25723, "wgs": 25724, "augustine": 25725, "ludo": 25726, "jasper": 25727, "henk": 25728, "hash": 25729, "梼": 25730, "below": 25731, "metz": 25732, "beyer": 25733, "freiburg": 25734, "ull": 25735, "flaming": 25736, "succession": 25737, "➤": 25738, "夬": 25739, "trevor": 25740, "souvenir": 25741, "contribution": 25742, "arsenal": 25743, "westinghouse": 25744, "hydraulic": 25745, "uia": 25746, "liet": 25747, "felice": 25748, "薬": 25749, "pendleton": 25750, "fossil": 25751, "perce": 25752, "tonya": 25753, "vary": 25754, "newport": 25755, "陴": 25756, "leonid": 25757, "䂵": 25758, "kohler": 25759, "rhine": 25760, "griff": 25761, "thames": 25762, "libby": 25763, "valuable": 25764, "zurich": 25765, "contains": 25766, "capability": 25767, "offshore": 25768, "widely": 25769, "claimed": 25770, "mariner": 25771, "忭": 25772, "kann": 25773, "diplom": 25774, "emilia": 25775, "puerto": 25776, "baile": 25777, "confused": 25778, "brunswick": 25779, "provo": 25780, "grizzly": 25781, "parkway": 25782, "ammi": 25783, "evin": 25784, "maat": 25785, "gee": 25786, "oxley": 25787, "chaplin": 25788, "pedal": 25789, "lotus": 25790, "coldplay": 25791, "everybody": 25792, "gonna": 25793, "anymore": 25794, "fairytale": 25795, "priory": 25796, "gwen": 25797, "ouen": 25798, "rabbi": 25799, "trenton": 25800, "mexico": 25801, "magick": 25802, "gameboy": 25803, "corsa": 25804, "romeo": 25805, "lancia": 25806, "maserati": 25807, "chrysler": 25808, "gmac": 25809, "爿": 25810, "ukko": 25811, "worcester": 25812, "旉": 25813, "frigate": 25814, "galle": 25815, "corvette": 25816, "祔": 25817, "amusement": 25818, "kwong": 25819, "櫈": 25820, "trinit": 25821, "sliver": 25822, "瑮": 25823, "harden": 25824, "舣": 25825, "硇": 25826, "maring": 25827, "hdf": 25828, "lagoon": 25829, "amphibious": 25830, "呉": 25831, "鲻": 25832, "jure": 25833, "bulgarian": 25834, "kiev": 25835, "principality": 25836, "conceptual": 25837, "verification": 25838, "akron": 25839, "clifton": 25840, "dvor": 25841, "leap": 25842, "castell": 25843, "interplay": 25844, "ubisoft": 25845, "petty": 25846, "thief": 25847, "aftermath": 25848, "liman": 25849, "mediterranean": 25850, "esmeralda": 25851, "ricardo": 25852, "kathryn": 25853, "ramsey": 25854, "favorite": 25855, "nader": 25856, "molly": 25857, "lillian": 25858, "haley": 25859, "minneapolis": 25860, "dumont": 25861, "tire": 25862, "culver": 25863, "forty": 25864, "connecticut": 25865, "tango": 25866, "wilton": 25867, "fez": 25868, "baku": 25869, "ruger": 25870, "粃": 25871, "豇": 25872, "sober": 25873, "cerro": 25874, "roden": 25875, "oude": 25876, "uju": 25877, "salina": 25878, "grandes": 25879, "quia": 25880, "pampa": 25881, "folder": 25882, "hug": 25883, "jig": 25884, "rten": 25885, "thang": 25886, "䝙": 25887, "pronoun": 25888, "astron": 25889, "nuova": 25890, "scamp": 25891, "lense": 25892, "muni": 25893, "ruz": 25894, "shel": 25895, "mame": 25896, "damian": 25897, "esau": 25898, "ryb": 25899, "joule": 25900, "valois": 25901, "registered": 25902, "働": 25903, "⌋": 25904, "boucher": 25905, "anyone": 25906, "瓖": 25907, "睥": 25908, "sindhu": 25909, "靑": 25910, "beating": 25911, "designated": 25912, "牻": 25913, "lola": 25914, "jennifer": 25915, "ashley": 25916, "brewster": 25917, "gner": 25918, "runescape": 25919, "jagex": 25920, "tangent": 25921, "achievement": 25922, "compi": 25923, "wilderness": 25924, "spawn": 25925, "zamora": 25926, "bounty": 25927, "thanksgiving": 25928, "accurate": 25929, "mortimer": 25930, "obispo": 25931, "coastal": 25932, "kpm": 25933, "glas": 25934, "wort": 25935, "automation": 25936, "belv": 25937, "quirk": 25938, "berkshire": 25939, "gebr": 25940, "bangor": 25941, "auburn": 25942, "lub": 25943, "jsp": 25944, "ellsworth": 25945, "dover": 25946, "belfast": 25947, "楗": 25948, "grenade": 25949, "atsc": 25950, "faulkner": 25951, "alton": 25952, "戽": 25953, "eung": 25954, "pleasant": 25955, "syracuse": 25956, "ouest": 25957, "lurk": 25958, "bebo": 25959, "xion": 25960, "wnba": 25961, "rajah": 25962, "shrine": 25963, "ctype": 25964, "dpkg": 25965, "identifier": 25966, "simplified": 25967, "davison": 25968, "rhel": 25969, "unbreakable": 25970, "ｒ": 25971, "fantastic": 25972, "dtv": 25973, "seiko": 25974, "edda": 25975, "厖": 25976, "thanks": 25977, "donato": 25978, "latino": 25979, "jamb": 25980, "hula": 25981, "swami": 25982, "esl": 25983, "speaker": 25984, "lenny": 25985, "arcadia": 25986, "hernando": 25987, "柒": 25988, "barron": 25989, "kink": 25990, "polydor": 25991, "nelly": 25992, "ʐ": 25993, "ɿ": 25994, "lexington": 25995, "koga": 25996, "cmm": 25997, "quincy": 25998, "chula": 25999, "hines": 26000, "ponce": 26001, "sped": 26002, "tsunami": 26003, "tough": 26004, "cano": 26005, "瓈": 26006, "gpo": 26007, "玓": 26008, "yuta": 26009, "msp": 26010, "wellness": 26011, "ڠ": 26012, "泂": 26013, "processed": 26014, "itself": 26015, "showing": 26016, "computation": 26017, "verde": 26018, "dez": 26019, "slaughter": 26020, "extasy": 26021, "unfinished": 26022, "tempest": 26023, "haug": 26024, "herbie": 26025, "metallica": 26026, "ै": 26027, "़": 26028, "suu": 26029, "kapoor": 26030, "durga": 26031, "sharma": 26032, "starz": 26033, "绲": 26034, "豲": 26035, "miley": 26036, "lettres": 26037, "augusto": 26038, "vitali": 26039, "fdr": 26040, "伡": 26041, "attitude": 26042, "vive": 26043, "bandai": 26044, "mankind": 26045, "brave": 26046, "syfy": 26047, "goodyear": 26048, "courthouse": 26049, "township": 26050, "medley": 26051, "wyman": 26052, "諌": 26053, "etops": 26054, "operational": 26055, "cfr": 26056, "banking": 26057, "ernesto": 26058, "gera": 26059, "mistral": 26060, "kya": 26061, "solis": 26062, "sni": 26063, "dahlia": 26064, "blessing": 26065, "octave": 26066, "tantra": 26067, "vajra": 26068, "remington": 26069, "sousa": 26070, "sier": 26071, "unisys": 26072, "eckert": 26073, "哝": 26074, "敫": 26075, "kfc": 26076, "tasty": 26077, "ferris": 26078, "蘂": 26079, "従": 26080, "cosmos": 26081, "淏": 26082, "playboy": 26083, "leif": 26084, "ravel": 26085, "lausanne": 26086, "lookup": 26087, "legge": 26088, "髡": 26089, "ripper": 26090, "kopp": 26091, "macpherson": 26092, "playground": 26093, "ಿ": 26094, "ನ": 26095, "ರ": 26096, "araya": 26097, "齮": 26098, "್": 26099, "lamborghini": 26100, "gallardo": 26101, "glock": 26102, "ಾ": 26103, "ந": 26104, "dodd": 26105, "madurai": 26106, "ு": 26107, "walkman": 26108, "rotterdam": 26109, "hague": 26110, "portuguese": 26111, "celestial": 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26177, "volker": 26178, "腙": 26179, "electoral": 26180, "nicolaus": 26181, "teller": 26182, "昜": 26183, "독": 26184, "konstantin": 26185, "jared": 26186, "眇": 26187, "reside": 26188, "匽": 26189, "fwd": 26190, "regno": 26191, "ivanov": 26192, "cooking": 26193, "meal": 26194, "toward": 26195, "shelby": 26196, "consult": 26197, "behar": 26198, "伋": 26199, "redwood": 26200, "hillary": 26201, "patna": 26202, "熘": 26203, "buu": 26204, "waa": 26205, "civilization": 26206, "鄫": 26207, "copeland": 26208, "ude": 26209, "nagara": 26210, "nirvana": 26211, "essai": 26212, "colli": 26213, "sector": 26214, "ㄆ": 26215, "midtown": 26216, "猢": 26217, "狲": 26218, "緁": 26219, "yule": 26220, "recursion": 26221, "gigi": 26222, "ecker": 26223, "sese": 26224, "seko": 26225, "媭": 26226, "廃": 26227, "sniper": 26228, "reload": 26229, "possibility": 26230, "aike": 26231, "yoo": 26232, "escola": 26233, "italia": 26234, "raine": 26235, "裇": 26236, "柊": 26237, "pudding": 26238, "䌷": 26239, "sosa": 26240, "santi": 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26484, "yours": 26485, "truly": 26486, "papal": 26487, "aven": 26488, "stiftung": 26489, "仳": 26490, "adj": 26491, "ะ": 26492, "달": 26493, "禑": 26494, "ֶ": 26495, "ק": 26496, "ȟ": 26497, "omari": 26498, "punt": 26499, "crat": 26500, "cng": 26501, "hades": 26502, "bemani": 26503, "gita": 26504, "cymbal": 26505, "melissa": 26506, "maas": 26507, "hessen": 26508, "lons": 26509, "nsk": 26510, "osha": 26511, "sane": 26512, "wroc": 26513, "kuj": 26514, "gabor": 26515, "habsburg": 26516, "constance": 26517, "synagogue": 26518, "硚": 26519, "boxing": 26520, "raster": 26521, "cider": 26522, "abby": 26523, "栿": 26524, "稃": 26525, "winged": 26526, "倏": 26527, "elst": 26528, "fouqu": 26529, "arata": 26530, "petrie": 26531, "palestine": 26532, "釆": 26533, "ivpp": 26534, "stylus": 26535, "flagship": 26536, "monty": 26537, "fang": 26538, "weston": 26539, "tuscany": 26540, "patsy": 26541, "rochefort": 26542, "crowley": 26543, "zira": 26544, "involved": 26545, "leaving": 26546, "getting": 26547, 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26612, "ade": 26613, "墁": 26614, "chios": 26615, "schmitt": 26616, "politische": 26617, "kein": 26618, "risch": 26619, "demokrat": 26620, "partei": 26621, "schwarze": 26622, "korps": 26623, "ő": 26624, "kassel": 26625, "貔": 26626, "镢": 26627, "tobias": 26628, "晈": 26629, "constructor": 26630, "montague": 26631, "fub": 26632, "tread": 26633, "consolidated": 26634, "peabody": 26635, "菰": 26636, "稞": 26637, "hampton": 26638, "pper": 26639, "entirely": 26640, "㈠": 26641, "㈡": 26642, "㈢": 26643, "rupal": 26644, "hardware": 26645, "parachute": 26646, "doesn": 26647, "cliff": 26648, "mixture": 26649, "rault": 26650, "michal": 26651, "ethno": 26652, "pritchard": 26653, "slashdot": 26654, "carmack": 26655, "鏳": 26656, "metallic": 26657, "xps": 26658, "josep": 26659, "asociaci": 26660, "devo": 26661, "gallup": 26662, "polska": 26663, "foto": 26664, "djvu": 26665, "hiram": 26666, "wynne": 26667, "potts": 26668, "郐": 26669, "natur": 26670, "nebo": 26671, "mouvement": 26672, "vols": 26673, 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27105, "explosion": 27106, "schaefer": 27107, "durant": 27108, "westbrook": 27109, "bele": 27110, "konz": 27111, "leibniz": 27112, "pvs": 27113, "█": 27114, "媺": 27115, "aiden": 27116, "hsing": 27117, "祾": 27118, "papilio": 27119, "𩾇": 27120, "盝": 27121, "dillon": 27122, "tight": 27123, "hornet": 27124, "gentry": 27125, "stephenson": 27126, "tahu": 27127, "rahi": 27128, "teridax": 27129, "餍": 27130, "eyck": 27131, "researcher": 27132, "heiner": 27133, "종": 27134, "switched": 27135, "畚": 27136, "verse": 27137, "alexandria": 27138, "aiko": 27139, "turbine": 27140, "mexicana": 27141, "abstraction": 27142, "durin": 27143, "barrack": 27144, "barge": 27145, "嬢": 27146, "donut": 27147, "garry": 27148, "pons": 27149, "𤞤": 27150, "debug": 27151, "bojan": 27152, "dusk": 27153, "臯": 27154, "vliw": 27155, "barkley": 27156, "compressor": 27157, "trumbull": 27158, "皦": 27159, "鍪": 27160, "rees": 27161, "seau": 27162, "บ": 27163, "ค": 27164, "์": 27165, "aurillac": 27166, "politique": 27167, 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"tarn": 27539, "bluebird": 27540, "cristatus": 27541, "饣": 27542, "𤩽": 27543, "mongoliensis": 27544, "raptor": 27545, "gode": 27546, "ulsan": 27547, "tamer": 27548, "saver": 27549, "alban": 27550, "eighth": 27551, "clough": 27552, "picard": 27553, "arad": 27554, "hendrix": 27555, "lindoro": 27556, "taddeo": 27557, "brion": 27558, "cris": 27559, "afd": 27560, "殛": 27561, "轵": 27562, "gazelle": 27563, "arabian": 27564, "oryx": 27565, "wsc": 27566, "piquet": 27567, "邶": 27568, "sleipnir": 27569, "polizei": 27570, "gsg": 27571, "篪": 27572, "iyo": 27573, "摀": 27574, "hele": 27575, "ṯ": 27576, "ḫ": 27577, "rather": 27578, "determine": 27579, "psalm": 27580, "ignition": 27581, "敻": 27582, "礓": 27583, "溲": 27584, "轳": 27585, "鳢": 27586, "olof": 27587, "粢": 27588, "郈": 27589, "solidarity": 27590, "thanh": 27591, "broom": 27592, "axle": 27593, "ecw": 27594, "mpu": 27595, "lancashire": 27596, "澚": 27597, "㎞": 27598, "scared": 27599, "捒": 27600, "rotc": 27601, "plume": 27602, "gutenberg": 27603, "woe": 27604, "mischa": 27605, "henryk": 27606, "vivaldi": 27607, "camillo": 27608, "quid": 27609, "theology": 27610, "benetton": 27611, "symonds": 27612, "adore": 27613, "kech": 27614, "pee": 27615, "nguyen": 27616, "pham": 27617, "wsop": 27618, "parser": 27619, "attach": 27620, "preview": 27621, "phenom": 27622, "richland": 27623, "carri": 27624, "godavari": 27625, "challenger": 27626, "paganini": 27627, "koshi": 27628, "seismic": 27629, "beale": 27630, "jur": 27631, "雟": 27632, "llama": 27633, "argus": 27634, "maximus": 27635, "와": 27636, "세": 27637, "열": 27638, "과": 27639, "khel": 27640, "ट": 27641, "livia": 27642, "晊": 27643, "saman": 27644, "aignan": 27645, "brittany": 27646, "istv": 27647, "ferenc": 27648, "bree": 27649, "髫": 27650, "pandora": 27651, "alejandro": 27652, "mbti": 27653, "keir": 27654, "castelnau": 27655, "cond": 27656, "wola": 27657, "dso": 27658, "juris": 27659, "freehand": 27660, "elrond": 27661, "eldar": 27662, "noldor": 27663, "swansea": 27664, "severn": 27665, "cobain": 27666, "ọ": 27667, "composition": 27668, "iranian": 27669, "persian": 27670, "umlaut": 27671, "magdalena": 27672, "홍": 27673, "mackintosh": 27674, "shakespeare": 27675, "bayard": 27676, "galloway": 27677, "maguire": 27678, "kuhn": 27679, "abell": 27680, "crowe": 27681, "corm": 27682, "workstation": 27683, "‭": 27684, "‬": 27685, "龁": 27686, "oromo": 27687, "埝": 27688, "鬷": 27689, "bitmap": 27690, "animator": 27691, "tartar": 27692, "㹴": 27693, "nielsen": 27694, "sophia": 27695, "薗": 27696, "慇": 27697, "cinta": 27698, "persia": 27699, "wiggle": 27700, "iglesias": 27701, "慝": 27702, "국": 27703, "총": 27704, "雱": 27705, "bbq": 27706, "headed": 27707, "esteem": 27708, "gillespie": 27709, "cathy": 27710, "romantic": 27711, "chinatown": 27712, "multiplayer": 27713, "nashville": 27714, "dign": 27715, "obscura": 27716, "tatum": 27717, "wimbledon": 27718, "outback": 27719, "타": 27720, "용": 27721, "singen": 27722, "grail": 27723, "ponta": 27724, "atlanta": 27725, "austen": 27726, 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"刧": 27791, "gorm": 27792, "herring": 27793, "suke": 27794, "wep": 27795, "wpa": 27796, "secam": 27797, "暲": 27798, "창": 27799, "poker": 27800, "mug": 27801, "蟊": 27802, "oleg": 27803, "affiliated": 27804, "埿": 27805, "carlton": 27806, "houghton": 27807, "chama": 27808, "endian": 27809, "tty": 27810, "rendezvous": 27811, "exist": 27812, "entourage": 27813, "ecole": 27814, "turin": 27815, "moire": 27816, "baj": 27817, "bruin": 27818, "playoff": 27819, "鳇": 27820, "槚": 27821, "flake": 27822, "corea": 27823, "oen": 27824, "belo": 27825, "remy": 27826, "joni": 27827, "husky": 27828, "hermitage": 27829, "brentwood": 27830, "rke": 27831, "prefecture": 27832, "koji": 27833, "舄": 27834, "蓥": 27835, "concordance": 27836, "connell": 27837, "quis": 27838, "bien": 27839, "rhee": 27840, "kendrick": 27841, "chanson": 27842, "cours": 27843, "margherita": 27844, "samurai": 27845, "mauritius": 27846, "racecourse": 27847, "deadly": 27848, "uptown": 27849, "shor": 27850, "skyscraper": 27851, "觔": 27852, 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28038, "naacp": 28039, "microphone": 28040, "cain": 28041, "medio": 28042, "儇": 28043, "cambodia": 28044, "taku": 28045, "faye": 28046, "禋": 28047, "鹓": 28048, "discover": 28049, "masse": 28050, "painting": 28051, "너": 28052, "바": 28053, "람": 28054, "꽃": 28055, "哟": 28056, "shinee": 28057, "환": 28058, "학": 28059, "교": 28060, "christof": 28061, "harmonica": 28062, "琫": 28063, "埽": 28064, "呸": 28065, "rainer": 28066, "thurn": 28067, "riemann": 28068, "arriva": 28069, "romulus": 28070, "remus": 28071, "jtf": 28072, "bure": 28073, "sunos": 28074, "merdeka": 28075, "cheri": 28076, "urbs": 28077, "보": 28078, "레": 28079, "스": 28080, "unleashed": 28081, "霅": 28082, "interlude": 28083, "prepared": 28084, "峣": 28085, "madeira": 28086, "batista": 28087, "浿": 28088, "trm": 28089, "ữ": 28090, "bloomfield": 28091, "郚": 28092, " ": 28093, "chevron": 28094, "texaco": 28095, "huwa": 28096, "bakar": 28097, "俋": 28098, "ặ": 28099, "muar": 28100, "diocesan": 28101, "fett": 28102, "bsch": 28103, "strict": 28104, "monad": 28105, "hati": 28106, "machado": 28107, "barnet": 28108, "marta": 28109, "hunk": 28110, "kfar": 28111, "pinky": 28112, "簃": 28113, "tude": 28114, "樉": 28115, "澯": 28116, "槏": 28117, "㸅": 28118, "𤈷": 28119, "爚": 28120, "楧": 28121, "墭": 28122, "浤": 28123, "焞": 28124, "鉌": 28125, "栘": 28126, "炟": 28127, "栐": 28128, "锳": 28129, "壐": 28130, "墡": 28131, "楬": 28132, "㴋": 28133, "樻": 28134, "埨": 28135, "柟": 28136, "labium": 28137, "apps": 28138, "簒": 28139, "dearest": 28140, "綷": 28141, "pinball": 28142, "aladdin": 28143, "majestic": 28144, "gamerankings": 28145, "evg": 28146, "ample": 28147, "thay": 28148, "㐌": 28149, "siva": 28150, "bambi": 28151, "塍": 28152, "wicker": 28153, "wrote": 28154, "shem": 28155, "americano": 28156, "dinh": 28157, "cfm": 28158, "crook": 28159, "solitude": 28160, "남": 28161, "蒺": 28162, "skyliner": 28163, "snapdragon": 28164, "aska": 28165, "kune": 28166, "𫔰": 28167, "hamer": 28168, "sgt": 28169, "confirmed": 28170, "aab": 28171, "sgml": 28172, "slink": 28173, "alchemy": 28174, "huac": 28175, "crum": 28176, "aware": 28177, "brandy": 28178, "jody": 28179, "bleue": 28180, "ahan": 28181, "beloved": 28182, "everyone": 28183, "gotta": 28184, "continue": 28185, "moved": 28186, "olaf": 28187, "oberg": 28188, "martyn": 28189, "crunchy": 28190, "원": 28191, "lega": 28192, "btc": 28193, "burgos": 28194, "antoinette": 28195, "cray": 28196, "선": 28197, "화": 28198, "kwon": 28199, "cosplayer": 28200, "荄": 28201, "afro": 28202, "willamette": 28203, "alchemist": 28204, "auditor": 28205, "wirth": 28206, "conducted": 28207, "cene": 28208, "leonhard": 28209, "권": 28210, "suba": 28211, "sativa": 28212, "ganja": 28213, "蒉": 28214, "usan": 28215, "maryann": 28216, "mariana": 28217, "baek": 28218, "lave": 28219, "alix": 28220, "aguirre": 28221, "rolando": 28222, "tyson": 28223, "deron": 28224, "mastercard": 28225, "ucf": 28226, "minidisc": 28227, "iomega": 28228, "algol": 28229, "statue": 28230, "logger": 28231, "daa": 28232, "厓": 28233, "刖": 28234, 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28300, "𠡠": 28301, "aeroplane": 28302, "benchmark": 28303, "𨮁": 28304, "瀹": 28305, "帔": 28306, "bci": 28307, "apos": 28308, "jati": 28309, "whitaker": 28310, "worked": 28311, "feistel": 28312, "azam": 28313, "workbench": 28314, "banco": 28315, "earthbound": 28316, "brownie": 28317, "anja": 28318, "楦": 28319, "guangdong": 28320, "immigrant": 28321, "immigration": 28322, "nationality": 28323, "aunt": 28324, "匦": 28325, "bunt": 28326, "cuor": 28327, "eland": 28328, "codd": 28329, "daro": 28330, "pris": 28331, "yukon": 28332, "astoria": 28333, "redon": 28334, "lustre": 28335, "akira": 28336, "躔": 28337, "ulli": 28338, "purus": 28339, "drawn": 28340, "gaultier": 28341, "cimi": 28342, "lube": 28343, "hauser": 28344, "castillo": 28345, "armas": 28346, "boeuf": 28347, "stile": 28348, "violin": 28349, "luk": 28350, "calling": 28351, "bomba": 28352, "cavalry": 28353, "exotic": 28354, "licence": 28355, "carly": 28356, "senator": 28357, "sill": 28358, "burton": 28359, "hoot": 28360, "mccartney": 28361, "gaius": 28362, "carrollton": 28363, "lyle": 28364, "cabrera": 28365, "foxy": 28366, "johansen": 28367, "甍": 28368, "clf": 28369, "represent": 28370, "catholique": 28371, "dominique": 28372, "stillwater": 28373, "primetime": 28374, "nevada": 28375, "classified": 28376, "ebbe": 28377, "pronunciation": 28378, "ollie": 28379, "reba": 28380, "weinstein": 28381, "溇": 28382, "pivotal": 28383, "nearby": 28384, "airplay": 28385, "caso": 28386, "sutton": 28387, "険": 28388, "matta": 28389, "潲": 28390, "greenfield": 28391, "orville": 28392, "hubbard": 28393, "vasco": 28394, "louvre": 28395, "tzi": 28396, "镡": 28397, "martial": 28398, "camry": 28399, "celica": 28400, "jbl": 28401, "rogue": 28402, "幛": 28403, "maser": 28404, "榣": 28405, "heartbeat": 28406, "neko": 28407, "荭": 28408, "kenta": 28409, "zeal": 28410, "sayuri": 28411, "dolph": 28412, "convair": 28413, "평": 28414, "mamma": 28415, "dynamo": 28416, "鬬": 28417, "yamamoto": 28418, "ț": 28419, "manolo": 28420, "mayflower": 28421, "syrian": 28422, "blockbuster": 28423, "cracker": 28424, "苊": 28425, "heck": 28426, "monza": 28427, "shrimp": 28428, "luminescence": 28429, "鱥": 28430, "knuth": 28431, "♂": 28432, "laszlo": 28433, "daniela": 28434, "villers": 28435, "arce": 28436, "ident": 28437, "haut": 28438, "basse": 28439, "priv": 28440, "eryn": 28441, "literacy": 28442, "melchior": 28443, "pussy": 28444, "jenna": 28445, "negra": 28446, "mumbai": 28447, "ziers": 28448, "vatan": 28449, "mehta": 28450, "roke": 28451, "衪": 28452, "caddy": 28453, "landscape": 28454, "鄎": 28455, "zelda": 28456, "tenta": 28457, "嫳": 28458, "carlsson": 28459, "habana": 28460, "leona": 28461, "vald": 28462, "愦": 28463, "佖": 28464, "rard": 28465, "peregrine": 28466, "♠": 28467, "♣": 28468, "gifford": 28469, "gospel": 28470, "jerusalem": 28471, "honduras": 28472, "atelier": 28473, "mouton": 28474, "blaye": 28475, "partements": 28476, "spector": 28477, "tramp": 28478, "veli": 28479, "braunschweig": 28480, "zama": 28481, "indonesian": 28482, "herschel": 28483, "jsa": 28484, "locker": 28485, "sin": 28486, "damned": 28487, "reeve": 28488, "roadster": 28489, "珷": 28490, "caribbean": 28491, "螣": 28492, "bebop": 28493, "duma": 28494, "ellington": 28495, "rascal": 28496, "bout": 28497, "calcutta": 28498, "courier": 28499, "鄕": 28500, "虿": 28501, "ס": 28502, "brenda": 28503, "selo": 28504, "vannes": 28505, "rhovanion": 28506, "gandalf": 28507, "aragorn": 28508, "ruin": 28509, "saruman": 28510, "挎": 28511, "admission": 28512, "racket": 28513, "zabel": 28514, "kettering": 28515, "touchdown": 28516, "hlen": 28517, "鮻": 28518, "鱚": 28519, "pape": 28520, "eryx": 28521, "climbing": 28522, "erotica": 28523, "bonita": 28524, "tanjong": 28525, "ander": 28526, "cede": 28527, "paix": 28528, "yone": 28529, "ugly": 28530, "debra": 28531, "valar": 28532, "鲳": 28533, "piranha": 28534, "defending": 28535, "corin": 28536, "shon": 28537, "stallion": 28538, "remarkable": 28539, "treble": 28540, "aguilar": 28541, "telekom": 28542, "nsted": 28543, "mbh": 28544, "𬭶": 28545, "瘉": 28546, "regarded": 28547, "lton": 28548, "uei": 28549, "guimar": 28550, "luz": 28551, "borja": 28552, "freddie": 28553, "ogden": 28554, "貍": 28555, "coeur": 28556, "minix": 28557, "terni": 28558, "pontifical": 28559, "argues": 28560, "beethoven": 28561, "rakyat": 28562, "taggart": 28563, "swear": 28564, "瑱": 28565, "olivet": 28566, "spectator": 28567, "slackware": 28568, "frodo": 28569, "loaded": 28570, "larue": 28571, "flick": 28572, "kicker": 28573, "priscilla": 28574, "entropy": 28575, "祃": 28576, "嵖": 28577, "岈": 28578, "拝": 28579, "palme": 28580, "礑": 28581, "douro": 28582, "furl": 28583, "midori": 28584, "唷": 28585, "bocage": 28586, "salman": 28587, "carteret": 28588, "㵲": 28589, "垟": 28590, "csiro": 28591, "burley": 28592, "bouvier": 28593, "jester": 28594, "kruger": 28595, "cento": 28596, "𫚔": 28597, "plea": 28598, "涠": 28599, "terran": 28600, "gili": 28601, "dinner": 28602, "marten": 28603, "dirac": 28604, "linde": 28605, "nowrap": 28606, "∴": 28607, "amit": 28608, "beginner": 28609, "penalty": 28610, "tver": 28611, "musician": 28612, "狝": 28613, "tractor": 28614, "orcs": 28615, "鳓": 28616, "hyperion": 28617, "sleepy": 28618, "maier": 28619, "carole": 28620, "fira": 28621, "juda": 28622, "capable": 28623, "himself": 28624, "mansur": 28625, "hedy": 28626, "disciple": 28627, "ggen": 28628, "拺": 28629, "shipyard": 28630, "handheld": 28631, "brou": 28632, "daal": 28633, "hayward": 28634, "溵": 28635, "fips": 28636, "tuned": 28637, "paladin": 28638, "蕡": 28639, "wiiware": 28640, "groovy": 28641, "sich": 28642, "孬": 28643, "noyes": 28644, "monsoon": 28645, "lucasarts": 28646, "kain": 28647, "projekt": 28648, "anguilla": 28649, "agama": 28650, "kitab": 28651, "bahr": 28652, "缍": 28653, "mariam": 28654, "khl": 28655, "brody": 28656, "varna": 28657, "罸": 28658, "syria": 28659, "haim": 28660, "uganda": 28661, "inquiry": 28662, "berber": 28663, "alor": 28664, "bibliothek": 28665, "otherwise": 28666, "rajendra": 28667, "romer": 28668, "hutt": 28669, "granite": 28670, "jameson": 28671, "parnell": 28672, "plugin": 28673, "veneta": 28674, "burberry": 28675, "қ": 28676, "ҡ": 28677, "ң": 28678, "puget": 28679, "makin": 28680, "deacon": 28681, "murdock": 28682, "quang": 28683, "ỏ": 28684, "bingham": 28685, "nuovo": 28686, "giuliano": 28687, "柃": 28688, "lsw": 28689, "acura": 28690, "irony": 28691, "vem": 28692, "exhaust": 28693, "kgf": 28694, "yaris": 28695, "gorilla": 28696, "awake": 28697, "亊": 28698, "gbit": 28699, "qing": 28700, "ankara": 28701, "roderick": 28702, "honorary": 28703, "輶": 28704, "truss": 28705, "moussa": 28706, "reel": 28707, "plac": 28708, "nimrod": 28709, "𪿫": 28710, "ock": 28711, "contained": 28712, "rdbms": 28713, "django": 28714, "strato": 28715, "sybil": 28716, "patan": 28717, "cottage": 28718, "cronos": 28719, "sochi": 28720, "mold": 28721, "裿": 28722, "beckett": 28723, "almanac": 28724, "stratus": 28725, "bohemian": 28726, "bonnet": 28727, "balin": 28728, "蒹": 28729, "glamorous": 28730, "耄": 28731, "耋": 28732, "magdalen": 28733, "winton": 28734, "collision": 28735, "infiniti": 28736, "continued": 28737, "fender": 28738, "equipped": 28739, "editorial": 28740, "reflect": 28741, "replaced": 28742, "connolly": 28743, "ttingen": 28744, "gorgon": 28745, "mugu": 28746, "dulce": 28747, "ripon": 28748, "ℝ": 28749, "divina": 28750, "meditation": 28751, "recruit": 28752, "wilfried": 28753, "marius": 28754, "brood": 28755, "pumpkin": 28756, "jib": 28757, "prunus": 28758, "ayumi": 28759, "hamasaki": 28760, "inspire": 28761, "glitter": 28762, "㚻": 28763, "redone": 28764, "ｰ": 28765, "wissenschaften": 28766, "跂": 28767, "reagan": 28768, "薹": 28769, "ㄘ": 28770, "crocker": 28771, "ſ": 28772, "imov": 28773, "冧": 28774, "roos": 28775, "exeter": 28776, "asphalt": 28777, "trud": 28778, "mancini": 28779, "憙": 28780, "naut": 28781, "stadio": 28782, "rime": 28783, "valent": 28784, "twig": 28785, "cauchy": 28786, "gravitational": 28787, "flq": 28788, "ү": 28789, "davi": 28790, "stosur": 28791, "urdu": 28792, "aang": 28793, "spelling": 28794, "awal": 28795, "treatise": 28796, "aami": 28797, "pfaff": 28798, "bcf": 28799, "blonde": 28800, "adl": 28801, "catalan": 28802, "publisher": 28803, "moselle": 28804, "tournay": 28805, "improved": 28806, "shandong": 28807, "irda": 28808, "♡": 28809, "agcl": 28810, "vishnu": 28811, "dhaka": 28812, "kalpa": 28813, "lombardi": 28814, "popper": 28815, "hatton": 28816, "黁": 28817, "燝": 28818, "酺": 28819, "崀": 28820, "animus": 28821, "hainan": 28822, "kbc": 28823, "衒": 28824, "markham": 28825, "spinning": 28826, "tennant": 28827, "蛏": 28828, "𩾃": 28829, "ziegler": 28830, "vesta": 28831, "cornelia": 28832, "roku": 28833, "lapis": 28834, "羂": 28835, "riaa": 28836, "alva": 28837, "redi": 28838, "lbf": 28839, "paine": 28840, "ezio": 28841, "shrink": 28842, "jurong": 28843, "luminous": 28844, "sault": 28845, "trou": 28846, "鯵": 28847, "ragnar": 28848, "esteban": 28849, "osma": 28850, "soy": 28851, "donnie": 28852, "cortana": 28853, "warsaw": 28854, "bryson": 28855, "clarendon": 28856, "keb": 28857, "utd": 28858, "전": 28859, "월": 28860, "ariel": 28861, "buff": 28862, "greenville": 28863, "nantes": 28864, "absent": 28865, "pedia": 28866, "bate": 28867, "romanov": 28868, "overture": 28869, "jacket": 28870, "claro": 28871, "montanus": 28872, "akka": 28873, "ssem": 28874, "pflp": 28875, "walid": 28876, "ishi": 28877, "aou": 28878, "espresso": 28879, "climax": 28880, "bvi": 28881, "siphon": 28882, "emmet": 28883, "跞": 28884, "publish": 28885, "chlorella": 28886, "tod": 28887, "typo": 28888, "rosidae": 28889, "黡": 28890, "easter": 28891, "nola": 28892, "cull": 28893, "榅": 28894, "桲": 28895, "nva": 28896, "varia": 28897, "ང": 28898, "napster": 28899, "limewire": 28900, "schwarzenberg": 28901, "aller": 28902, "heide": 28903, "passer": 28904, "stoner": 28905, "sergeant": 28906, "afghanistan": 28907, "alang": 28908, "sherry": 28909, "琄": 28910, "hospitality": 28911, "miyu": 28912, "窓": 28913, "casta": 28914, "foc": 28915, "prodigy": 28916, "smiley": 28917, "belinda": 28918, "pujols": 28919, "byrd": 28920, "mccann": 28921, "natha": 28922, "volcano": 28923, "nihil": 28924, "nuku": 28925, "nonce": 28926, "fragilis": 28927, "mancha": 28928, "muck": 28929, "ched": 28930, "悊": 28931, "锫": 28932, "telmex": 28933, "luma": 28934, "piracy": 28935, "windy": 28936, "corbett": 28937, "蒡": 28938, "眛": 28939, "嫪": 28940, "毐": 28941, "圧": 28942, "더": 28943, "wannabe": 28944, "bulldozer": 28945, "nehalem": 28946, "aligned": 28947, "spica": 28948, "budapest": 28949, "deneb": 28950, "discussion": 28951, "givenchy": 28952, "卲": 28953, "theophilus": 28954, "jakub": 28955, "scribe": 28956, "њ": 28957, "mainz": 28958, "snes": 28959, "hepburn": 28960, "afghan": 28961, "whig": 28962, "𥐟": 28963, "invitational": 28964, "grubbs": 28965, "shaka": 28966, "compositor": 28967, "holocaust": 28968, "wonderswan": 28969, "interact": 28970, "bucket": 28971, "twisted": 28972, "방": 28973, "lucent": 28974, "counsel": 28975, "darby": 28976, "nuno": 28977, "clearing": 28978, "grafton": 28979, "rockingham": 28980, "demonstrated": 28981, "encoder": 28982, "outdoor": 28983, "isuzu": 28984, "suru": 28985, "italic": 28986, "scherzer": 28987, "instrumentation": 28988, "wmi": 28989, "interrupt": 28990, "masjid": 28991, "tanjung": 28992, "esplanade": 28993, "dewan": 28994, "created": 28995, "prepare": 28996, "indicus": 28997, "葇": 28998, "kirsten": 28999, "tintin": 29000, "etna": 29001, "congratulation": 29002, "麇": 29003, "旵": 29004, "substitution": 29005, "complexity": 29006, "㫻": 29007, "泩": 29008, "พ": 29009, "畯": 29010, "hebrew": 29011, "subversion": 29012, "akbar": 29013, "糌": 29014, "rojo": 29015, "𪢠": 29016, "貅": 29017, "accreditation": 29018, "匂": 29019, "dipl": 29020, "cnrs": 29021, "manila": 29022, "massacre": 29023, "譲": 29024, "獬": 29025, "arista": 29026, "limbo": 29027, "秾": 29028, "庲": 29029, "rolex": 29030, "虓": 29031, "puppy": 29032, "engadget": 29033, "gizmo": 29034, "bungie": 29035, "installation": 29036, "nazca": 29037, "瑆": 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29098, "rourke": 29099, "manny": 29100, "guadalupe": 29101, "tentacle": 29102, "throttle": 29103, "scum": 29104, "gamers": 29105, "ㅂ": 29106, "tattoo": 29107, "orne": 29108, "cochin": 29109, "bjorn": 29110, "hoya": 29111, "chinon": 29112, "greeting": 29113, "longhorn": 29114, "blackcomb": 29115, "statcounter": 29116, "zdnet": 29117, "technet": 29118, "sandbox": 29119, "kiedis": 29120, "hillel": 29121, "slovak": 29122, "mayhem": 29123, "fairfax": 29124, "oden": 29125, "kamo": 29126, "turkish": 29127, "ấ": 29128, "uttar": 29129, "mya": 29130, "bodhi": 29131, "roca": 29132, "ochs": 29133, "docker": 29134, "shao": 29135, "shiny": 29136, "피": 29137, "gareth": 29138, "acker": 29139, "馕": 29140, "sancho": 29141, "designation": 29142, "navigator": 29143, "蒾": 29144, "ashford": 29145, "葎": 29146, "dolores": 29147, "faithful": 29148, "khaled": 29149, "irian": 29150, "caleb": 29151, "marissa": 29152, "carp": 29153, "morro": 29154, "aerosmith": 29155, "梾": 29156, "怱": 29157, "svetlana": 29158, 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"erebor": 29526, "upchurch": 29527, "rohirrim": 29528, "tsar": 29529, "auxi": 29530, "torture": 29531, "macedonia": 29532, "luang": 29533, "baggins": 29534, "nazg": 29535, "mordor": 29536, "beleriand": 29537, "valinor": 29538, "kure": 29539, "isengard": 29540, "gamgee": 29541, "maia": 29542, "ཀ": 29543, "bbi": 29544, "gls": 29545, "ffc": 29546, "baume": 29547, "zquez": 29548, "𪾢": 29549, "lothl": 29550, "amroth": 29551, "smaug": 29552, "ered": 29553, "barad": 29554, "bilbo": 29555, "greenwich": 29556, "szent": 29557, "biblical": 29558, "caball": 29559, "blanco": 29560, "convent": 29561, "galad": 29562, "attendant": 29563, "fff": 29564, "somme": 29565, "petrol": 29566, "ameba": 29567, "颥": 29568, "dhani": 29569, "parlophone": 29570, "reject": 29571, "emporio": 29572, "xli": 29573, "puteri": 29574, "razak": 29575, "adnan": 29576, "gerry": 29577, "oeste": 29578, "naturelle": 29579, "鳁": 29580, "germain": 29581, "sundance": 29582, "脳": 29583, "strow": 29584, "invincible": 29585, "mbuti": 29586, "argonne": 29587, "hungary": 29588, "suffolk": 29589, "eugenio": 29590, "pano": 29591, "rhombus": 29592, "髷": 29593, "pentagon": 29594, "sorbonne": 29595, "resp": 29596, "chemin": 29597, "twain": 29598, "laize": 29599, "flinders": 29600, "alger": 29601, "himalaya": 29602, "嫫": 29603, "pallid": 29604, "marge": 29605, "cleaner": 29606, "christy": 29607, "juliet": 29608, "kashi": 29609, "showgirl": 29610, "catwalk": 29611, "ˑ": 29612, "showroom": 29613, "발": 29614, "안": 29615, "술": 29616, "godaddy": 29617, "heckler": 29618, "ibiza": 29619, "artyom": 29620, "lydekker": 29621, "seeley": 29622, "spyker": 29623, "patrese": 29624, "qualify": 29625, "achieve": 29626, "resulting": 29627, "scoring": 29628, "hungarian": 29629, "mansell": 29630, "finishing": 29631, "consecutive": 29632, "foca": 29633, "wraith": 29634, "bajo": 29635, "militia": 29636, "gaya": 29637, "ੰ": 29638, "vieux": 29639, "ภ": 29640, "ู": 29641, "ข": 29642, "mew": 29643, "shai": 29644, "furniture": 29645, "drosera": 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"gsn": 29708, "siddeley": 29709, "rossa": 29710, "anse": 29711, "lassi": 29712, "servo": 29713, "imai": 29714, "strat": 29715, "poincar": 29716, "misr": 29717, "exposition": 29718, "newsweek": 29719, "habitat": 29720, "kaist": 29721, "furious": 29722, "madame": 29723, "dedicated": 29724, "晙": 29725, "漷": 29726, "valdemar": 29727, "jacobus": 29728, "്": 29729, "baraka": 29730, "nieuwe": 29731, "suny": 29732, "nii": 29733, "ikh": 29734, "滽": 29735, "juri": 29736, "citar": 29737, "meyers": 29738, "yummy": 29739, "blooming": 29740, "鸷": 29741, "霦": 29742, "christus": 29743, "bayon": 29744, "jord": 29745, "chesterfield": 29746, "folsom": 29747, "lucifer": 29748, "uday": 29749, "screw": 29750, "templeton": 29751, "babcock": 29752, "deity": 29753, "growl": 29754, "zappa": 29755, "𫁡": 29756, "鵙": 29757, "refused": 29758, "bronze": 29759, "kristin": 29760, "gail": 29761, "fray": 29762, "vicki": 29763, "cuk": 29764, "sharpless": 29765, "calorie": 29766, "mullin": 29767, "淛": 29768, "鐤": 29769, "侹": 29770, "wehrmacht": 29771, "sahib": 29772, "nuf": 29773, "banner": 29774, "fernandez": 29775, "caliber": 29776, "encarta": 29777, "yost": 29778, "airman": 29779, "brigadier": 29780, "frits": 29781, "hepta": 29782, "설": 29783, "maemo": 29784, "yyyy": 29785, "scripting": 29786, "httpd": 29787, "umber": 29788, "keaton": 29789, "melton": 29790, "坧": 29791, "nestor": 29792, "copyleft": 29793, "eccles": 29794, "scripture": 29795, "xanadu": 29796, "esperanto": 29797, "sont": 29798, "ன": 29799, "xana": 29800, "sano": 29801, "nanta": 29802, "loose": 29803, "mithra": 29804, "hashim": 29805, "agia": 29806, "坫": 29807, "durand": 29808, "salah": 29809, "slush": 29810, "鬰": 29811, "soar": 29812, "operculum": 29813, "bagh": 29814, "ganga": 29815, "sixteen": 29816, "拑": 29817, "colby": 29818, "timbaland": 29819, "bronx": 29820, "riki": 29821, "珒": 29822, "smu": 29823, "reinhold": 29824, "啷": 29825, "ember": 29826, "cqb": 29827, "하": 29828, "marionette": 29829, "pup": 29830, "terrell": 29831, "darrell": 29832, "speedy": 29833, "orochi": 29834, "piccolo": 29835, "fife": 29836, "kanan": 29837, "supa": 29838, "rootkit": 29839, "okay": 29840, "moira": 29841, "halloween": 29842, "mentioned": 29843, "remove": 29844, "regarding": 29845, "nemegt": 29846, "rizal": 29847, "枬": 29848, "fateh": 29849, "虨": 29850, "rinus": 29851, "stacy": 29852, "mover": 29853, "価": 29854, "tutt": 29855, "mercator": 29856, "부": 29857, "憼": 29858, "strath": 29859, "khw": 29860, "撖": 29861, "ople": 29862, "tariq": 29863, "epworth": 29864, "locust": 29865, "conseil": 29866, "dg": 29867, "mckee": 29868, "parted": 29869, "leger": 29870, "panj": 29871, "禔": 29872, "rence": 29873, "leia": 29874, "cobol": 29875, "婠": 29876, "ksg": 29877, "tatiana": 29878, "callahan": 29879, "sate": 29880, "lindberg": 29881, "aust": 29882, "蕚": 29883, "motel": 29884, "lunch": 29885, "differ": 29886, "alisa": 29887, "koper": 29888, "songwriter": 29889, "expected": 29890, "suggested": 29891, "composed": 29892, "stated": 29893, 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29954, "shish": 29955, "italiano": 29956, "tue": 29957, "almaz": 29958, "rubik": 29959, "bamford": 29960, "phish": 29961, "deportiva": 29962, "ndes": 29963, "yehuda": 29964, "문": 29965, "primera": 29966, "divisi": 29967, "skytrain": 29968, "fag": 29969, "磻": 29970, "oba": 29971, "鲿": 29972, "鮠": 29973, "胂": 29974, "uchida": 29975, "ukraine": 29976, "liturgy": 29977, "balrog": 29978, "galadriel": 29979, "애": 29980, "그": 29981, "syndicat": 29982, "chatterjee": 29983, "kirkpatrick": 29984, "imt": 29985, "humbert": 29986, "ronne": 29987, "貒": 29988, "shuv": 29989, "鳚": 29990, "ɵ": 29991, "collider": 29992, "neos": 29993, "fuzz": 29994, "bhaga": 29995, "塆": 29996, "herrera": 29997, "osvaldo": 29998, "belarus": 29999, "balkan": 30000, "cbb": 30001, "sanj": 30002, "hispania": 30003, "abingdon": 30004, "wolverhampton": 30005, "excelsior": 30006, "enfield": 30007, "tmz": 30008, "vengeance": 30009, "sumatra": 30010, "kowal": 30011, "decided": 30012, "魣": 30013, "kye": 30014, "aude": 30015, 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30077, "niven": 30078, "khazad": 30079, "macgregor": 30080, "caster": 30081, "vienne": 30082, "naja": 30083, "ignaz": 30084, "娍": 30085, "¡": 30086, "revoluci": 30087, "dela": 30088, "jacopo": 30089, "duomo": 30090, "琡": 30091, "marek": 30092, "騨": 30093, "𩽾": 30094, "mansour": 30095, "puerta": 30096, "seki": 30097, "sweeney": 30098, "easton": 30099, "gettin": 30100, "獏": 30101, "idw": 30102, "directional": 30103, "serenity": 30104, "lifer": 30105, "palacio": 30106, "oliva": 30107, "menzies": 30108, "earle": 30109, "malvern": 30110, "peasant": 30111, "yoda": 30112, "rcd": 30113, "磝": 30114, "scalable": 30115, "tomcat": 30116, "rquez": 30117, "晡": 30118, "pipa": 30119, "镴": 30120, "gamepad": 30121, "beggar": 30122, "hershey": 30123, "talon": 30124, "recipe": 30125, "lard": 30126, "lisieux": 30127, "karat": 30128, "pamiers": 30129, "digne": 30130, "니": 30131, "hamas": 30132, "operate": 30133, "wrestlemania": 30134, "lesnar": 30135, "parse": 30136, "eica": 30137, "dorset": 30138, "bosworth": 30139, "qnx": 30140, "magister": 30141, "osb": 30142, "follows": 30143, "urm": 30144, "悧": 30145, "klass": 30146, "颣": 30147, "kirara": 30148, "esslingen": 30149, "repository": 30150, "pdm": 30151, "restful": 30152, "xuan": 30153, "borneo": 30154, "paka": 30155, "aviv": 30156, "ccg": 30157, "nsc": 30158, "automaton": 30159, "laye": 30160, "缌": 30161, "conmebol": 30162, "spent": 30163, "দ": 30164, "ু": 30165, "র": 30166, "shout": 30167, "claudius": 30168, "mcnally": 30169, "telepathy": 30170, "paxton": 30171, "akim": 30172, "ruyter": 30173, "yea": 30174, "nafta": 30175, "privilege": 30176, "cubic": 30177, "daud": 30178, "屄": 30179, "clap": 30180, "゜": 30181, "鬈": 30182, "xsl": 30183, "氪": 30184, "weasel": 30185, "dingo": 30186, "峃": 30187, "arcy": 30188, "bieber": 30189, "mastering": 30190, "purity": 30191, "hawaiian": 30192, "danish": 30193, "indicated": 30194, "刿": 30195, "
": 30196, "趵": 30197, "tvi": 30198, "yap": 30199, "neuilly": 30200, "mlia": 30201, "senegal": 30202, "overlord": 30203, "trujillo": 30204, "碡": 30205, "kimmel": 30206, "fille": 30207, "giment": 30208, "mortgage": 30209, "scipio": 30210, "juba": 30211, "褕": 30212, "mandrake": 30213, "ghana": 30214, "letzte": 30215, "returning": 30216, "initialize": 30217, "𫄧": 30218, "镤": 30219, "咻": 30220, "辒": 30221, "砮": 30222, "tomasz": 30223, "catharina": 30224, "populaire": 30225, "seer": 30226, "cabal": 30227, "grosse": 30228, "鄀": 30229, "grunge": 30230, "feuer": 30231, "淠": 30232, "hidalgo": 30233, "勔": 30234, "blowing": 30235, "dynamite": 30236, "caste": 30237, "skim": 30238, "ŏ": 30239, "cappella": 30240, "∠": 30241, "spend": 30242, "yass": 30243, "felicity": 30244, "gegen": 30245, "halsey": 30246, "edmonton": 30247, "rotu": 30248, "cinco": 30249, "prieto": 30250, "oculus": 30251, "fsi": 30252, "agassiz": 30253, "hansa": 30254, "arion": 30255, "turnbull": 30256, "kristina": 30257, "newt": 30258, "嘭": 30259, "norodom": 30260, "buckland": 30261, "nhm": 30262, "ibis": 30263, "byu": 30264, "vertebrate": 30265, "paleontology": 30266, "clade": 30267, "gish": 30268, "refer": 30269, "nicolae": 30270, "bmk": 30271, "sueur": 30272, "합": 30273, "satellaview": 30274, "rete": 30275, "nizam": 30276, "murshidabad": 30277, "miran": 30278, "ื": 30279, "樛": 30280, "媖": 30281, "meck": 30282, "aib": 30283, "unable": 30284, "jekyll": 30285, "nigeria": 30286, "calibre": 30287, "স": 30288, "জ": 30289, "ী": 30290, "য": 30291, "়": 30292, "convoy": 30293, "salve": 30294, "burne": 30295, "gupta": 30296, "kush": 30297, "ở": 30298, "soundscan": 30299, "nels": 30300, "bade": 30301, "popmatters": 30302, "erlewine": 30303, "isola": 30304, "magus": 30305, "cliffe": 30306, "摂": 30307, "碶": 30308, "avalanche": 30309, "蟥": 30310, "jesper": 30311, "daigo": 30312, "shelton": 30313, "denmark": 30314, "maharaja": 30315, "竩": 30316, "轱": 30317, "曁": 30318, "rebirth": 30319, "taoyuan": 30320, "occurred": 30321, "apogee": 30322, "benito": 30323, "clash": 30324, "鲡": 30325, "鯙": 30326, "stax": 30327, "doubs": 30328, "fonds": 30329, "mnf": 30330, "缂": 30331, "aumont": 30332, "moutier": 30333, "rental": 30334, "skew": 30335, "destroyed": 30336, "mascis": 30337, "rewrite": 30338, "harmonia": 30339, "magica": 30340, "sultanate": 30341, "trivium": 30342, "francine": 30343, "slog": 30344, "pinch": 30345, "fauna": 30346, "waal": 30347, "compile": 30348, "sunbird": 30349, "ٹ": 30350, "mccormick": 30351, "dublin": 30352, "fuhr": 30353, "captor": 30354, "曺": 30355, "bellini": 30356, "almost": 30357, "lynd": 30358, "honest": 30359, "videogame": 30360, "bearer": 30361, "horr": 30362, "kipling": 30363, "eim": 30364, "gald": 30365, "celebr": 30366, "noam": 30367, "chomsky": 30368, "speedster": 30369, "rachael": 30370, "lief": 30371, "quenya": 30372, "elemental": 30373, "fishbase": 30374, "‍": 30375, "jasmin": 30376, "oleo": 30377, "marcin": 30378, "carmelo": 30379, "鲬": 30380, "scad": 30381, "appletalk": 30382, "dmca": 30383, "bsod": 30384, "associazione": 30385, "attila": 30386, "oust": 30387, "charlton": 30388, "streamer": 30389, "amsa": 30390, "sterna": 30391, "hbr": 30392, "蒄": 30393, "bex": 30394, "焴": 30395, "quinta": 30396, "lizzy": 30397, "manchu": 30398, "frankenstein": 30399, "montero": 30400, "masc": 30401, "amerika": 30402, "玹": 30403, "tortoise": 30404, "ikki": 30405, "髢": 30406, "거": 30407, "izumi": 30408, "민": 30409, "운": 30410, "gans": 30411, "弨": 30412, "檠": 30413, "⊙": 30414, "improve": 30415, "realize": 30416, "relating": 30417, "stratton": 30418, "bonanza": 30419, "抿": 30420, "sadistic": 30421, "esperanza": 30422, "𩷶": 30423, "isca": 30424, "luisa": 30425, "bihar": 30426, "unkel": 30427, "smtown": 30428, "donghae": 30429, "eunhyuk": 30430, "鴈": 30431, "랑": 30432, "yeon": 30433, "馺": 30434, "讬": 30435, "shorter": 30436, "castellan": 30437, "communiste": 30438, "publicain": 30439, "𨱑": 30440, "phyllis": 30441, "谖": 30442, "misery": 30443, "matador": 30444, "̆": 30445, "broadcaster": 30446, "pippin": 30447, "հ": 30448, "ringing": 30449, "jimi": 30450, "ftse": 30451, "蓐": 30452, "peck": 30453, "sjk": 30454, "籾": 30455, "〒": 30456, "router": 30457, "sadie": 30458, "櫂": 30459, "rtsp": 30460, "rdt": 30461, "booster": 30462, "seong": 30463, "autoroute": 30464, "tiberius": 30465, "mchale": 30466, "sekolah": 30467, "menengah": 30468, "esf": 30469, "祘": 30470, "angkor": 30471, "musume": 30472, "maung": 30473, "basilique": 30474, "ĕ": 30475, "hohenlohe": 30476, "ohci": 30477, "agnan": 30478, "nove": 30479, "eest": 30480, "琲": 30481, "sandro": 30482, "嘥": 30483, "faraday": 30484, "tuple": 30485, "fdl": 30486, "sentry": 30487, "hast": 30488, "汸": 30489, "serre": 30490, "jaco": 30491, "蜣": 30492, "georgian": 30493, "antara": 30494, "brunei": 30495, "macaulay": 30496, "aldar": 30497, "guetta": 30498, "jla": 30499, "氕": 30500, "হ": 30501, "ল": 30502, "আ": 30503, "ম": 30504, "proceed": 30505, "鵎": 30506, "鵼": 30507, "lath": 30508, "joh": 30509, "jovi": 30510, "艛": 30511, "瑨": 30512, "瞫": 30513, "malka": 30514, "fxx": 30515, "stingray": 30516, "atsushi": 30517, "ো": 30518, "প": 30519, "ট": 30520, "hille": 30521, "jeon": 30522, "partito": 30523, "lilith": 30524, "topology": 30525, "빈": 30526, "煕": 30527, "registrar": 30528, "ratti": 30529, "verdi": 30530, "lapa": 30531, "𫍲": 30532, "staffordshire": 30533, "蕖": 30534, "tabl": 30535, "gluck": 30536, "walcott": 30537, "predator": 30538, "ripley": 30539, "gunter": 30540, "hannes": 30541, "allee": 30542, "rives": 30543, "nautical": 30544, "덕": 30545, "ủ": 30546, "舳": 30547, "粦": 30548, "huon": 30549, "추": 30550, "aldin": 30551, "firma": 30552, "piercing": 30553, "俌": 30554, "晛": 30555, "wtf": 30556, "postman": 30557, "johnnie": 30558, "addict": 30559, "penney": 30560, "bourges": 30561, "hsieh": 30562, "swain": 30563, "sorus": 30564, "panty": 30565, "garb": 30566, "llosa": 30567, "robust": 30568, "shqip": 30569, "rihanna": 30570, "latham": 30571, "malabar": 30572, "rampur": 30573, "amarillo": 30574, "♦": 30575, "pono": 30576, "ถ": 30577, "literal": 30578, "snmp": 30579, "zedd": 30580, "naoto": 30581, "burial": 30582, "movia": 30583, "defeating": 30584, "tripura": 30585, "akg": 30586, "apart": 30587, "adagio": 30588, "讐": 30589, "waterfall": 30590, "andar": 30591, "alwa": 30592, "kaman": 30593, "kmno": 30594, "猱": 30595, "asni": 30596, "daze": 30597, "eez": 30598, "femme": 30599, "harb": 30600, "assembler": 30601, "meester": 30602, "lissa": 30603, "厷": 30604, "whitworth": 30605, "adrienne": 30606, "barret": 30607, "genevieve": 30608, "dunk": 30609, "gonzales": 30610, "seda": 30611, "hapoel": 30612, "jazeera": 30613, "fearless": 30614, "autobot": 30615, "mullen": 30616, "param": 30617, "halifax": 30618, "marsan": 30619, "contractor": 30620, "uruguay": 30621, "屭": 30622, "andrzej": 30623, "tariff": 30624, "ianto": 30625, "cadre": 30626, "vichy": 30627, "banu": 30628, "expr": 30629, "preto": 30630, "hosting": 30631, "shelf": 30632, "sesame": 30633, "mobo": 30634, "awl": 30635, "beru": 30636, "hummel": 30637, "kuroda": 30638, "sumo": 30639, "嘀": 30640, "laing": 30641, "grimm": 30642, "alca": 30643, "layla": 30644, "petition": 30645, "씨": 30646, "송": 30647, "ਾ": 30648, "brine": 30649, "boxer": 30650, "crisp": 30651, "瓉": 30652, "蓚": 30653, "梂": 30654, "buyer": 30655, "lasso": 30656, "submission": 30657, "russo": 30658, "greer": 30659, "wilber": 30660, "quimper": 30661, "賔": 30662, "bounded": 30663, "lorca": 30664, "svn": 30665, "doling": 30666, "mongolia": 30667, "allowing": 30668, "occur": 30669, "therefore": 30670, "suggests": 30671, "jquery": 30672, "̣": 30673, "goya": 30674, "dwayne": 30675, "唹": 30676, "jesuit": 30677, "mealy": 30678, "lumpur": 30679, "내": 30680, "까": 30681, "파": 30682, "tinker": 30683, "colegio": 30684, "hispanic": 30685, "aiff": 30686, "watan": 30687, "partridge": 30688, "comcast": 30689, "錱": 30690, "myki": 30691, "ruling": 30692, "burzum": 30693, "corporal": 30694, "hamiltonian": 30695, "kola": 30696, "socialiste": 30697, "철": 30698, "wist": 30699, "cabaret": 30700, "면": 30701, "houten": 30702, "suzy": 30703, "broker": 30704, "禨": 30705, "marlin": 30706, "ferrand": 30707, "polka": 30708, "ammunition": 30709, "hangar": 30710, "humble": 30711, "schweizer": 30712, "logue": 30713, "soulja": 30714, "ranc": 30715, "姹": 30716, "meijer": 30717, "omx": 30718, "圌": 30719, "firing": 30720, "mfi": 30721, "mery": 30722, "learner": 30723, "鯻": 30724, "̈": 30725, "theotokos": 30726, "minecraft": 30727, "request": 30728, "searching": 30729, "栂": 30730, "senj": 30731, "ซ": 30732, "jealousy": 30733, "daydream": 30734, "orthodox": 30735, "communion": 30736, "ฟ": 30737, "quota": 30738, "鲯": 30739, "䲟": 30740, "clang": 30741, "虉": 30742, "hould": 30743, "quaid": 30744, "荵": 30745, "崾": 30746, "linkin": 30747, "躄": 30748, "purpurea": 30749, "lask": 30750, "fisa": 30751, "梱": 30752, "骖": 30753, "delivered": 30754, "ranchi": 30755, "repubblica": 30756, "surat": 30757, "boto": 30758, "coppa": 30759, "fante": 30760, "鳒": 30761, "mtf": 30762, "telegram": 30763, "雝": 30764, "mtbf": 30765, "ussr": 30766, "荅": 30767, "petar": 30768, "arabic": 30769, "airdrop": 30770, "苈": 30771, "bathurst": 30772, "wolverine": 30773, "gravel": 30774, "reald": 30775, "require": 30776, "waffen": 30777, "차": 30778, "물": 30779, "girolamo": 30780, "selinux": 30781, "yarra": 30782, "루": 30783, "빛": 30784, "decree": 30785, "wikitable": 30786, "ohne": 30787, "spinner": 30788, "rijndael": 30789, "oceanic": 30790, "dink": 30791, "rlund": 30792, "upload": 30793, "厣": 30794, "todos": 30795, "բ": 30796, "տ": 30797, "origine": 30798, "forester": 30799, "getty": 30800, "hopeless": 30801, "catalunya": 30802, "湴": 30803, "thrift": 30804, "grimaldi": 30805, "fresnel": 30806, "studied": 30807, "ghaz": 30808, "glastonbury": 30809, "kanye": 30810, "kraftwerk": 30811, "debugger": 30812, "softly": 30813, "carried": 30814, "mcfly": 30815, "nuneaton": 30816, "everest": 30817, "assumption": 30818, "pinocchio": 30819, "cloche": 30820, "즈": 30821, "히": 30822, "트": 30823, "chich": 30824, "protest": 30825, "incredible": 30826, "曱": 30827, "甴": 30828, "堮": 30829, "blanchard": 30830, "dominik": 30831, "magh": 30832, "borisov": 30833, "consideration": 30834, "rooster": 30835, "蟛": 30836, "langon": 30837, "chug": 30838, "busybox": 30839, "ncurses": 30840, "punta": 30841, "uplink": 30842, "daeng": 30843, "lucha": 30844, "hommes": 30845, "sigur": 30846, "gny": 30847, "瀷": 30848, "southeastern": 30849, "lyoko": 30850, "猋": 30851, "墎": 30852, "mixtape": 30853, "naci": 30854, "amid": 30855, "gotham": 30856, "mellifera": 30857, "yanga": 30858, "yomi": 30859, "styx": 30860, "persei": 30861, "finlay": 30862, "啯": 30863, "pve": 30864, "fop": 30865, "minx": 30866, "toru": 30867, "mohr": 30868, "dcd": 30869, "surfer": 30870, "zheng": 30871, "杧": 30872, "deliver": 30873, "twister": 30874, "loen": 30875, "觌": 30876, "guildhall": 30877, "cwc": 30878, "assemblage": 30879, "duan": 30880, "velasco": 30881, "hilt": 30882, "textile": 30883, "guam": 30884, "명": 30885, "piz": 30886, "escher": 30887, "caret": 30888, "difficult": 30889, "turdus": 30890, "phoebus": 30891, "gringo": 30892, "rki": 30893, "quasimodo": 30894, "archimedes": 30895, "hasbro": 30896, "linke": 30897, "synod": 30898, "giscard": 30899, "tuma": 30900, "magdalene": 30901, "沍": 30902, "funimation": 30903, "sentai": 30904, "鮨": 30905, "礅": 30906, "namgyal": 30907, "leuven": 30908, "kaito": 30909, "katrina": 30910, "체": 30911, "柾": 30912, "voa": 30913, "barrington": 30914, "disch": 30915, "ㄬ": 30916, "kilogram": 30917, "coluim": 30918, "nuco": 30919, "developing": 30920, "fgm": 30921, "ལ": 30922, "lucio": 30923, "kouros": 30924, "translate": 30925, "syllable": 30926, "older": 30927, "salamanca": 30928, "fella": 30929, "relevant": 30930, "garuda": 30931, "诐": 30932, "freeform": 30933, "volleyball": 30934, "단": 30935, "旆": 30936, "saying": 30937, "spicy": 30938, "터": 30939, "mojang": 30940, "undead": 30941, "verso": 30942, "remain": 30943, "鲦": 30944, "implement": 30945, "𫛚": 30946, "gopal": 30947, "璮": 30948, "evere": 30949, "crespo": 30950, "烔": 30951, "suan": 30952, "𫞩": 30953, "讉": 30954, "agnieszka": 30955, "shahar": 30956, "wozniacki": 30957, "karolina": 30958, "booklet": 30959, "suffered": 30960, "grigori": 30961, "magnitude": 30962, "recently": 30963, "creates": 30964, "distinct": 30965, "acquire": 30966, "indicate": 30967, "removed": 30968, "approximately": 30969, "tumblr": 30970, "exploring": 30971, "gremlin": 30972, "sponge": 30973, "gerais": 30974, "nouveau": 30975, "ponti": 30976, "yuga": 30977, "kabuki": 30978, "scary": 30979, "刣": 30980, "兪": 30981, "yulia": 30982, "gimme": 30983, "heartbreak": 30984, "blueprint": 30985, "talib": 30986, "destroy": 30987, "pixie": 30988, "eiken": 30989, "remake": 30990, "xxiii": 30991, "cassini": 30992, "unie": 30993, "avil": 30994, "backstreet": 30995, "peet": 30996, "gea": 30997, "yup": 30998, "mikkel": 30999, "zoey": 31000, "獇": 31001, "yano": 31002, "namie": 31003, "버": 31004, "lullaby": 31005, "晪": 31006, "eek": 31007, "鿔": 31008, "comhairle": 31009, "altar": 31010, "hearst": 31011, "wight": 31012, "qsl": 31013, "atx": 31014, "iihf": 31015, "姈": 31016, "misfit": 31017, "quelle": 31018, "岣": 31019, "swr": 31020, "terrence": 31021, "swp": 31022, "tunis": 31023, "fivb": 31024, "vitry": 31025, "cija": 31026, "warte": 31027, "shard": 31028, "ghosh": 31029, "seaside": 31030, "蔊": 31031, "嚢": 31032, "backing": 31033, "dionysius": 31034, "orly": 31035, "禥": 31036, "㬎": 31037, "鯈": 31038, "converted": 31039, "enya": 31040, "jalil": 31041, "kemal": 31042, "reyna": 31043, "禚": 31044, "騋": 31045, "铹": 31046, "izzy": 31047, "curva": 31048, "doge": 31049, "continues": 31050, "posh": 31051, "barrie": 31052, "dispute": 31053, "rieu": 31054, "stratocaster": 31055, "manx": 31056, "clearly": 31057, "changer": 31058, "gemma": 31059, "晄": 31060, "miletus": 31061, "镅": 31062, "rely": 31063, "firearm": 31064, "稉": 31065, "raspberry": 31066, "𩖃": 31067, "cattedra": 31068, "桴": 31069, "placed": 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"徫": 31319, "kdc": 31320, "keating": 31321, "terer": 31322, "皷": 31323, "weave": 31324, "jihad": 31325, "姸": 31326, "荎": 31327, "hazy": 31328, "예": 31329, "copyvio": 31330, "sift": 31331, "muddy": 31332, "𣚅": 31333, "mansa": 31334, "jey": 31335, "彛": 31336, "calico": 31337, "teaser": 31338, "blume": 31339, "scheduled": 31340, "䱻": 31341, "讙": 31342, "魮": 31343, "陁": 31344, "judi": 31345, "傃": 31346, "敩": 31347, "kiara": 31348, "leessang": 31349, "chunsoft": 31350, "descriptor": 31351, "efb": 31352, "coloring": 31353, "tartan": 31354, "austronesian": 31355, "箦": 31356, "thallus": 31357, "foix": 31358, "ᠨ": 31359, "escapist": 31360, "糀": 31361, "toshi": 31362, "attend": 31363, "darr": 31364, "kbr": 31365, "hock": 31366, "larval": 31367, "pass": 31368, "䁐": 31369, "bangladesh": 31370, "scorpio": 31371, "binh": 31372, "myanmar": 31373, "upazila": 31374, "vocabulary": 31375, "위": 31376, "bixby": 31377, "banga": 31378, "𤏸": 31379, "fuchsia": 31380, "seung": 31381, "없": 31382, "심": 31383, "₹": 31384, "rigueux": 31385, "ḗ": 31386, "chira": 31387, "pmid": 31388, "筩": 31389, "bahnhof": 31390, "altes": 31391, "aloe": 31392, "熈": 31393, "affected": 31394, "sooner": 31395, "helped": 31396, "semifinal": 31397, "finalist": 31398, "upcoming": 31399, "ླ": 31400, "kosovo": 31401, "thiers": 31402, "guas": 31403, "adopted": 31404, "moonstone": 31405, "initially": 31406, "헌": 31407, "昖": 31408, "mandi": 31409, "령": 31410, "saas": 31411, "joined": 31412, "providing": 31413, "harmonix": 31414, "侘": 31415, "duce": 31416, "primal": 31417, "ⓒ": 31418, "迆": 31419, "inscription": 31420, "sangha": 31421, "asana": 31422, "rejected": 31423, "tuc": 31424, "љ": 31425, "kummer": 31426, "manage": 31427, "紑": 31428, "憻": 31429, "詥": 31430, "lerm": 31431, "褞": 31432, "duy": 31433, "ﾀ": 31434, "ʑ": 31435, "̩": 31436, "yuyu": 31437, "vania": 31438, "wile": 31439, "鮈": 31440, "hanley": 31441, "yuka": 31442, "cardboard": 31443, "wetland": 31444, "̌": 31445, "螠": 31446, "fcw": 31447, "厳": 31448, "tatsu": 31449, "chiesa": 31450, "敭": 31451, "bourse": 31452, "‪": 31453, "faiz": 31454, "hakuna": 31455, "matata": 31456, "褧": 31457, "marxist": 31458, "들": 31459, "tunku": 31460, "andrej": 31461, "nematode": 31462, "typically": 31463, "rounded": 31464, "snatch": 31465, "kauffman": 31466, "sarlat": 31467, "cdtv": 31468, "㭽": 31469, "goosebump": 31470, "coru": 31471, "瑅": 31472, "riser": 31473, "洨": 31474, "exoplanet": 31475, "bushi": 31476, "thionville": 31477, "significantly": 31478, "decrease": 31479, "seeya": 31480, "luster": 31481, "calculated": 31482, "allows": 31483, "eyesight": 31484, "＇": 31485, "洤": 31486, "calvados": 31487, "falaise": 31488, "incorporates": 31489, "lineup": 31490, "ự": 31491, "挻": 31492, "namak": 31493, "ahli": 31494, "𣰋": 31495, "jail": 31496, "eats": 31497, "hester": 31498, "paseo": 31499, "arent": 31500, "nanjing": 31501, "indeed": 31502, "madan": 31503, "repackage": 31504, "brahim": 31505, "colonna": 31506, "玶": 31507, "禘": 31508, "seulles": 31509, "prosecutor": 31510, "iplayer": 31511, "hanteo": 31512, "encrypt": 31513, "병": 31514, "mundus": 31515, "lothar": 31516, "saud": 31517, "tfa": 31518, "때": 31519, "gugu": 31520, "wildfire": 31521, "gump": 31522, "commit": 31523, "malva": 31524, "kubuntu": 31525, "mauve": 31526, "fsu": 31527, "lollapalooza": 31528, "pkk": 31529, "bitcoin": 31530, "fairbank": 31531, "蠼": 31532, "螋": 31533, "铗": 31534, "stolen": 31535, "aimed": 31536, "contiki": 31537, "costume": 31538, "榼": 31539, "bookstore": 31540, "kaos": 31541, "婞": 31542, "smackdown": 31543, "leninist": 31544, "punggol": 31545, "pasir": 31546, "mangal": 31547, "kommen": 31548, "das": 31549, "sseldorf": 31550, "flughafen": 31551, "vorst": 31552, "hajime": 31553, "unveil": 31554, "eventual": 31555, "withdrew": 31556, "fraktur": 31557, "starhub": 31558, "munis": 31559, "베": 31560, "暁": 31561, "혁": 31562, "enquin": 31563, "mcintyre": 31564, "boulay": 31565, "socs": 31566, "elegant": 31567, "크": 31568, "槩": 31569, "fandango": 31570, "㶲": 31571, "mella": 31572, "sfr": 31573, "ט": 31574, "䗩": 31575, "រ": 31576, "ម": 31577, "ហ": 31578, "ា": 31579, "ុ": 31580, "េ": 31581, "anzac": 31582, "柉": 31583, "psychedelic": 31584, "ẓ": 31585, "kurdistan": 31586, "esn": 31587, "峅": 31588, "nxd": 31589, "있": 31590, "cyworld": 31591, "친": 31592, "䲗": 31593, "mtn": 31594, "밤": 31595, "subsequent": 31596, "celebrate": 31597, "vulture": 31598, "venta": 31599, "memento": 31600, "neas": 31601, "cqd": 31602, "bons": 31603, "栝": 31604, "gourdon": 31605, "yasi": 31606, "dignitas": 31607, "gehenna": 31608, "埌": 31609, "昑": 31610, "洣": 31611, "nrw": 31612, "dima": 31613, "좋": 31614, "khalifa": 31615, "idolator": 31616, "gibing": 31617, "근": 31618, "binge": 31619, "slumber": 31620, "釬": 31621, "cyndi": 31622, "cidr": 31623, "skiff": 31624, "준": 31625, "nascimento": 31626, "㫤": 31627, "僎": 31628, "roommate": 31629, "₁": 31630, "ḱ": 31631, "ǵ": 31632, "orbiter": 31633, "adela": 31634, "䱵": 31635, "jeet": 31636, "瀼": 31637, "鮗": 31638, "dailymotion": 31639, "laus": 31640, "iceland": 31641, "wiser": 31642, "baumann": 31643, "grasse": 31644, "treyarch": 31645, "knowing": 31646, "윤": 31647, "呖": 31648, "sabina": 31649, "messi": 31650, "gangsta": 31651, "krung": 31652, "lipstick": 31653, "cwm": 31654, "𨱏": 31655, "dakshina": 31656, "爀": 31657, "nusantara": 31658, "르": 31659, "orpik": 31660, "garibaldi": 31661, "somma": 31662, "incubator": 31663, "canaan": 31664, "brag": 31665, "unus": 31666, "pegi": 31667, "looser": 31668, "harbourfront": 31669, "䳭": 31670, "㺢": 31671, "㹢": 31672, "狓": 31673, "歊": 31674, "梫": 31675, "tarja": 31676, "soule": 31677, "형": 31678, "ȵ": 31679, "fianc": 31680, "decent": 31681, "saeed": 31682, "僴": 31683, "bolshevik": 31684, "𢽾": 31685, "solidity": 31686, "炻": 31687, "kenz": 31688, "笒": 31689, "prng": 31690, "orphanage": 31691, "affero": 31692, "줘": 31693, "瑈": 31694, "katara": 31695, "khat": 31696, "mavis": 31697, "悱": 31698, "misconception": 31699, "jussi": 31700, "taag": 31701, "chrysalis": 31702, "himalayan": 31703, "deployed": 31704, "䲢": 31705, "蜮": 31706, "irp": 31707, "ribeira": 31708, "賹": 31709, "鲘": 31710, "fema": 31711, "requires": 31712, "儦": 31713, "cariboo": 31714, "vieille": 31715, "bondi": 31716, "綧": 31717, "후": 31718, "𫄨": 31719, "cuna": 31720, "lyrical": 31721, "rusa": 31722, "thematic": 31723, "코": 31724, "compare": 31725, "markdown": 31726, "shetland": 31727, "cozy": 31728, "寊": 31729, "荬": 31730, "picnic": 31731, "shebang": 31732, "wsu": 31733, "忳": 31734, "darul": 31735, "鄡": 31736, "苧": 31737, "primavera": 31738, "celsus": 31739, "fizz": 31740, "甝": 31741, "曔": 31742, "hibiscus": 31743, "stallman": 31744, "磙": 31745, "trickster": 31746, "눈": 31747, "jammu": 31748, "弖": 31749, "uverworld": 31750, "砫": 31751, "asce": 31752, "ruel": 31753, "finnish": 31754, "sledgehammer": 31755, "paste": 31756, "woff": 31757, "lully": 31758, "husayn": 31759, "severin": 31760, "mitro": 31761, "vallon": 31762, "孮": 31763, "손": 31764, "antares": 31765, "길": 31766, "stood": 31767, "ipoh": 31768, "pode": 31769, "鸑": 31770, "橒": 31771, "nuevo": 31772, "tiara": 31773, "civilian": 31774, "fishing": 31775, "invoke": 31776, "defeated": 31777, "玧": 31778, "憕": 31779, "bootleg": 31780, "stardom": 31781, "泇": 31782, "锿": 31783, "gruen": 31784, "aiki": 31785, "潽": 31786, "nrk": 31787, "wwwf": 31788, "eiji": 31789, "mirim": 31790, "bgs": 31791, "maron": 31792, "祅": 31793, "foolish": 31794, "bilateral": 31795, "㩲": 31796, "eih": 31797, "feint": 31798, "visionary": 31799, "憓": 31800, "倗": 31801, "montju": 31802, "delegation": 31803, "jackal": 31804, "thoth": 31805, "yori": 31806, "seon": 31807, "elysium": 31808, "drilling": 31809, "colmar": 31810, "firaxis": 31811, "lvarez": 31812, "tocantins": 31813, "madre": 31814, "loggia": 31815, "secl": 31816, "ryde": 31817, "emmanuelle": 31818, "fain": 31819, "窅": 31820, "ferret": 31821, "alcantara": 31822, "hakata": 31823, "troff": 31824, "侁": 31825, "hossein": 31826, "nmap": 31827, "haruka": 31828, "dindigul": 31829, "colombe": 31830, "gaillard": 31831, "sambar": 31832, "openstreetmap": 31833, "hurac": 31834, "irk": 31835, "onenote": 31836, "nemann": 31837, "드": 31838, "priceline": 31839, "埻": 31840, "favicon": 31841, "serri": 31842, "鰶": 31843, "arlin": 31844, "鍟": 31845, "moog": 31846, "𬭛": 31847, "𫟷": 31848, "鉨": 31849, "ⱪ": 31850, "archery": 31851, "ferrier": 31852, "锎": 31853, "xff": 31854, "sedna": 31855, "∥": 31856, "subjunctive": 31857, "nisi": 31858, "娎": 31859, "牼": 31860, "鞌": 31861, "ubiquity": 31862, "owari": 31863, "desiro": 31864, "shoki": 31865, "caterina": 31866, "municipio": 31867, "coachella": 31868, "mangling": 31869, "mende": 31870, "ultraman": 31871, "ʅ": 31872, "helloworld": 31873, "skor": 31874, "媄": 31875, "忯": 31876, "quarterfinal": 31877, "sorana": 31878, "rstea": 31879, "burglar": 31880, "truecrypt": 31881, "typescript": 31882, "溞": 31883, "fullmetal": 31884, "ngawang": 31885, "媓": 31886, "珫": 31887, "valiant": 31888, "槠": 31889, "硓": 31890, "autopackage": 31891, "㚟": 31892, "宄": 31893, "侅": 31894, 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+import numpy as np + +class preprocessing(): + def __init__(self, hidden_dim, word2idx, max_positions): + """ + :param hidden_dim: 模型hidden维度 + :param word2idx: 字典, for tokenizing + :param max_positions: 最大positions的长度, 用来初始化sinosoid positional encoding + """ + self.max_positions = max_positions+2 + self.hidden_dim = hidden_dim + self.word2idx = word2idx + self.pad_index = 0 + self.unk_index = 1 + self.cls_index = 2 + self.sep_index = 3 + self.mask_index = 4 + self.num_index = 5 + self.positional_encoding = self.init_positional_encoding() + + def init_positional_encoding(self): + # 初始化 sinosoid, + position_enc = np.array([ + [pos / np.power(10000, 2 * i / self.hidden_dim) for i in range(self.hidden_dim)] + if pos != 0 else np.zeros(self.hidden_dim) for pos in range(self.max_positions)]) + + position_enc[1:, 0::2] = np.sin(position_enc[1:, 0::2]) # dim 2i + position_enc[1:, 1::2] = np.cos(position_enc[1:, 1::2]) # dim 2i+1 + denominator = np.sqrt(np.sum(position_enc**2, axis=1, keepdims=True)) + position_enc = position_enc / (denominator + 1e-8) + return position_enc + + + def tokenize(self, text_or_label, dict): + # tokenize a sentence, return a list of tokens sequence + return [dict.get(i, self.unk_index) for i in text_or_label] + + def add_cls_sep(self, text_tokens): + return [self.cls_index] + text_tokens + [self.sep_index] + + def add_cls_sep_padding(self, tokens): + return [self.pad_index] + tokens + [self.pad_index] + + + def __call__(self, text_list, max_seq_len): + text_list_len = [len(i) for i in text_list] + # 判断输入文本长度是否合规, 是否小于等于 + if max(text_list_len) > self.max_positions - 2: + raise AssertionError( + "max_seq_len exceeds the maximum length of positional encoding! 输入的最大文本长度{}大于最大位置嵌入允许的长度{}!".format(max(text_list_len), self.max_positions-2)) + # if ignore error, then cut length + # max_seq_len = self.max_positions - 2 + # if max(text_list_len) > max_seq_len: + # warnings.warn( + # "maximum length of input texts exceeds \"max_seq_len\"! exceeded length will be cut off! 输入的最大文本长度大于指定最大长度, 多余的部分将会被剪切!") + batch_max_seq_len = max_seq_len + 2 + # tokenize + texts_tokens = [self.tokenize(i, self.word2idx) for i in text_list] + # add cls, sep + texts_tokens = [self.add_cls_sep(i) for i in texts_tokens] + # padding + texts_tokens = [torch.tensor(i) for i in texts_tokens] + texts_tokens = torch.nn.utils.rnn.pad_sequence(texts_tokens, batch_first=True) + positional_enc = \ + torch.from_numpy(self.positional_encoding[:batch_max_seq_len]).type(torch.FloatTensor) + return texts_tokens, positional_enc \ No newline at end of file diff --git a/transformer_tutorial/BERT_tutorial_and_attention_visualization/dataset/wiki_dataset.py b/transformer_tutorial/BERT_tutorial_and_attention_visualization/dataset/wiki_dataset.py new file mode 100644 index 0000000..6dea48b --- /dev/null +++ b/transformer_tutorial/BERT_tutorial_and_attention_visualization/dataset/wiki_dataset.py @@ -0,0 +1,139 @@ +from torch.utils.data import Dataset +import tqdm +import json +import torch +import random +import numpy as np +from sklearn.utils import shuffle + + +class BERTDataset(Dataset): + def __init__(self, corpus_path, word2idx_path, seq_len, hidden_dim=384, on_memory=True): + # hidden dimension for positional encoding + self.hidden_dim = hidden_dim + # define path of dicts + self.word2idx_path = word2idx_path + # define max length + self.seq_len = seq_len + # load whole corpus at once or not + self.on_memory = on_memory + # directory of corpus dataset + self.corpus_path = corpus_path + # define special symbols + self.pad_index = 0 + self.unk_index = 1 + self.cls_index = 2 + self.sep_index = 3 + self.mask_index = 4 + self.num_index = 5 + + # 加载字典 + with open(word2idx_path, "r", encoding="utf-8") as f: + self.word2idx = json.load(f) + + # 加载语料 + with open(corpus_path, "r", encoding="utf-8") as f: + if not on_memory: + # 如果不将数据集直接加载到内存, 则需先确定语料行数 + self.corpus_lines = 0 + for _ in tqdm.tqdm(f, desc="Loading Dataset"): + self.corpus_lines += 1 + + if on_memory: + # 将数据集全部加载到内存 + self.lines = [eval(line) for line in tqdm.tqdm(f, desc="Loading Dataset")] + self.corpus_lines = len(self.lines) + + if not on_memory: + # 如果不全部加载到内存, 首先打开语料 + self.file = open(corpus_path, "r", encoding="utf-8") + # 然后再打开同样的语料, 用来抽取负样本 + self.random_file = open(corpus_path, "r", encoding="utf-8") + # 下面是为了错位抽取负样本 + for _ in range(np.random.randint(self.corpus_lines if self.corpus_lines < 1000 else 1000)): + self.random_file.__next__() + + def __len__(self): + return self.corpus_lines + + def __getitem__(self, item): + t1, t2, is_next_label = self.random_sent(item) + + t1_random, t1_label = self.random_char(t1) + t2_random, t2_label = self.random_char(t2) + + t1 = [self.cls_index] + t1_random + [self.sep_index] + t2 = t2_random + [self.sep_index] + + t1_label = [self.pad_index] + t1_label + [self.pad_index] + t2_label = t2_label + [self.pad_index] + + segment_label = ([0 for _ in range(len(t1))] + [1 for _ in range(len(t2))])[:self.seq_len] + bert_input = (t1 + t2)[:self.seq_len] + bert_label = (t1_label + t2_label)[:self.seq_len] + + output = {"bert_input": torch.tensor(bert_input), + "bert_label": torch.tensor(bert_label), + "segment_label": torch.tensor(segment_label), + "is_next": torch.tensor([is_next_label])} + + return output + + def tokenize_char(self, segments): + return [self.word2idx.get(char, self.unk_index) for char in segments] + + def random_char(self, sentence): + char_tokens_ = list(sentence) + char_tokens = self.tokenize_char(char_tokens_) + + output_label = [] + for i, token in enumerate(char_tokens): + prob = random.random() + if prob < 0.30: + prob /= 0.30 + output_label.append(char_tokens[i]) + # 80% randomly change token to mask token + if prob < 0.8: + char_tokens[i] = self.mask_index + # 10% randomly change token to random token + elif prob < 0.9: + char_tokens[i] = random.randrange(len(self.word2idx)) + else: + output_label.append(0) + return char_tokens, output_label + + + def random_sent(self, index): + t1, t2 = self.get_corpus_line(index) + + # output_text, label(isNotNext:0, isNext:1) + if random.random() > 0.5: + return t1, t2, 1 + else: + return t1, self.get_random_line(), 0 + + def get_corpus_line(self, item): + if self.on_memory: + return self.lines[item]["text1"], self.lines[item]["text2"] + else: + line = self.file.__next__() + if line is None: + self.file.close() + self.file = open(self.corpus_path, "r", encoding="utf-8") + line = self.file.__next__() + line = eval(line) + t1, t2 = line["text1"], line["text2"] + return t1, t2 + + def get_random_line(self): + if self.on_memory: + return self.lines[random.randrange(len(self.lines))]["text2"] + + line = self.random_file.__next__() + if line is None: + self.random_file.close() + self.random_file = open(self.corpus_path, "r", encoding="utf-8") + for _ in range(np.random.randint(self.corpus_lines if self.corpus_lines < 1000 else 1000)): + self.random_file.__next__() + line = self.random_file.__next__() + return eval(line)["text2"] \ No newline at end of file diff --git a/transformer_tutorial/BERT_tutorial_and_attention_visualization/model_config.ini b/transformer_tutorial/BERT_tutorial_and_attention_visualization/model_config.ini new file mode 100644 index 0000000..983f56a --- /dev/null +++ b/transformer_tutorial/BERT_tutorial_and_attention_visualization/model_config.ini @@ -0,0 +1,8 @@ +[DEFAULT] +word2idx_path = ./corpus/bert_word2idx_extend.json +state_dict_dir = ./bert_state_dict +vocab_size = 32162 +num_workers = 0 +train_corpus_path = "./pretraining_data/wiki_dataset/train_wiki.txt" +test_corpus_path = "./pretraining_data/wiki_dataset/test_wiki.txt" + diff --git a/transformer_tutorial/BERT_tutorial_and_attention_visualization/models/__pycache__/__init__.cpython-36.pyc b/transformer_tutorial/BERT_tutorial_and_attention_visualization/models/__pycache__/__init__.cpython-36.pyc new file mode 100644 index 0000000..e2c32c2 Binary files /dev/null and b/transformer_tutorial/BERT_tutorial_and_attention_visualization/models/__pycache__/__init__.cpython-36.pyc differ diff --git a/transformer_tutorial/BERT_tutorial_and_attention_visualization/models/__pycache__/bert_model.cpython-36.pyc b/transformer_tutorial/BERT_tutorial_and_attention_visualization/models/__pycache__/bert_model.cpython-36.pyc new file mode 100644 index 0000000..744fc03 Binary files /dev/null and b/transformer_tutorial/BERT_tutorial_and_attention_visualization/models/__pycache__/bert_model.cpython-36.pyc differ diff --git a/transformer_tutorial/BERT_tutorial_and_attention_visualization/models/bert_model.py b/transformer_tutorial/BERT_tutorial_and_attention_visualization/models/bert_model.py new file mode 100644 index 0000000..e5ca473 --- /dev/null +++ b/transformer_tutorial/BERT_tutorial_and_attention_visualization/models/bert_model.py @@ -0,0 +1,1145 @@ +# coding=utf-8 +# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch BERT model.""" + +from __future__ import absolute_import, division, print_function, unicode_literals + +import copy +import json +import logging +import math +import os +import sys +from io import open +import torch +from torch import nn +from torch.nn import CrossEntropyLoss + + +logger = logging.getLogger(__name__) + +PRETRAINED_MODEL_ARCHIVE_MAP = { + 'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz", + 'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased.tar.gz", + 'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased.tar.gz", + 'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased.tar.gz", + 'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased.tar.gz", + 'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased.tar.gz", + 'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese.tar.gz", +} +BERT_CONFIG_NAME = 'bert_config.json' +TF_WEIGHTS_NAME = 'model.ckpt' + +def load_tf_weights_in_bert(model, tf_checkpoint_path): + """ Load tf checkpoints in a pytorch model + """ + try: + import re + import numpy as np + import tensorflow as tf + except ImportError: + print("Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see " + "https://www.tensorflow.org/install/ for installation instructions.") + raise + tf_path = os.path.abspath(tf_checkpoint_path) + print("Converting TensorFlow checkpoint from {}".format(tf_path)) + # Load weights from TF model + init_vars = tf.train.list_variables(tf_path) + names = [] + arrays = [] + for name, shape in init_vars: + print("Loading TF weight {} with shape {}".format(name, shape)) + array = tf.train.load_variable(tf_path, name) + names.append(name) + arrays.append(array) + + for name, array in zip(names, arrays): + name = name.split('/') + # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v + # which are not required for using pretrained model + if any(n in ["adam_v", "adam_m", "global_step"] for n in name): + print("Skipping {}".format("/".join(name))) + continue + pointer = model + for m_name in name: + if re.fullmatch(r'[A-Za-z]+_\d+', m_name): + l = re.split(r'_(\d+)', m_name) + else: + l = [m_name] + if l[0] == 'kernel' or l[0] == 'gamma': + pointer = getattr(pointer, 'weight') + elif l[0] == 'output_bias' or l[0] == 'beta': + pointer = getattr(pointer, 'bias') + elif l[0] == 'output_weights': + pointer = getattr(pointer, 'weight') + elif l[0] == 'squad': + pointer = getattr(pointer, 'classifier') + else: + try: + pointer = getattr(pointer, l[0]) + except AttributeError: + print("Skipping {}".format("/".join(name))) + continue + if len(l) >= 2: + num = int(l[1]) + pointer = pointer[num] + if m_name[-11:] == '_embeddings': + pointer = getattr(pointer, 'weight') + elif m_name == 'kernel': + array = np.transpose(array) + try: + assert pointer.shape == array.shape + except AssertionError as e: + e.args += (pointer.shape, array.shape) + raise + print("Initialize PyTorch weight {}".format(name)) + pointer.data = torch.from_numpy(array) + return model + + +def gelu(x): + """Implementation of the gelu activation function. + For information: OpenAI GPT's gelu is slightly different (and gives slightly different results): + 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) + Also see https://arxiv.org/abs/1606.08415 + """ + return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0))) + + +def swish(x): + return x * torch.sigmoid(x) + + +ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish} + + +class BertConfig(object): + """Configuration class to store the configuration of a `BertModel`. + """ + def __init__(self, + vocab_size_or_config_json_file, + hidden_size=384, + num_hidden_layers=6, + num_attention_heads=12, + intermediate_size=384*4, + hidden_act="gelu", + hidden_dropout_prob=0.1, + attention_probs_dropout_prob=0.1, + max_position_embeddings=512*2, + type_vocab_size=256, + initializer_range=0.02): + """Constructs BertConfig. + + Args: + vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `BertModel`. + hidden_size: Size of the encoder layers and the pooler layer. + num_hidden_layers: Number of hidden layers in the Transformer encoder. + num_attention_heads: Number of attention heads for each attention layer in + the Transformer encoder. + intermediate_size: The size of the "intermediate" (i.e., feed-forward) + layer in the Transformer encoder. + hidden_act: The non-linear activation function (function or string) in the + encoder and pooler. If string, "gelu", "relu" and "swish" are supported. + hidden_dropout_prob: The dropout probabilitiy for all fully connected + layers in the embeddings, encoder, and pooler. + attention_probs_dropout_prob: The dropout ratio for the attention + probabilities. + max_position_embeddings: The maximum sequence length that this model might + ever be used with. Typically set this to something large just in case + (e.g., 512 or 1024 or 2048). + type_vocab_size: The vocabulary size of the `token_type_ids` passed into + `BertModel`. + initializer_range: The sttdev of the truncated_normal_initializer for + initializing all weight matrices. + """ + if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2 + and isinstance(vocab_size_or_config_json_file, unicode)): + with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader: + json_config = json.loads(reader.read()) + for key, value in json_config.items(): + self.__dict__[key] = value + elif isinstance(vocab_size_or_config_json_file, int): + self.vocab_size = vocab_size_or_config_json_file + self.hidden_size = hidden_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.hidden_act = hidden_act + self.intermediate_size = intermediate_size + self.hidden_dropout_prob = hidden_dropout_prob + self.attention_probs_dropout_prob = attention_probs_dropout_prob + self.max_position_embeddings = max_position_embeddings + self.type_vocab_size = type_vocab_size + self.initializer_range = initializer_range + else: + raise ValueError("First argument must be either a vocabulary size (int)" + "or the path to a pretrained model config file (str)") + + @classmethod + def from_dict(cls, json_object): + """Constructs a `BertConfig` from a Python dictionary of parameters.""" + config = BertConfig(vocab_size_or_config_json_file=-1) + for key, value in json_object.items(): + config.__dict__[key] = value + return config + + @classmethod + def from_json_file(cls, json_file): + """Constructs a `BertConfig` from a json file of parameters.""" + with open(json_file, "r", encoding='utf-8') as reader: + text = reader.read() + return cls.from_dict(json.loads(text)) + + def __repr__(self): + return str(self.to_json_string()) + + def to_dict(self): + """Serializes this instance to a Python dictionary.""" + output = copy.deepcopy(self.__dict__) + return output + + def to_json_string(self): + """Serializes this instance to a JSON string.""" + return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n" + + def to_json_file(self, json_file_path): + """ Save this instance to a json file.""" + with open(json_file_path, "w", encoding='utf-8') as writer: + writer.write(self.to_json_string()) + +try: + from apex.normalization.fused_layer_norm import FusedLayerNorm as BertLayerNorm +except ImportError: + logger.info("Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex .") + class BertLayerNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-12): + """Construct a layernorm module in the TF style (epsilon inside the square root). + """ + super(BertLayerNorm, self).__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.bias = nn.Parameter(torch.zeros(hidden_size)) + self.variance_epsilon = eps + + def forward(self, x): + u = x.mean(-1, keepdim=True) + s = (x - u).pow(2).mean(-1, keepdim=True) + x = (x - u) / torch.sqrt(s + self.variance_epsilon) + return self.weight * x + self.bias + +class BertEmbeddings(nn.Module): + """Construct the embeddings from word, position and token_type embeddings. + """ + def __init__(self, config): + super(BertEmbeddings, self).__init__() + self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=0) + # self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) + self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) + nn.init.orthogonal_(self.word_embeddings.weight) + # nn.init.orthogonal_(self.position_embeddings.weight) + nn.init.orthogonal_(self.token_type_embeddings.weight) + epsilon = 1e-8 + self.word_embeddings.weight.data = self.word_embeddings.weight.data.div(torch.norm(self.word_embeddings.weight, p=2, dim=1, keepdim=True).data+epsilon) + self.token_type_embeddings.weight.data = self.token_type_embeddings.weight.data.div(torch.norm(self.token_type_embeddings.weight, p=2, dim=1, keepdim=True).data+epsilon) + + + + # freeze embedding parameters + # self.word_embeddings.weight.requires_grad = False + # self.token_type_embeddings.weight.requires_grad = False + + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, input_ids, positional_enc, token_type_ids=None, + ner_probs=None, ensemble_mode=False, num_tags=None): + """ + :param input_ids: + :param positional_enc: + :param token_type_ids: + :param ner_probs: 是没有经过argmax的维特比输出 + :param nre_mode: 训练nre模型的时候设为True + :return: + """ + + words_embeddings = self.word_embeddings(input_ids) + + if ensemble_mode: + token_type_embeddings = self.token_type_embeddings.weight[2: 2 + num_tags] + # [num_tags, embed_dim] + # ner_probs [batch, seq_len, num_tags] + token_type_embeddings = torch.matmul(ner_probs, token_type_embeddings) + else: + # if token_type_ids is None: + # token_type_ids = torch.zeros_like(input_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = words_embeddings + positional_enc + token_type_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + +class BertSelfAttention(nn.Module): + def __init__(self, config): + super(BertSelfAttention, self).__init__() + if config.hidden_size % config.num_attention_heads != 0: + raise ValueError( + "The hidden size (%d) is not a multiple of the number of attention " + "heads (%d)" % (config.hidden_size, config.num_attention_heads)) + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + x = x.view(*new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward(self, hidden_states, attention_mask, get_attention_matrices=False): + mixed_query_layer = self.query(hidden_states) + mixed_key_layer = self.key(hidden_states) + mixed_value_layer = self.value(hidden_states) + + query_layer = self.transpose_for_scores(mixed_query_layer) + key_layer = self.transpose_for_scores(mixed_key_layer) + value_layer = self.transpose_for_scores(mixed_value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + # Apply the attention mask is (precomputed for all layers in BertModel forward() function) + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + attention_probs_ = nn.Softmax(dim=-1)(attention_scores) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs_) + + context_layer = torch.matmul(attention_probs, value_layer) + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(*new_context_layer_shape) + if get_attention_matrices: + return context_layer, attention_probs_ + return context_layer, None + + +class BertSelfOutput(nn.Module): + def __init__(self, config): + super(BertSelfOutput, self).__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class BertAttention(nn.Module): + def __init__(self, config): + super(BertAttention, self).__init__() + self.self = BertSelfAttention(config) + self.output = BertSelfOutput(config) + + def forward(self, input_tensor, attention_mask, get_attention_matrices=False): + self_output, attention_matrices = self.self(input_tensor, attention_mask, get_attention_matrices=get_attention_matrices) + attention_output = self.output(self_output, input_tensor) + return attention_output, attention_matrices + + +class BertIntermediate(nn.Module): + def __init__(self, config): + super(BertIntermediate, self).__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str) or (sys.version_info[0] == 2 and isinstance(config.hidden_act, unicode)): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states): + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +class BertOutput(nn.Module): + def __init__(self, config): + super(BertOutput, self).__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class BertLayer(nn.Module): + def __init__(self, config): + super(BertLayer, self).__init__() + self.attention = BertAttention(config) + self.intermediate = BertIntermediate(config) + self.output = BertOutput(config) + + def forward(self, hidden_states, attention_mask, get_attention_matrices=False): + attention_output, attention_matrices = self.attention(hidden_states, attention_mask, get_attention_matrices=get_attention_matrices) + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output, attention_matrices + +# transformer block +class BertEncoder(nn.Module): + def __init__(self, config): + super(BertEncoder, self).__init__() + layer = BertLayer(config) + self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.num_hidden_layers)]) + + def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True, get_attention_matrices=False): + all_attention_matrices = [] + all_encoder_layers = [] + for layer_module in self.layer: + hidden_states, attention_matrices = layer_module(hidden_states, attention_mask, get_attention_matrices=get_attention_matrices) + if output_all_encoded_layers: + all_encoder_layers.append(hidden_states) + all_attention_matrices.append(attention_matrices) + if not output_all_encoded_layers: + all_encoder_layers.append(hidden_states) + all_attention_matrices.append(attention_matrices) + return all_encoder_layers, all_attention_matrices + + +class BertPooler(nn.Module): + def __init__(self, config): + super(BertPooler, self).__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states): + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class BertPredictionHeadTransform(nn.Module): + def __init__(self, config): + super(BertPredictionHeadTransform, self).__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + if isinstance(config.hidden_act, str) or (sys.version_info[0] == 2 and isinstance(config.hidden_act, unicode)): + self.transform_act_fn = ACT2FN[config.hidden_act] + else: + self.transform_act_fn = config.hidden_act + self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) + + def forward(self, hidden_states): + hidden_states = self.dense(hidden_states) + hidden_states = self.transform_act_fn(hidden_states) + hidden_states = self.LayerNorm(hidden_states) + return hidden_states + + +class BertLMPredictionHead(nn.Module): + def __init__(self, config, bert_model_embedding_weights): + super(BertLMPredictionHead, self).__init__() + self.transform = BertPredictionHeadTransform(config) + + # The output weights are the same as the input embeddings, but there is + # an output-only bias for each token. + self.decoder = nn.Linear(bert_model_embedding_weights.size(1), + bert_model_embedding_weights.size(0), + bias=False) + self.decoder.weight = bert_model_embedding_weights + self.bias = nn.Parameter(torch.zeros(bert_model_embedding_weights.size(0))) + + def forward(self, hidden_states): + hidden_states = self.transform(hidden_states) + hidden_states = self.decoder(hidden_states) + self.bias + return hidden_states + + +class BertOnlyMLMHead(nn.Module): + def __init__(self, config, bert_model_embedding_weights): + super(BertOnlyMLMHead, self).__init__() + self.predictions = BertLMPredictionHead(config, bert_model_embedding_weights) + + def forward(self, sequence_output): + prediction_scores = self.predictions(sequence_output) + return prediction_scores + + +class BertOnlyNSPHead(nn.Module): + def __init__(self, config): + super(BertOnlyNSPHead, self).__init__() + self.seq_relationship = nn.Linear(config.hidden_size, 2) + + def forward(self, pooled_output): + seq_relationship_score = self.seq_relationship(pooled_output) + return seq_relationship_score + + +class BertPreTrainingHeads(nn.Module): + def __init__(self, config, bert_model_embedding_weights): + super(BertPreTrainingHeads, self).__init__() + self.predictions = BertLMPredictionHead(config, bert_model_embedding_weights) + self.seq_relationship = nn.Linear(config.hidden_size, 2) + + def forward(self, sequence_output, pooled_output): + prediction_scores = self.predictions(sequence_output) + seq_relationship_score = self.seq_relationship(pooled_output) + return prediction_scores, seq_relationship_score + + +class BertPreTrainedModel(nn.Module): + """ An abstract class to handle weights initialization and + a simple interface for dowloading and loading pretrained models. + """ + def __init__(self, config, *inputs, **kwargs): + super(BertPreTrainedModel, self).__init__() + if not isinstance(config, BertConfig): + raise ValueError( + "Parameter config in `{}(config)` should be an instance of class `BertConfig`. " + "To create a model from a Google pretrained model use " + "`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format( + self.__class__.__name__, self.__class__.__name__ + )) + self.config = config + + def init_bert_weights(self, module): + """ Initialize the weights. + """ + if isinstance(module, (nn.Linear)): + # if isinstance(module, (nn.Linear, nn.Embedding)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + # nn.init.orthogonal_(module.weight) + + # nn.init.xavier_normal_(module.weight) + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + elif isinstance(module, BertLayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + if isinstance(module, nn.Linear) and module.bias is not None: + module.bias.data.zero_() + + +class BertModel(BertPreTrainedModel): + """BERT model ("Bidirectional Embedding Representations from a Transformer"). + + Params: + config: a BertConfig class instance with the configuration to build a new model + + Inputs: + `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] + with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts + `extract_features.py`, `run_classifier.py` and `run_squad.py`) + `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token + types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to + a `sentence B` token (see BERT paper for more details). + `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices + selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max + input sequence length in the current batch. It's the mask that we typically use for attention when + a batch has varying length sentences. + `output_all_encoded_layers`: boolean which controls the content of the `encoded_layers` output as described below. Default: `True`. + + Outputs: Tuple of (encoded_layers, pooled_output) + `encoded_layers`: controled by `output_all_encoded_layers` argument: + - `output_all_encoded_layers=True`: outputs a list of the full sequences of encoded-hidden-states at the end + of each attention block (i.e. 12 full sequences for BERT-base, 24 for BERT-large), each + encoded-hidden-state is a torch.FloatTensor of size [batch_size, sequence_length, hidden_size], + - `output_all_encoded_layers=False`: outputs only the full sequence of hidden-states corresponding + to the last attention block of shape [batch_size, sequence_length, hidden_size], + `pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a + classifier pretrained on top of the hidden state associated to the first character of the + input (`CLS`) to train on the Next-Sentence task (see BERT's paper). + + Example usage: + ```python + # Already been converted into WordPiece token ids + input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) + input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) + token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) + + config = modeling.BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, + num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) + + model = modeling.BertModel(config=config) + all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask) + ``` + """ + def __init__(self, config): + super(BertModel, self).__init__(config) + self.embeddings = BertEmbeddings(config) + self.encoder = BertEncoder(config) + self.pooler = BertPooler(config) + self.apply(self.init_bert_weights) + + def forward(self, input_ids, positional_enc, token_type_ids=None, attention_mask=None, output_all_encoded_layers=True, + get_attention_mask=False, ner_probs=None, ensemble_mode=False, num_tags=None, get_attention_matrices=False): + if attention_mask is None: + # torch.LongTensor + # attention_mask = torch.ones_like(input_ids) + if ensemble_mode: + attention_mask = (input_ids > 1) + else: + attention_mask = (input_ids > 0) + if token_type_ids is None: + token_type_ids = torch.zeros_like(input_ids) + + # We create a 3D attention mask from a 2D tensor mask. + # Sizes are [batch_size, 1, 1, to_seq_length] + # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] + # this attention mask is more simple than the triangular masking of causal attention + # used in OpenAI GPT, we just need to prepare the broadcast dimension here. + extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) + + # Since attention_mask is 1.0 for positions we want to attend and 0.0 for + # masked positions, this operation will create a tensor which is 0.0 for + # positions we want to attend and -10000.0 for masked positions. + # Since we are adding it to the raw scores before the softmax, this is + # effectively the same as removing these entirely. + extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility + extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 + + embedding_output = self.embeddings(input_ids, positional_enc, token_type_ids, + ner_probs=ner_probs, ensemble_mode=ensemble_mode, num_tags=num_tags) + encoded_layers, all_attention_matrices = self.encoder(embedding_output, + extended_attention_mask, + output_all_encoded_layers=output_all_encoded_layers, + get_attention_matrices=get_attention_matrices) + if get_attention_matrices: + return all_attention_matrices + sequence_output = encoded_layers[-1] + pooled_output = self.pooler(sequence_output) + if not output_all_encoded_layers: + encoded_layers = encoded_layers[-1] + if not get_attention_mask: + return encoded_layers, pooled_output + else: + return encoded_layers, pooled_output, attention_mask, extended_attention_mask + + +class BertForPreTraining(BertPreTrainedModel): + """BERT model with pre-training heads. + This module comprises the BERT model followed by the two pre-training heads: + - the masked language modeling head, and + - the next sentence classification head. + + Params: + config: a BertConfig class instance with the configuration to build a new model. + + Inputs: + `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] + with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts + `extract_features.py`, `run_classifier.py` and `run_squad.py`) + `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token + types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to + a `sentence B` token (see BERT paper for more details). + `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices + selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max + input sequence length in the current batch. It's the mask that we typically use for attention when + a batch has varying length sentences. + `masked_lm_labels`: optional masked language modeling labels: torch.LongTensor of shape [batch_size, sequence_length] + with indices selected in [-1, 0, ..., vocab_size]. All labels set to -1 are ignored (masked), the loss + is only computed for the labels set in [0, ..., vocab_size] + `next_sentence_label`: optional next sentence classification loss: torch.LongTensor of shape [batch_size] + with indices selected in [0, 1]. + 0 => next sentence is the continuation, 1 => next sentence is a random sentence. + + Outputs: + if `masked_lm_labels` and `next_sentence_label` are not `None`: + Outputs the total_loss which is the sum of the masked language modeling loss and the next + sentence classification loss. + if `masked_lm_labels` or `next_sentence_label` is `None`: + Outputs a tuple comprising + - the masked language modeling logits of shape [batch_size, sequence_length, vocab_size], and + - the next sentence classification logits of shape [batch_size, 2]. + + Example usage: + ```python + # Already been converted into WordPiece token ids + input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) + input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) + token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) + + config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, + num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) + + model = BertForPreTraining(config) + masked_lm_logits_scores, seq_relationship_logits = model(input_ids, token_type_ids, input_mask) + ``` + """ + def __init__(self, config): + super(BertForPreTraining, self).__init__(config) + self.bert = BertModel(config) + self.cls = BertPreTrainingHeads(config, self.bert.embeddings.word_embeddings.weight) + self.apply(self.init_bert_weights) + self.vocab_size = config.vocab_size + self.next_loss_func = CrossEntropyLoss() + self.mlm_loss_func = CrossEntropyLoss(ignore_index=0) + + def compute_loss(self, predictions, labels, num_class=2, ignore_index=-100): + loss_func = CrossEntropyLoss(ignore_index=ignore_index) + return loss_func(predictions.view(-1, num_class), labels.view(-1)) + + def forward(self, input_ids, positional_enc, token_type_ids=None, attention_mask=None, + masked_lm_labels=None, next_sentence_label=None): + sequence_output, pooled_output = self.bert(input_ids, positional_enc, token_type_ids, attention_mask, + output_all_encoded_layers=False) + mlm_preds, next_sen_preds = self.cls(sequence_output, pooled_output) + return mlm_preds, next_sen_preds + # mlm_loss = self.mlm_loss_func(mlm_preds.transpose(1, 2), masked_lm_labels) + # next_sen_loss = self.next_loss_func(next_sen_preds, next_sentence_label) + # total_loss = mlm_loss + next_sen_loss + # return total_loss, mlm_loss, next_sen_loss, mlm_preds, next_sen_preds + + # if masked_lm_labels is not None and next_sentence_label is not None: + # loss_fct = CrossEntropyLoss(ignore_index=-1) + # masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), masked_lm_labels.view(-1)) + # next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1)) + # total_loss = masked_lm_loss + next_sentence_loss + # return total_loss + # else: + # return prediction_scores, seq_relationship_score + + +class BertForMaskedLM(BertPreTrainedModel): + """BERT model with the masked language modeling head. + This module comprises the BERT model followed by the masked language modeling head. + + Params: + config: a BertConfig class instance with the configuration to build a new model. + + Inputs: + `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] + with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts + `extract_features.py`, `run_classifier.py` and `run_squad.py`) + `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token + types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to + a `sentence B` token (see BERT paper for more details). + `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices + selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max + input sequence length in the current batch. It's the mask that we typically use for attention when + a batch has varying length sentences. + `masked_lm_labels`: masked language modeling labels: torch.LongTensor of shape [batch_size, sequence_length] + with indices selected in [-1, 0, ..., vocab_size]. All labels set to -1 are ignored (masked), the loss + is only computed for the labels set in [0, ..., vocab_size] + + Outputs: + if `masked_lm_labels` is not `None`: + Outputs the masked language modeling loss. + if `masked_lm_labels` is `None`: + Outputs the masked language modeling logits of shape [batch_size, sequence_length, vocab_size]. + + Example usage: + ```python + # Already been converted into WordPiece token ids + input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) + input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) + token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) + + config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, + num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) + + model = BertForMaskedLM(config) + masked_lm_logits_scores = model(input_ids, token_type_ids, input_mask) + ``` + """ + def __init__(self, config): + super(BertForMaskedLM, self).__init__(config) + self.bert = BertModel(config) + self.cls = BertOnlyMLMHead(config, self.bert.embeddings.word_embeddings.weight) + self.apply(self.init_bert_weights) + + def forward(self, input_ids, token_type_ids=None, attention_mask=None, masked_lm_labels=None): + sequence_output, _ = self.bert(input_ids, token_type_ids, attention_mask, + output_all_encoded_layers=False) + prediction_scores = self.cls(sequence_output) + + if masked_lm_labels is not None: + loss_fct = CrossEntropyLoss(ignore_index=-1) + masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), masked_lm_labels.view(-1)) + return masked_lm_loss + else: + return prediction_scores + + +class BertForNextSentencePrediction(BertPreTrainedModel): + """BERT model with next sentence prediction head. + This module comprises the BERT model followed by the next sentence classification head. + + Params: + config: a BertConfig class instance with the configuration to build a new model. + + Inputs: + `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] + with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts + `extract_features.py`, `run_classifier.py` and `run_squad.py`) + `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token + types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to + a `sentence B` token (see BERT paper for more details). + `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices + selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max + input sequence length in the current batch. It's the mask that we typically use for attention when + a batch has varying length sentences. + `next_sentence_label`: next sentence classification loss: torch.LongTensor of shape [batch_size] + with indices selected in [0, 1]. + 0 => next sentence is the continuation, 1 => next sentence is a random sentence. + + Outputs: + if `next_sentence_label` is not `None`: + Outputs the total_loss which is the sum of the masked language modeling loss and the next + sentence classification loss. + if `next_sentence_label` is `None`: + Outputs the next sentence classification logits of shape [batch_size, 2]. + + Example usage: + ```python + # Already been converted into WordPiece token ids + input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) + input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) + token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) + + config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, + num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) + + model = BertForNextSentencePrediction(config) + seq_relationship_logits = model(input_ids, token_type_ids, input_mask) + ``` + """ + def __init__(self, config): + super(BertForNextSentencePrediction, self).__init__(config) + self.bert = BertModel(config) + self.cls = BertOnlyNSPHead(config) + self.apply(self.init_bert_weights) + + def forward(self, input_ids, token_type_ids=None, attention_mask=None, next_sentence_label=None): + _, pooled_output = self.bert(input_ids, token_type_ids, attention_mask, + output_all_encoded_layers=False) + seq_relationship_score = self.cls( pooled_output) + + if next_sentence_label is not None: + loss_fct = CrossEntropyLoss(ignore_index=-1) + next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1)) + return next_sentence_loss + else: + return seq_relationship_score + + +class BertForSequenceClassification(BertPreTrainedModel): + """BERT model for classification. + This module is composed of the BERT model with a linear layer on top of + the pooled output. + + Params: + `config`: a BertConfig class instance with the configuration to build a new model. + `num_labels`: the number of classes for the classifier. Default = 2. + + Inputs: + `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] + with the word token indices in the vocabulary. Items in the batch should begin with the special "CLS" token. (see the tokens preprocessing logic in the scripts + `extract_features.py`, `run_classifier.py` and `run_squad.py`) + `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token + types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to + a `sentence B` token (see BERT paper for more details). + `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices + selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max + input sequence length in the current batch. It's the mask that we typically use for attention when + a batch has varying length sentences. + `labels`: labels for the classification output: torch.LongTensor of shape [batch_size] + with indices selected in [0, ..., num_labels]. + + Outputs: + if `labels` is not `None`: + Outputs the CrossEntropy classification loss of the output with the labels. + if `labels` is `None`: + Outputs the classification logits of shape [batch_size, num_labels]. + + Example usage: + ```python + # Already been converted into WordPiece token ids + input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) + input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) + token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) + + config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, + num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) + + num_labels = 2 + + model = BertForSequenceClassification(config, num_labels) + logits = model(input_ids, token_type_ids, input_mask) + ``` + """ + def __init__(self, config, num_labels): + super(BertForSequenceClassification, self).__init__(config) + self.num_labels = num_labels + self.bert = BertModel(config) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.classifier = nn.Linear(config.hidden_size, num_labels) + self.apply(self.init_bert_weights) + + def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None): + _, pooled_output = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False) + pooled_output = self.dropout(pooled_output) + logits = self.classifier(pooled_output) + + if labels is not None: + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + return loss + else: + return logits + + +class BertForMultipleChoice(BertPreTrainedModel): + """BERT model for multiple choice tasks. + This module is composed of the BERT model with a linear layer on top of + the pooled output. + + Params: + `config`: a BertConfig class instance with the configuration to build a new model. + `num_choices`: the number of classes for the classifier. Default = 2. + + Inputs: + `input_ids`: a torch.LongTensor of shape [batch_size, num_choices, sequence_length] + with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts + `extract_features.py`, `run_classifier.py` and `run_squad.py`) + `token_type_ids`: an optional torch.LongTensor of shape [batch_size, num_choices, sequence_length] + with the token types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` + and type 1 corresponds to a `sentence B` token (see BERT paper for more details). + `attention_mask`: an optional torch.LongTensor of shape [batch_size, num_choices, sequence_length] with indices + selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max + input sequence length in the current batch. It's the mask that we typically use for attention when + a batch has varying length sentences. + `labels`: labels for the classification output: torch.LongTensor of shape [batch_size] + with indices selected in [0, ..., num_choices]. + + Outputs: + if `labels` is not `None`: + Outputs the CrossEntropy classification loss of the output with the labels. + if `labels` is `None`: + Outputs the classification logits of shape [batch_size, num_labels]. + + Example usage: + ```python + # Already been converted into WordPiece token ids + input_ids = torch.LongTensor([[[31, 51, 99], [15, 5, 0]], [[12, 16, 42], [14, 28, 57]]]) + input_mask = torch.LongTensor([[[1, 1, 1], [1, 1, 0]],[[1,1,0], [1, 0, 0]]]) + token_type_ids = torch.LongTensor([[[0, 0, 1], [0, 1, 0]],[[0, 1, 1], [0, 0, 1]]]) + config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, + num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) + + num_choices = 2 + + model = BertForMultipleChoice(config, num_choices) + logits = model(input_ids, token_type_ids, input_mask) + ``` + """ + def __init__(self, config, num_choices): + super(BertForMultipleChoice, self).__init__(config) + self.num_choices = num_choices + self.bert = BertModel(config) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.classifier = nn.Linear(config.hidden_size, 1) + self.apply(self.init_bert_weights) + + def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None): + flat_input_ids = input_ids.view(-1, input_ids.size(-1)) + flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) + flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) + _, pooled_output = self.bert(flat_input_ids, flat_token_type_ids, flat_attention_mask, output_all_encoded_layers=False) + pooled_output = self.dropout(pooled_output) + logits = self.classifier(pooled_output) + reshaped_logits = logits.view(-1, self.num_choices) + + if labels is not None: + loss_fct = CrossEntropyLoss() + loss = loss_fct(reshaped_logits, labels) + return loss + else: + return reshaped_logits + + +class BertForTokenClassification(BertPreTrainedModel): + """BERT model for token-level classification. + This module is composed of the BERT model with a linear layer on top of + the full hidden state of the last layer. + + Params: + `config`: a BertConfig class instance with the configuration to build a new model. + `num_labels`: the number of classes for the classifier. Default = 2. + + Inputs: + `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] + with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts + `extract_features.py`, `run_classifier.py` and `run_squad.py`) + `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token + types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to + a `sentence B` token (see BERT paper for more details). + `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices + selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max + input sequence length in the current batch. It's the mask that we typically use for attention when + a batch has varying length sentences. + `labels`: labels for the classification output: torch.LongTensor of shape [batch_size, sequence_length] + with indices selected in [0, ..., num_labels]. + + Outputs: + if `labels` is not `None`: + Outputs the CrossEntropy classification loss of the output with the labels. + if `labels` is `None`: + Outputs the classification logits of shape [batch_size, sequence_length, num_labels]. + + Example usage: + ```python + # Already been converted into WordPiece token ids + input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) + input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) + token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) + + config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, + num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) + + num_labels = 2 + + model = BertForTokenClassification(config, num_labels) + logits = model(input_ids, token_type_ids, input_mask) + ``` + """ + def __init__(self, config, num_labels): + super(BertForTokenClassification, self).__init__(config) + self.num_labels = num_labels + self.bert = BertModel(config) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.classifier = nn.Linear(config.hidden_size, num_labels) + self.apply(self.init_bert_weights) + + def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None): + sequence_output, _ = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False) + sequence_output = self.dropout(sequence_output) + logits = self.classifier(sequence_output) + + if labels is not None: + loss_fct = CrossEntropyLoss() + # Only keep active parts of the loss + if attention_mask is not None: + active_loss = attention_mask.view(-1) == 1 + active_logits = logits.view(-1, self.num_labels)[active_loss] + active_labels = labels.view(-1)[active_loss] + loss = loss_fct(active_logits, active_labels) + else: + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + return loss + else: + return logits + + +class BertForQuestionAnswering(BertPreTrainedModel): + """BERT model for Question Answering (span extraction). + This module is composed of the BERT model with a linear layer on top of + the sequence output that computes start_logits and end_logits + + Params: + `config`: a BertConfig class instance with the configuration to build a new model. + + Inputs: + `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] + with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts + `extract_features.py`, `run_classifier.py` and `run_squad.py`) + `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token + types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to + a `sentence B` token (see BERT paper for more details). + `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices + selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max + input sequence length in the current batch. It's the mask that we typically use for attention when + a batch has varying length sentences. + `start_positions`: position of the first token for the labeled span: torch.LongTensor of shape [batch_size]. + Positions are clamped to the length of the sequence and position outside of the sequence are not taken + into account for computing the loss. + `end_positions`: position of the last token for the labeled span: torch.LongTensor of shape [batch_size]. + Positions are clamped to the length of the sequence and position outside of the sequence are not taken + into account for computing the loss. + + Outputs: + if `start_positions` and `end_positions` are not `None`: + Outputs the total_loss which is the sum of the CrossEntropy loss for the start and end token positions. + if `start_positions` or `end_positions` is `None`: + Outputs a tuple of start_logits, end_logits which are the logits respectively for the start and end + position tokens of shape [batch_size, sequence_length]. + + Example usage: + ```python + # Already been converted into WordPiece token ids + input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) + input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) + token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) + + config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, + num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) + + model = BertForQuestionAnswering(config) + start_logits, end_logits = model(input_ids, token_type_ids, input_mask) + ``` + """ + def __init__(self, config): + super(BertForQuestionAnswering, self).__init__(config) + self.bert = BertModel(config) + # TODO check with Google if it's normal there is no dropout on the token classifier of SQuAD in the TF version + # self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.qa_outputs = nn.Linear(config.hidden_size, 2) + self.apply(self.init_bert_weights) + + def forward(self, input_ids, token_type_ids=None, attention_mask=None, start_positions=None, end_positions=None): + sequence_output, _ = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False) + logits = self.qa_outputs(sequence_output) + start_logits, end_logits = logits.split(1, dim=-1) + start_logits = start_logits.squeeze(-1) + end_logits = end_logits.squeeze(-1) + + if start_positions is not None and end_positions is not None: + # If we are on multi-GPU, split add a dimension + if len(start_positions.size()) > 1: + start_positions = start_positions.squeeze(-1) + if len(end_positions.size()) > 1: + end_positions = end_positions.squeeze(-1) + # sometimes the start/end positions are outside our model inputs, we ignore these terms + ignored_index = start_logits.size(1) + start_positions.clamp_(0, ignored_index) + end_positions.clamp_(0, ignored_index) + + loss_fct = CrossEntropyLoss(ignore_index=ignored_index) + start_loss = loss_fct(start_logits, start_positions) + end_loss = loss_fct(end_logits, end_positions) + total_loss = (start_loss + end_loss) / 2 + return total_loss + else: + return start_logits, end_logits diff --git a/transformer_tutorial/BERT_tutorial_and_attention_visualization/plot_attention.ipynb b/transformer_tutorial/BERT_tutorial_and_attention_visualization/plot_attention.ipynb new file mode 100644 index 0000000..f998540 --- /dev/null +++ b/transformer_tutorial/BERT_tutorial_and_attention_visualization/plot_attention.ipynb @@ -0,0 +1,91 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from attention_visualization import *\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "# 为了支持中文显示\n", + "from matplotlib.font_manager import FontProperties\n", + "font = FontProperties(fname='./SimHei.ttf')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "model = Pretrainer(vocab_size=config[\"vocab_size\"], #字数量\n", + " max_seq_len=256, # 最大句长\n", + " batch_size=1, # batch_size只能为1\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "text = \"为什么要上班\"\n", + "attention_matrices = model(text)\n", + "print(attention_matrices[0].shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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中文自然语言处理
\n", + "
Transformer模型(一)
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "说到自然语言处理, 语言模型, 命名实体识别, 机器翻译, 可能很多人想到的**LSTM等循环神经网络**, 但目前其实LSTM起码在自然语言处理领域已经过时了, 在Stanford阅读理解数据集**(SQuAD2.0)**榜单里, 机器的成绩已经超人类表现, 这很大程度要归功于**transformer的BERT预训练模型**. \n", + "今天我们来讲一下**transformer模型**, 你不需要有很多深度学习和数学基础, 我来用简单的语言和可视化的方法从零讲起. \n", + "transformer是谷歌大脑在2017年底发表的论文**attention is all you need**中所提出的seq2seq模型. 现在已经取得了大范围的应用和扩展, 而BERT就是从transformer中衍生出来的预训练语言模型. \n", + "\n", + "在我们开始之前, 允许我简单说一下目前自然语言处理领域的现状, 目前**transformer**模型已经得到广泛认可和应用, 而应用的方式主要是先进行**预训练语言模型**, 然后把预训练的模型适配给下游任务, 以完成各种不同的任务, 如分类, 生成, 标记等等, 预训练模型非常重要, 预训练的模型的性能直接影响下游任务的性能, 通过我制作的这几期视频, 我有信心让小伙伴们充分理解transformer并具备一定衍生模型的设计和编写能力." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "为了让大家充分理解和初步使用transformer和训练BERT, 并应用到自己的需求上, 这个连载课程将包括以下几个视频来完成, 今天讲解第一部分: \n", + "__一. transformer编码器(理论部分)__:\n", + "0. $transformer$模型的直觉, 建立直观认识;\n", + "1. $positional \\ encoding$, 即**位置嵌入**(或位置编码);\n", + "2. $self \\ attention \\ mechanism$, 即**自注意力机制**与**注意力矩阵可视化**;\n", + "3. $Layer \\ Normalization$和残差连接.\n", + "4. $transformer \\ encoder$整体结构. \n", + "\n", + "__二. transformer代码解读, 语料数据预处理, BERT的预训练和情感分析的应用__:\n", + "\n", + "__三. sequence 2 sequence(序列到序列)模型或Name Entity Recognition(命名实体识别)(待定)__: \n", + "此部分根据前面的反馈待定." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 0. $transformer$模型的直觉, 建立直观认识;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "首先来说一下**transformer**和**LSTM**的最大区别, 就是LSTM的训练是迭代的, 是一个接一个字的来, 当前这个字过完LSTM单元, 才可以进下一个字, 而transformer的训练是并行了, 就是所有字是全部同时训练的, 这样就大大加快了计算效率, transformer使用了位置嵌入$(positional \\ encoding)$来理解语言的顺序, 使用自注意力机制和全连接层来进行计算, 这些后面都会详细讲解. \n", + "transformer模型主要分为**两大部分**, 分别是**编码器**和**解码器**, **编码器**负责把自然语言序列映射成为**隐藏层**(下图中**第2步**用九宫格比喻的部分), 含有自然语言序列的数学表达. 然后解码器把隐藏层再映射为自然语言序列, 从而使我们可以解决各种问题, 如情感分类, 命名实体识别, 语义关系抽取, 摘要生成, 机器翻译等等, 下面我们简单说一下下图的每一步都做了什么: \n", + "1. 输入自然语言序列到编码器: Why do we work?(为什么要工作);\n", + "2. 编码器输出的隐藏层, 再输入到解码器;\n", + "3. 输入$$(起始)符号到解码器;\n", + "4. 得到第一个字\"为\";\n", + "5. 将得到的第一个字\"为\"落下来再输入到解码器;\n", + "6. 得到第二个字\"什\";\n", + "7. 将得到的第二字再落下来, 直到解码器输出$$(终止符), 即序列生成完成." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](./imgs/intuition.jpg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "我们这节课的内容限于**编码器**部分, 即把**自然语言序列映射为隐藏层的数学表达的过程**, 因为理解了编码器中的结构, 理解解码器就非常简单了,最重要的是**BERT预训练模型**只用到了编码器的部分, 也就是先用编码器训练一个语言模型, 然后再把它适配给其他五花八门的任务. \n", + "如果你不知道**语言模型**是什么, 没关系, 这丝毫不影响本节课内容的理解, 下次我们讲**BERT**的时候会讲. \n", + "而且我们用编码器就能够完成一些自然语言处理中比较主流的任务, 如情感分类, 语义关系分析, 命名实体识别等, 解码器的内容和序列到序列模型有机会我们会涉及到." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Transformer Block结构图, 注意: 为方便查看, 下面的内容分别对应着上图第1, 2, 3, 4个方框的序号:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](./imgs/encoder.jpg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. $positional \\ encoding$, 即**位置嵌入**(或位置编码);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "由于transformer模型**没有**循环神经网络的迭代操作, 所以我们必须提供每个字的位置信息给transformer, 才能识别出语言中的顺序关系. \n", + "现在定义一个位置嵌入的概念, 也就是$positional \\ encoding$, 位置嵌入的维度为$[max \\ sequence \\ length, \\ embedding \\ dimension]$, 嵌入的维度同词向量的维度, $max \\ sequence \\ length$属于超参数, 指的是限定的最大单个句长. \n", + "注意, 我们一般以字为单位训练transformer模型, 也就是说我们不用分词了, 首先我们要初始化字向量为$[vocab \\ size, \\ embedding \\ dimension]$, $vocab \\ size$为总共的字库数量, $embedding \\ dimension$为字向量的维度, 也是每个字的数学表达. \n", + "在这里论文中使用了$sine$和$cosine$函数的线性变换来提供给模型位置信息: \n", + "$$PE_{(pos,2i)} = sin(pos / 10000^{2i/d_{\\text{model}}}) \\quad PE_{(pos,2i+1)} = cos(pos / 10000^{2i/d_{\\text{model}}})\\tag{eq.1}$$\n", + "上式中$pos$指的是句中字的位置, 取值范围是$[0, \\ max \\ sequence \\ length)$, $i$指的是词向量的维度, 取值范围是$[0, \\ embedding \\ dimension)$, 上面有$sin$和$cos$一组公式, 也就是对应着$embedding \\ dimension$维度的一组奇数和偶数的序号的维度, 例如$0, 1$一组, $2, 3$一组, 分别用上面的$sin$和$cos$函数做处理, 从而产生不同的周期性变化, 而位置嵌入在$embedding \\ dimension$维度上随着维度序号增大, 周期变化会越来越慢, 而产生一种包含位置信息的纹理, 就像论文原文中第六页讲的, 位置嵌入函数的周期从$2 \\pi$到$10000 * 2 \\pi$变化, 而每一个位置在$embedding \\ dimension$维度上都会得到不同周期的$sin$和$cos$函数的取值组合, 从而产生独一的纹理位置信息, 模型从而学到位置之间的依赖关系和自然语言的时序特性. \n", + "下面画一下位置嵌入, 可见纵向观察, 随着$embedding \\ dimension$增大, 位置嵌入函数呈现不同的周期变化." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "# 导入依赖库\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import math" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "def get_positional_encoding(max_seq_len, embed_dim):\n", + " # 初始化一个positional encoding\n", + " # embed_dim: 字嵌入的维度\n", + " # max_seq_len: 最大的序列长度\n", + " positional_encoding = np.array([\n", + " [pos / np.power(10000, 2 * i / embed_dim) for i in range(embed_dim)]\n", + " if pos != 0 else np.zeros(embed_dim) for pos in range(max_seq_len)])\n", + " positional_encoding[1:, 0::2] = np.sin(positional_encoding[1:, 0::2]) # dim 2i 偶数\n", + " positional_encoding[1:, 1::2] = np.cos(positional_encoding[1:, 1::2]) # dim 2i+1 奇数\n", + " # 归一化, 用位置嵌入的每一行除以它的模长\n", + " # denominator = np.sqrt(np.sum(position_enc**2, axis=1, keepdims=True))\n", + " # position_enc = position_enc / (denominator + 1e-8)\n", + " return positional_encoding" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(69.0, 0.5, 'sequence length')" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "positional_encoding = get_positional_encoding(max_seq_len=100, embed_dim=16)\n", + "plt.figure(figsize=(10,10))\n", + "sns.heatmap(positional_encoding)\n", + "plt.title(\"Sinusoidal Function\")\n", + "plt.xlabel(\"hidden dimension\")\n", + "plt.ylabel(\"sequence length\")" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Period of Positional Encoding')" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 5))\n", + "plt.plot(positional_encoding[1:, 1], label=\"dimension 1\")\n", + "plt.plot(positional_encoding[1:, 2], label=\"dimension 2\")\n", + "plt.plot(positional_encoding[1:, 3], label=\"dimension 3\")\n", + "plt.legend()\n", + "plt.xlabel(\"Sequence length\")\n", + "plt.ylabel(\"Period of Positional Encoding\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X: [batch_size, len, embedding_size]\n", + "W: [embedding_size, hidden_dimension]\n", + "XW = [batch_size, len, hidden_dimension]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. $self \\ attention \\ mechanism$, **自注意力机制**;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](./imgs/attention_0.jpg)\n", + "![](./imgs/attention_1.jpg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Attention Mask" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](./imgs/attention_mask.jpg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "注意, 在上面$self \\ attention$的计算过程中, 我们通常使用$mini \\ batch$来计算, 也就是一次计算多句话, 也就是$X$的维度是$[batch \\ size, \\ sequence \\ length]$, $sequence \\ length$是句长, 而一个$mini \\ batch$是由多个不等长的句子组成的, 我们就需要按照这个$mini \\ batch$中最大的句长对剩余的句子进行补齐长度, 我们一般用$0$来进行填充, 这个过程叫做$padding$. \n", + "但这时在进行$softmax$的时候就会产生问题, 回顾$softmax$函数$\\sigma (\\mathbf {z} )_{i}={\\frac {e^{z_{i}}}{\\sum _{j=1}^{K}e^{z_{j}}}}$, $e^0$是1, 是有值的, 这样的话$softmax$中被$padding$的部分就参与了运算, 就等于是让无效的部分参与了运算, 会产生很大隐患, 这时就需要做一个$mask$让这些无效区域不参与运算, 我们一般给无效区域加一个很大的负数的偏置, 也就是:\n", + "$$z_{illegal} = z_{illegal} + bias_{illegal}$$\n", + "$$bias_{illegal} \\to -\\infty$$\n", + "$$e^{z_{illegal}} \\to 0 $$\n", + "经过上式的$masking$我们使无效区域经过$softmax$计算之后还几乎为$0$, 这样就避免了无效区域参与计算.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. $Layer \\ Normalization$和残差连接." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1). **残差连接**: \n", + "我们在上一步得到了经过注意力矩阵加权之后的$V$, 也就是$Attention(Q, \\ K, \\ V)$, 我们对它进行一下转置, 使其和$X_{embedding}$的维度一致, 也就是$[batch \\ size, \\ sequence \\ length, \\ embedding \\ dimension]$, 然后把他们加起来做残差连接, 直接进行元素相加, 因为他们的维度一致: \n", + "$$X_{embedding} + Attention(Q, \\ K, \\ V)$$\n", + "在之后的运算里, 每经过一个模块的运算, 都要把运算之前的值和运算之后的值相加, 从而得到残差连接, 训练的时候可以使梯度直接走捷径反传到最初始层:\n", + "$$X + SubLayer(X) \\tag{eq. 5}$$\n", + "2). $LayerNorm$: \n", + "$Layer Normalization$的作用是把神经网络中隐藏层归一为标准正态分布, 也就是$i.i.d$独立同分布, 以起到加快训练速度, 加速收敛的作用:\n", + "$$\\mu_{i}=\\frac{1}{m} \\sum^{m}_{i=1}x_{ij}$$\n", + "上式中以矩阵的行$(row)$为单位求均值;\n", + "$$\\sigma^{2}_{j}=\\frac{1}{m} \\sum^{m}_{i=1}\n", + "(x_{ij}-\\mu_{j})^{2}$$\n", + "上式中以矩阵的行$(row)$为单位求方差;\n", + "$$LayerNorm(x)=\\alpha \\odot \\frac{x_{ij}-\\mu_{i}}\n", + "{\\sqrt{\\sigma^{2}_{i}+\\epsilon}} + \\beta \\tag{eq.6}$$\n", + "然后用**每一行**的**每一个元素**减去**这行的均值**, 再除以**这行的标准差**, 从而得到归一化后的数值, $\\epsilon$是为了防止除$0$; \n", + "之后引入两个可训练参数$\\alpha, \\ \\beta$来弥补归一化的过程中损失掉的信息, 注意$\\odot$表示元素相乘而不是点积, 我们一般初始化$\\alpha$为全$1$, 而$\\beta$为全$0$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. $transformer \\ encoder$整体结构. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "经过上面3个步骤, 我们已经基本了解到来$transformer$编码器的主要构成部分, 我们下面用公式把一个$transformer \\ block$的计算过程整理一下: \n", + "1). 字向量与位置编码: \n", + "$$X = EmbeddingLookup(X) + PositionalEncoding \\tag{eq.2}$$\n", + "$$X \\in \\mathbb{R}^{batch \\ size \\ * \\ seq. \\ len. \\ * \\ embed. \\ dim.} $$\n", + "2). 自注意力机制: \n", + "$$Q = Linear(X) = XW_{Q}$$ \n", + "$$K = Linear(X) = XW_{K} \\tag{eq.3}$$\n", + "$$V = Linear(X) = XW_{V}$$\n", + "$$X_{attention} = SelfAttention(Q, \\ K, \\ V) \\tag{eq.4}$$\n", + "3). 残差连接与$Layer \\ Normalization$\n", + "$$X_{attention} = X + X_{attention} \\tag{eq. 5}$$\n", + "$$X_{attention} = LayerNorm(X_{attention}) \\tag{eq. 6}$$\n", + "4). 下面进行$transformer \\ block$结构图中的**第4部分**, 也就是$FeedForward$, 其实就是两层线性映射并用激活函数激活, 比如说$ReLU$: \n", + "$$X_{hidden} = Activate(Linear(Linear(X_{attention}))) \\tag{eq. 7}$$\n", + "5). 重复3).:\n", + "$$X_{hidden} = X_{attention} + X_{hidden}$$\n", + "$$X_{hidden} = LayerNorm(X_{hidden})$$\n", + "$$X_{hidden} \\in \\mathbb{R}^{batch \\ size \\ * \\ seq. \\ len. \\ * \\ embed. \\ dim.} $$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**小结:** \n", + "我们到现在位置已经讲完了transformer的编码器的部分, 了解到了transformer是怎样获得自然语言的位置信息的, 注意力机制是怎样的, 其实举个语言情感分类的例子, 我们已经知道, 经过自注意力机制, 一句话中的每个字都含有这句话中其他所有字的信息, 那么我们可不可以添加一个空白字符到句子最前面, 然后让句子中的所有信息向这个空白字符汇总, 然后再映射成想要分的类别呢? 这就是**BERT**, 我们下次会讲到. \n", + "在**BERT**的预训练中, 我们给每句话的句头加一个特殊字符, 然后句末再加一个特殊字符, 之后模型预训练完毕之后, 我们就可以用句头的特殊字符的$hidden \\ state$完成一些分类任务了." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}