diff --git a/0-eval_pretrain.py b/0-eval_pretrain.py new file mode 100644 index 0000000..0fa9710 --- /dev/null +++ b/0-eval_pretrain.py @@ -0,0 +1,180 @@ +import random +import time + +import numpy as np +import torch +import warnings +from transformers import AutoTokenizer, AutoModelForCausalLM +from model.model import Transformer +from model.LMConfig import LMConfig + +warnings.filterwarnings('ignore') + + +def count_parameters(model): + return sum(p.numel() for p in model.parameters() if p.requires_grad) + + +def init_model(lm_config): + tokenizer = AutoTokenizer.from_pretrained('./model', + trust_remote_code=True, use_fast=False) + model_from = 1 # 1从权重,2用transformers + + if model_from == 1: + moe_path = '_moe' if lm_config.use_moe else '' + ckp = f'./out/pretrain_{lm_config.dim}{moe_path}.pth' + + model = Transformer(lm_config) + state_dict = torch.load(ckp, map_location=device) + + # 处理不需要的前缀 + unwanted_prefix = '_orig_mod.' + for k, v in list(state_dict.items()): + if k.startswith(unwanted_prefix): + state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k) + + for k, v in list(state_dict.items()): + if 'mask' in k: + del state_dict[k] + + # 加载到模型中 + model.load_state_dict(state_dict, strict=False) + else: + model = AutoModelForCausalLM.from_pretrained('minimind', trust_remote_code=True) + model = model.to(device) + + print(f'模型参数: {count_parameters(model) / 1e6} 百万 = {count_parameters(model) / 1e9} B (Billion)') + return model, tokenizer + + +def setup_seed(seed): + random.seed(seed) # 设置 Python 的随机种子 + np.random.seed(seed) # 设置 NumPy 的随机种子 + torch.manual_seed(seed) # 设置 PyTorch 的随机种子 + torch.cuda.manual_seed(seed) # 为当前 GPU 设置随机种子(如果有) + torch.cuda.manual_seed_all(seed) # 为所有 GPU 设置随机种子(如果有) + torch.backends.cudnn.deterministic = True # 确保每次返回的卷积算法是确定的 + torch.backends.cudnn.benchmark = False # 关闭 cuDNN 的自动调优,避免不确定性 + + +if __name__ == "__main__": + # ----------------------------------------------------------------------------- + out_dir = 'out' + start = "" + temperature = 0.7 + top_k = 8 + setup_seed(1337) + # device = 'cpu' + device = 'cuda:0' if torch.cuda.is_available() else 'cpu' + dtype = 'bfloat16' + max_seq_len = 512 + lm_config = LMConfig() + lm_config.max_seq_len = max_seq_len + # 对话是否携带历史对话(当前模型太弱,增大历史上下文,基本导致胡言乱语) + contain_history_chat = False + # ----------------------------------------------------------------------------- + + model, tokenizer = init_model(lm_config) + + model = model.eval() + # 推送到huggingface + # model.push_to_hub("minimind") + # tokenizer.push_to_hub("minimind") + + # answer_way = int(input('输入0自动测试,输入1问题测试:')) + answer_way = 0 + stream = True + + prompt_datas = [ + '椭圆和圆的区别', + '中国关于马克思主义基本原理', + '人类大脑的主要功能是', + '万有引力是', + '世界上人口最多的国家是', + 'DNA的全称是', + '数学中π的值大约是', + '世界上最高的山峰是', + '太阳系中最大的行星是', + '二氧化碳的化学分子式是', + '地球上最大的动物是', + '地球自转一圈大约需要', + '杭州市的美食有', + '江苏省的最好的大学', + ] + + messages_origin = [] + messages = messages_origin + + qa_index = 0 + while True: + start = time.time() + if not contain_history_chat: + messages = messages_origin.copy() + + if answer_way == 1: + # run generation + prompt = input('用户:') + else: + if qa_index >= len(prompt_datas): + break + prompt = prompt_datas[qa_index] + print('问题:', prompt) + qa_index += 1 + + messages.append({"role": "user", "content": prompt}) + + # print(messages) + new_prompt = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True + )[-(max_seq_len - 1):] + + x = tokenizer(prompt).data['input_ids'] + x = (torch.tensor(x, dtype=torch.long, device=device)[None, ...]) + + answer = new_prompt + + with torch.no_grad(): + res_y = model.generate(x, tokenizer.eos_token_id, max_new_tokens=max_seq_len, temperature=temperature, + top_k=top_k, stream=stream) + print('回答:', end='') + try: + y = next(res_y) + except StopIteration: + print("No answer") + continue + + history_idx = 0 + while y != None: + answer = tokenizer.decode(y[0].tolist()) + if answer and answer[-1] == '�': + try: + y = next(res_y) + except: + break + continue + # print(answer) + if not len(answer): + try: + y = next(res_y) + except: + break + continue + + print(answer[history_idx:], end='', flush=True) + try: + y = next(res_y) + except: + break + history_idx = len(answer) + if not stream: + break + + print('\n') + + if contain_history_chat: + assistant_answer = answer.replace(new_prompt, "") + messages.append({"role": "assistant", "content": assistant_answer}) + end = time.time() + print(end - start,'s') diff --git a/1-pretrain.py b/1-pretrain.py new file mode 100644 index 0000000..720396e --- /dev/null +++ b/1-pretrain.py @@ -0,0 +1,194 @@ +import os +import platform +import time +import math +import warnings +import torch +import torch.distributed as dist +from torch import optim +from torch.nn.parallel import DistributedDataParallel +from torch.optim.lr_scheduler import CosineAnnealingLR +from torch.utils.data import DataLoader, DistributedSampler +from contextlib import nullcontext +from model.model import Transformer +from model.LMConfig import LMConfig +from model.dataset import PretrainDataset + +warnings.filterwarnings('ignore') + + +def Logger(content): + if not ddp or dist.get_rank() == 0: + print(content) + + +def get_lr(it, all): + warmup_iters = 0 + lr_decay_iters = all + min_lr = learning_rate / 10 + + if it < warmup_iters: + return learning_rate * it / warmup_iters + if it > lr_decay_iters: + return min_lr + decay_ratio = (it - warmup_iters) / (lr_decay_iters - warmup_iters) + assert 0 <= decay_ratio <= 1 + coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio)) + return min_lr + coeff * (learning_rate - min_lr) + + +def train_epoch(epoch): + start_time = time.time() + + for step, (X, Y) in enumerate(train_loader): + X = X.to(device) + Y = Y.to(device) + + # 设置学习率 + lr = get_lr(epoch * iter_per_epoch + step, epochs * iter_per_epoch) + for param_group in optimizer.param_groups: + param_group['lr'] = lr + + # 前向传播和损失计算 + with ctx: + out = model(X, Y) + loss = out.last_loss + + # 反向传播 + scaler.scale(loss).backward() + + # 梯度剪裁和更新参数 + scaler.unscale_(optimizer) + torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) + scaler.step(optimizer) + scaler.update() + + # 清零梯度 + optimizer.zero_grad(set_to_none=True) + + if step % 100 == 0: + spend_time = time.time() - start_time + Logger( + 'Epoch:[{}/{}]({}/{}) loss:{:.3f} lr:{:.7f} epoch_Time:{}min:'.format( + epoch, + epochs, + step, + iter_per_epoch, + loss.item(), + optimizer.param_groups[-1]['lr'], + spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60)) + + if (step + 1) % 1000 == 0 and (not ddp or dist.get_rank() == 0): + model.eval() + # torch.save(model.state_dict(), '{}/iter_{}.pth'.format(save_dir, int(step + epoch * iter_per_epoch))) + moe_path = '_moe' if lm_config.use_moe else '' + ckp = f'{save_dir}/pretrain_{lm_config.dim}{moe_path}.pth' + + if isinstance(model, torch.nn.parallel.DistributedDataParallel): + state_dict = model.module.state_dict() + else: + state_dict = model.state_dict() + + torch.save(state_dict, ckp) + model.train() + + +def init_model(): + def count_parameters(model): + return sum(p.numel() for p in model.parameters() if p.requires_grad) + + # model init + model = Transformer(lm_config).to(device) + # moe_path = '_moe' if lm_config.use_moe else '' + # ckp = f'{save_dir}/pretrain_{lm_config.dim}{moe_path}.pth' + # + # state_dict = torch.load(ckp, map_location=device) + # unwanted_prefix = '_orig_mod.' + # for k, v in list(state_dict.items()): + # if k.startswith(unwanted_prefix): + # state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k) + # model.load_state_dict(state_dict, strict=False) + + Logger(f'LLM总参数量:{count_parameters(model) / 1e6:.3f} 百万') + return model + + +def init_distributed_mode(): + if not ddp: return + global ddp_local_rank, DEVICE + + dist.init_process_group(backend="nccl") + ddp_rank = int(os.environ["RANK"]) + ddp_local_rank = int(os.environ["LOCAL_RANK"]) + ddp_world_size = int(os.environ["WORLD_SIZE"]) + DEVICE = f"cuda:{ddp_local_rank}" + torch.cuda.set_device(DEVICE) + + +# torchrun --nproc_per_node 2 1-pretrain.py +# I/O +if __name__ == "__main__": + # ----------------------------------------------------------------------------- + lm_config = LMConfig() + max_seq_len = lm_config.max_seq_len + out_dir = 'out' + epochs = 20 + batch_size = 8 + learning_rate = 1e-4 + device = 'cuda:0' + dtype = 'bfloat16' + save_dir = os.path.join(out_dir) + os.makedirs(save_dir, exist_ok=True) + os.makedirs(out_dir, exist_ok=True) + tokens_per_iter = batch_size * max_seq_len + torch.manual_seed(1337) + device_type = device if "cuda" in device else "cpu" + ctx = ( + nullcontext() + if device_type == "cpu" + else torch.cuda.amp.autocast() + ) + ddp = int(os.environ.get("RANK", -1)) != -1 # is this a ddp run? + ddp_local_rank, DEVICE = 0, "cuda:0" + if ddp: + init_distributed_mode() + device = torch.device(DEVICE) + # ----------------------------------------------------------------------------- + + # -----init dataloader------ + data_path_list = ['./dataset/pretrain_data.bin'] + train_ds = PretrainDataset(data_path_list, max_length=max_seq_len, memmap=True) + train_sampler = DistributedSampler(train_ds) if ddp else None + num_workers = 8 # 可以根据系统的 CPU 核心数来调整 + train_loader = DataLoader( + train_ds, + batch_size=batch_size, + pin_memory=True, + drop_last=False, + shuffle=False, + num_workers=num_workers, + sampler=train_sampler + ) + + # init model + model = init_model() + + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == dtype)) + # optimizer + optimizer = optim.Adam(model.parameters(), lr=learning_rate) + # compile the model + if False and platform.system() != 'Windows' and float(torch.__version__.split('.')[0]) >= 2: + Logger("compiling the model... (takes a ~minute)") + unoptimized_model = model + model = torch.compile(model) + + if ddp: + # Ignore the freqs_cis buffer so that DDP does not broadcast it at + # construction time since NCCL does not support ComplexFloat + model._ddp_params_and_buffers_to_ignore = {"pos_cis"} + model = DistributedDataParallel(model, device_ids=[ddp_local_rank]) + + # training loop + iter_per_epoch = len(train_loader) + for epoch in range(epochs): + train_epoch(epoch) diff --git a/2-eval.py b/2-eval.py new file mode 100644 index 0000000..4ce7759 --- /dev/null +++ b/2-eval.py @@ -0,0 +1,182 @@ +import random +import time + +import numpy as np +import torch +import warnings +from transformers import AutoTokenizer, AutoModelForCausalLM +from model.model import Transformer +from model.LMConfig import LMConfig + +warnings.filterwarnings('ignore') + + +def count_parameters(model): + return sum(p.numel() for p in model.parameters()) + + +def init_model(lm_config): + tokenizer = AutoTokenizer.from_pretrained('./model', + trust_remote_code=True, use_fast=False) + model_from = 2 # 1从权重,2用transformers + + if model_from == 1: + moe_path = '_moe' if lm_config.use_moe else '' + ckp = f'./out/multi_chat/full_sft_{lm_config.dim}{moe_path}.pth' + + model = Transformer(lm_config) + state_dict = torch.load(ckp, map_location=device) + + # 处理不需要的前缀 + unwanted_prefix = '_orig_mod.' + for k, v in list(state_dict.items()): + if k.startswith(unwanted_prefix): + state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k) + + for k, v in list(state_dict.items()): + if 'mask' in k: + del state_dict[k] + + # 加载到模型中 + model.load_state_dict(state_dict, strict=False) + else: + model = AutoModelForCausalLM.from_pretrained("minimind-small", trust_remote_code=True) + + tokenizer = AutoTokenizer.from_pretrained('minimind-small', + trust_remote_code=True, use_fast=False) + model = model.to(device) + + print(f'模型参数: {count_parameters(model) / 1e6} 百万 = {count_parameters(model) / 1e9} B (Billion)') + return model, tokenizer + + +def setup_seed(seed): + random.seed(seed) # 设置 Python 的随机种子 + np.random.seed(seed) # 设置 NumPy 的随机种子 + torch.manual_seed(seed) # 设置 PyTorch 的随机种子 + torch.cuda.manual_seed(seed) # 为当前 GPU 设置随机种子(如果有) + torch.cuda.manual_seed_all(seed) # 为所有 GPU 设置随机种子(如果有) + torch.backends.cudnn.deterministic = True # 确保每次返回的卷积算法是确定的 + torch.backends.cudnn.benchmark = False # 关闭 cuDNN 的自动调优,避免不确定性 + + +if __name__ == "__main__": + # ----------------------------------------------------------------------------- + out_dir = 'out' + start = "" + temperature = 0.7 + top_k = 8 + setup_seed(1337) + # device = 'cpu' + device = 'cuda:0' if torch.cuda.is_available() else 'cpu' + dtype = 'bfloat16' + max_seq_len = 1 * 1024 + lm_config = LMConfig() + lm_config.max_seq_len = max_seq_len + # 对话是否携带历史对话(当前模型没有在连续对话数据集上训练,增大历史上文基本不会有新的问答能力) + contain_history_chat = False + # ----------------------------------------------------------------------------- + + model, tokenizer = init_model(lm_config) + + model = model.eval() + # 推送到huggingface + # model.push_to_hub("minimind") + # tokenizer.push_to_hub("minimind") + + # answer_way = int(input('输入0自动测试,输入1问题测试:')) + answer_way = 0 + stream = True + + prompt_datas = [ + '你知道长江吗?', + '人类的血液主要由哪些成分组成?', + '第一颗人造卫星是哪个国家发射的?', + '你知道杭州有什么美食吗?', + '你知道泰山在哪里吗?', + '地球上最大的动物是什么?', + '地球自转一圈大约需要多少时间?', + '人类最早使用的金属是什么?', + '水的化学分子式是什么?', + '大气层中含量最多的气体是什么?', + '世界上最高的山峰是什么?', + '你知道世界上最深的海沟是什么吗?', + '最早发明印刷术的是哪个国家?', + '万有引力是谁提出的?', + '光合作用的主要原理是什么?', + '你知道大熊猫的主要食物是什么吗?', + '海水为什么是咸的?', + '我们平时喝的牛奶主要含有什么营养成分?', + '一星期有多少天?' + ] + + messages_origin = [] + messages = messages_origin + + i = 0 + while i < len(prompt_datas): + if not contain_history_chat: + messages = messages_origin.copy() + + if answer_way == 1: + prompt = input('[Q]: ') + else: + prompt = prompt_datas[i] + print(f'[Q]: {prompt}') + i += 1 + + messages.append({"role": "user", "content": prompt}) + + # print(messages) + new_prompt = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True + )[-(max_seq_len - 1):] + + x = tokenizer(new_prompt).data['input_ids'] + x = (torch.tensor(x, dtype=torch.long, device=device)[None, ...]) + + answer = new_prompt + + with torch.no_grad(): + res_y = model.generate(x, tokenizer.eos_token_id, max_new_tokens=max_seq_len, temperature=temperature, + top_k=top_k, stream=stream) + print('[A]: ', end='') + try: + y = next(res_y) + except StopIteration: + print("No answer") + continue + + history_idx = 0 + while y != None: + answer = tokenizer.decode(y[0].tolist()) + if answer and answer[-1] == '�': + try: + y = next(res_y) + except: + break + continue + # print(answer) + if not len(answer): + try: + y = next(res_y) + except: + break + continue + + print(answer[history_idx:], end='', flush=True) + try: + y = next(res_y) + except: + break + history_idx = len(answer) + if not stream: + break + + print('\n') + + if contain_history_chat: + assistant_answer = answer.replace(new_prompt, "") + messages.append({"role": "assistant", "content": assistant_answer}) diff --git a/3-full_sft.py b/3-full_sft.py new file mode 100644 index 0000000..459c244 --- /dev/null +++ b/3-full_sft.py @@ -0,0 +1,212 @@ +import os +import platform +import time +import math +import warnings + +import pandas as pd +import torch +import torch.nn.functional as F +import torch.distributed as dist +from contextlib import nullcontext + +from torch import optim +from torch.nn.parallel import DistributedDataParallel +from torch.optim.lr_scheduler import CosineAnnealingLR +from torch.utils.data import DataLoader, DistributedSampler +from transformers import AutoTokenizer, AutoModel +from model.model import Transformer +from model.LMConfig import LMConfig +from model.dataset import SFTDataset + +warnings.filterwarnings('ignore') + + +def Logger(content): + if not ddp or dist.get_rank() == 0: + print(content) + + +def get_lr(it, all): + warmup_iters = 0 + lr_decay_iters = all + min_lr = learning_rate / epochs + + if it < warmup_iters: + return learning_rate * it / warmup_iters + if it > lr_decay_iters: + return min_lr + decay_ratio = (it - warmup_iters) / (lr_decay_iters - warmup_iters) + assert 0 <= decay_ratio <= 1 + coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio)) + return min_lr + coeff * (learning_rate - min_lr) + + +# ------------------------------------------------------------------------------ +def train_epoch(epoch): + start_time = time.time() + for step, (X, Y, loss_mask) in enumerate(train_loader): + X = X.to(device) + Y = Y.to(device) + loss_mask = loss_mask.to(device) + lr = get_lr(epoch * iter_per_epoch + step, epochs * iter_per_epoch) + for param_group in optimizer.param_groups: + param_group['lr'] = lr + + with ctx: + logits = model(X, Y).logits + loss = F.cross_entropy(logits.view(-1, logits.size(-1)), Y.view(-1), ignore_index=0, reduction='none') + loss_mask = loss_mask.view(-1) + loss = torch.sum(loss * loss_mask) / loss_mask.sum() + + # Backward pass + scaler.scale(loss).backward() + + # Unscale gradients and clip them + scaler.unscale_(optimizer) + torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) + + # Update parameters + scaler.step(optimizer) + scaler.update() + + # Zero the gradients + optimizer.zero_grad(set_to_none=True) + + # 打印日志 + if step % 100 == 0: + spend_time = time.time() - start_time + Logger( + 'Epoch:[{}/{}]({}/{}) loss:{:.3f} lr:{:.8f} epoch_Time:{}min:'.format( + epoch, + epochs, + step, + iter_per_epoch, + loss, + optimizer.param_groups[-1]['lr'], + spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60)) + + if (step + 1) % 1000 == 0 and (not ddp or dist.get_rank() == 0): + model.eval() + # torch.save(model.state_dict(), '{}/sft_iter_{}.pth'.format(save_dir, int(step + epoch * iter_per_epoch))) + moe_path = '_moe' if lm_config.use_moe else '' + ckp = f'{save_dir}/full_sft_{lm_config.dim}{moe_path}.pth' + if isinstance(model, torch.nn.parallel.DistributedDataParallel): + state_dict = model.module.state_dict() + else: + state_dict = model.state_dict() + + torch.save(state_dict, ckp) + model.train() + + +def init_model(lm_config): + tokenizer = AutoTokenizer.from_pretrained('./model/minimind_tokenizer') + model_from = 1 # 1从权重,2用transformers + + def count_parameters(model): + return sum(p.numel() for p in model.parameters() if p.requires_grad) + + if model_from == 1: + moe_path = '_moe' if lm_config.use_moe else '' + ckp = f'./out/single_chat/full_sft_{lm_config.dim}{moe_path}.pth' + + model = Transformer(lm_config) + state_dict = torch.load(ckp, map_location=device) + + unwanted_prefix = '_orig_mod.' + for k, v in list(state_dict.items()): + if k.startswith(unwanted_prefix): + state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k) + model.load_state_dict(state_dict, strict=False) + else: + model = AutoModel.from_pretrained('./minimind', trust_remote_code=True) + + Logger(f'LLM总参数量:{count_parameters(model) / 1e6:.3f} 百万') + model = model.to(device) + + return model, tokenizer + + +def init_distributed_mode(): + if not ddp: return + global ddp_local_rank, DEVICE + + dist.init_process_group(backend="nccl") + ddp_rank = int(os.environ["RANK"]) + ddp_local_rank = int(os.environ["LOCAL_RANK"]) + ddp_world_size = int(os.environ["WORLD_SIZE"]) + DEVICE = f"cuda:{ddp_local_rank}" + torch.cuda.set_device(DEVICE) + + +# I/O +if __name__ == "__main__": + # ----------------------------------------------------------------------------- + lm_config = LMConfig() + max_seq_len = lm_config.max_seq_len + out_dir = 'out' + epochs = 19 + gradient_accumulation_steps = 1 + batch_size = 48 + learning_rate = 2e-8 + device = 'cuda:0' + dtype = 'bfloat16' + # dtype = 'float16' + save_dir = os.path.join(out_dir) + os.makedirs(save_dir, exist_ok=True) + tokens_per_iter = gradient_accumulation_steps * batch_size * max_seq_len + os.makedirs(out_dir, exist_ok=True) + torch.manual_seed(1337) + device_type = device if "cuda" in device else "cpu" + ctx = ( + nullcontext() + if device_type == "cpu" + else torch.cuda.amp.autocast() + ) + + ### ddp config + ddp = int(os.environ.get("RANK", -1)) != -1 # is this a ddp run? + ddp_local_rank, DEVICE = 0, "cuda:0" + if ddp: + init_distributed_mode() + device = torch.device(DEVICE) + # ----------------------------------------------------------------------------- + + model, tokenizer = init_model(lm_config) + # -----init dataloader------ + df = pd.read_csv('./dataset/sft_data.csv') + df = df.sample(frac=1.0) + train_ds = SFTDataset(df, tokenizer, max_length=max_seq_len) + train_sampler = DistributedSampler(train_ds) if ddp else None + train_loader = DataLoader( + train_ds, + batch_size=batch_size, + pin_memory=False, + drop_last=False, + shuffle=False, + num_workers=8, + sampler=train_sampler + ) + + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == dtype)) + # optimizer + optimizer = optim.Adam(model.parameters(), lr=learning_rate) + + iter_per_epoch = len(train_loader) + # compile the model + if False and not lm_config.use_moe and platform.system() != 'Windows' and float( + torch.__version__.split('.')[0]) >= 2: + Logger("compiling the model... (takes a ~minute)") + unoptimized_model = model + model = torch.compile(model) # requires PyTorch 2.0 + + if ddp: + # Ignore the pos_cis buffer so that DDP does not broadcast it at + # construction time since NCCL does not support ComplexFloat + model._ddp_params_and_buffers_to_ignore = {"pos_cis"} + model = DistributedDataParallel(model, device_ids=[ddp_local_rank]) + + # training loop + for epoch in range(epochs): + train_epoch(epoch) diff --git a/4-lora_sft.py b/4-lora_sft.py new file mode 100644 index 0000000..936461d --- /dev/null +++ b/4-lora_sft.py @@ -0,0 +1,166 @@ +import os +import platform +import time +import math +import warnings +import torch +import pandas as pd +import torch.nn.functional as F +from contextlib import nullcontext + +from torch import optim +from transformers import AutoTokenizer +from transformers import AutoModelForCausalLM +from peft import get_peft_model, LoraConfig, TaskType +from torch.utils.data import DataLoader +from model.LMConfig import LMConfig +from model.dataset import SFTDataset + +warnings.filterwarnings('ignore', category=UserWarning) + + +def get_lr(it): + warmup_iters = 1000 + lr_decay_iters = 80000 + min_lr = 1e-5 + + if it < warmup_iters: + return learning_rate * it / warmup_iters + if it > lr_decay_iters: + return min_lr + decay_ratio = (it - warmup_iters) / (lr_decay_iters - warmup_iters) + assert 0 <= decay_ratio <= 1 + coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio)) + return min_lr + coeff * (learning_rate - min_lr) + + +# ------------------------------------------------------------------------------ +def train_epoch(epoch): + start_time = time.time() + for step, (X, Y, loss_mask) in enumerate(train_loader): + X = X.to(device) + Y = Y.to(device) + loss_mask = loss_mask.to(device) + lr = get_lr(epoch * iter_per_epoch + step) + for param_group in optimizer.param_groups: + param_group['lr'] = lr + + with ctx: + logits = model(X, Y).logits + loss = F.cross_entropy(logits.view(-1, logits.size(-1)), Y.view(-1), ignore_index=0, reduction='none') + loss_mask = loss_mask.view(-1) + loss = torch.sum(loss * loss_mask) / loss_mask.sum() + + scaler.scale(loss).backward() + + scaler.unscale_(optimizer) + torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) + + scaler.step(optimizer) + scaler.update() + + optimizer.zero_grad(set_to_none=True) + + if step % 100 == 0: + spend_time = time.time() - start_time + print( + 'Epoch:[{}/{}]({}/{}) loss:{:.3f} lr:{:.7f} epoch_Time:{}min:'.format( + epoch, + epochs, + step, + iter_per_epoch, + loss.item(), + optimizer.param_groups[-1]['lr'], + spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60)) + + +def find_all_linear_names(model): + cls = torch.nn.Linear + lora_module_names = set() + for name, module in model.named_modules(): + if isinstance(module, cls): + names = name.split('.') + lora_module_names.add(names[0] if len(names) == 1 else names[-1]) + + if 'lm_head' in lora_module_names: + lora_module_names.remove('lm_head') + return list(lora_module_names) + + +def init_model(): + model_name_or_path = "./minimind" + tokenizer_name_or_path = "./minimind" + tokenizer = AutoTokenizer.from_pretrained(tokenizer_name_or_path, trust_remote_code=True, use_fast=False) + model = AutoModelForCausalLM.from_pretrained(model_name_or_path, trust_remote_code=True).to(device) + + target_modules = find_all_linear_names(model) + peft_config = LoraConfig( + task_type=TaskType.CAUSAL_LM, + r=8, + lora_alpha=16, + lora_dropout=0.1, + inference_mode=False, + target_modules=target_modules + ) + model = get_peft_model(model, peft_config) + model.print_trainable_parameters() + model = model.to(device) + return model, tokenizer + + +# I/O +if __name__ == "__main__": + # ----------------------------------------------------------------------------- + lm_config = LMConfig() + max_seq_len = lm_config.max_seq_len + out_dir = 'out' + epochs = 20 + gradient_accumulation_steps = 1 + batch_size = 16 + learning_rate = 1e-4 + weight_decay = 1e-1 + device = 'cuda:0' + dtype = 'bfloat16' + save_dir = os.path.join(out_dir) + os.makedirs(save_dir, exist_ok=True) + tokens_per_iter = gradient_accumulation_steps * batch_size * max_seq_len + os.makedirs(out_dir, exist_ok=True) + torch.manual_seed(1337) + device_type = device if "cuda" in device else "cpu" + ctx = ( + nullcontext() + if device_type == "cpu" + else torch.cuda.amp.autocast() + ) + # ----------------------------------------------------------------------------- + + model, tokenizer = init_model() + + # -----init dataloader------ + df = pd.read_csv('./dataset/sft_data.csv') + df = df.sample(frac=1.0) + train_ds = SFTDataset(df, tokenizer, max_length=max_seq_len) + train_loader = DataLoader( + train_ds, + batch_size=batch_size, + pin_memory=False, + drop_last=False, + shuffle=False, + num_workers=0, + ) + + scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) + # optimizer + optimizer = optim.Adam(model.parameters(), lr=learning_rate) + iter_per_epoch = len(train_loader) + # compile the model + if False and platform.system() != 'Windows' and float(torch.__version__.split('.')[0]) >= 2: + print("compiling the model... (takes a ~minute)") + unoptimized_model = model + model = torch.compile(model) + + raw_model = model + # training loop + for epoch in range(epochs): + train_epoch(epoch) + model.save_pretrained('minimind') diff --git a/5-dpo_train.py b/5-dpo_train.py new file mode 100644 index 0000000..99314c5 --- /dev/null +++ b/5-dpo_train.py @@ -0,0 +1,71 @@ +import os +os.environ['CUDA_VISIBLE_DEVICES'] = '0' + +import torch +from transformers import TrainingArguments, AutoModelForCausalLM, AutoTokenizer +from trl import DPOTrainer +from peft import get_peft_model, LoraConfig, TaskType +from datasets import load_dataset + +def find_all_linear_names(model): + cls = torch.nn.Linear + lora_module_names = set() + for name, module in model.named_modules(): + if isinstance(module, cls): + names = name.split('.') + lora_module_names.add(names[0] if len(names) == 1 else names[-1]) + + if 'lm_head' in lora_module_names: + lora_module_names.remove('lm_head') + return list(lora_module_names) + + +def init_model(): + device = 'cuda:0' + # Do model patching and add fast LoRA weights + model_name_or_path = "minimind" + tokenizer_name_or_path = "minimind" + model = AutoModelForCausalLM.from_pretrained(model_name_or_path, trust_remote_code=True) + tokenizer = AutoTokenizer.from_pretrained(tokenizer_name_or_path, trust_remote_code=True, use_fast=False) + tokenizer.pad_token = tokenizer.eos_token + target_modules = find_all_linear_names(model) + peft_config = LoraConfig( + task_type=TaskType.CAUSAL_LM, + r=8, + lora_alpha=16, + lora_dropout=0.1, + inference_mode=False, + target_modules=target_modules + ) + model = get_peft_model(model, peft_config) + model.print_trainable_parameters() + model = model.to(device) + return model, tokenizer + + +if __name__ == '__main__': + model, tokenizer = init_model() + training_args = TrainingArguments(output_dir="./minimind_dpo", + per_device_train_batch_size=1, + remove_unused_columns=False) + + ################ + # Dataset + ################ + # 确保路径正确,文件存在 + dataset_path = './dataset/dpo/train_data.json' + + # 加载数据集 + train_dataset = load_dataset('json', data_files=dataset_path) + + dpo_trainer = DPOTrainer( + model, + ref_model=None, + args=training_args, + beta=0.1, + train_dataset=train_dataset['train'], + tokenizer=tokenizer, + max_length=512, + max_prompt_length=512 + ) + dpo_trainer.train() diff --git a/README.md b/README.md index ad997b6..89f593b 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,690 @@ - + +
+
+ “大道至简”
+
+
+
+ "The Greatest Path is the Simplest"
+
+
E X_2$","$P(X_1=3)>P(X_2=3), E X_1 0$ 时, $P(A \cup B \mid C) \leq P(A \mid C)+P(B \mid C)$"
+67,"设$X_{1},X_{2},\cdots,X_{n}$是来自总体$X\sim U(0,8\pi)$的简单样本,$\mathrm{Y}_{i}=\frac{\pi}{4}\sin\left(\frac{1}{8}X_{1}\right)$,则$\frac1n\sum_{i=1}^{n}Y_{i}$依概率收敛于____",$1 / 16$,$1 / 4$,$1 / 8$,$1 / 2$
+68,"设随机变量X和Y相互独立,都服从$[0,b]$上均匀分布,则$E[\min(X,Y)]=$____",\frac{b}{2},b,\frac{b}{3},\frac{b}{4}
+69,"设随机事件A,B的概率均大于0。(1)若A,B互不相容,则它们相互独立.(2)若A,B相互独立,则它们互不相容.(3)若P(A)=P(B)=0.5,则它们互不相容.(4)若P(A)=P(B)=0.5,则它们相互独立.上述结论正确的个数为:____",2个,3个,1个,0个
+70,"设随机变量$X_1,X_2,\ldots,X_n$相互独立,服从同一分布,方差$\sigma^2>0$,$\bar{X}=\frac{1}{n}\sum_{i=1}^nX_i$,则必有____","$cov\left(X_1, \bar{X}\right)=\frac{\sigma^2}{n}$;","$cov\left(X_1, \bar{X}\right)=\sigma^2$",$\mathrm{D}\left(X_1+\bar{X}\right)=\frac{(n+2) \sigma^2}{n}$,$\mathrm{D}\left(X_1-X\right)=\frac{(n+1) \sigma^2}{n}$
+71,"设$X_1,X_2,\cdots,X_{100}$是来自总体$X\sim B(1,p)$的简单样本,则下列结论中不正确的是:____",$\frac{1}{100} \sum_{k=1}^{100} X_k \stackrel{P}{\longrightarrow} p$,"$\sum_{k=1}^{100} X_k \sim B(100, p)$","$\sum_{k=1}^{100} X_k \sim N(100 p, 100 p(1-p))$ (近似)",$P\left\{a<\sum_{k=1}^{100} X_k0$,$a=\frac{1}{t}-1$ 且 $t<\mathbf{1}$,$a>0$ 且 $0 0,P(B\mid A)=1-P(\bar{B}\mid\bar{A})$,则必有____",$P(A \mid B)=P(\bar{A} \mid B)$,$P(A \mid B) \neq P(\bar{A} \mid B)$,$P(\bar{A} \bar{B})=P(\bar{A}) P(\bar{B})$,$P(A B) \neq P(A) P(B)$
+85,"设$A$与$B$为互不相容的事件,且$P(A)>0,P(B)>0$,则下列各式中不正确的是____",$P(\bar{B} \mid A)=0$,$P(A \cap B)=0$,$P(A \cup B)=P(A)+P(B)$,$P(A \cap \bar{B})=P(A)$
+86,"设$\left(X_1,X_2,\cdots,X\right.$,为来自标准正态总体$N(1,9)$的简单随机样本,$\bar{X}$与$S^2$分别为样本均值与样本方差,令$Y=\bar{X}^2-S^2$,则$E(Y)=$____",-2,-8,0,7
+87,设$(X,Y)$的联合概率密度函数为$f(x,y)=Ae^{-x}(x>0,0 4$为来自总体$X$的一个样本,下列$E(X)$估计量中最有效的是____",$\frac{1}{4} \sum_{i=1}^4 X_i$,$\frac{1}{2}\left(X_1+X_2\right)$,$\frac{1}{n} \sum_{i=1}^n X_i$,"$\sum_{i=1}^n C_i X_i$, 其中 $\sum_{i=1}^n C_i=1$ 。"
+140,"设二维随机变量$(X,Y)$在区域$D=\{(x,y):0 0$,$P(X_1^2+X_2^2+X_3^2+X_4^2\le kX_5^2)=\alpha$则k=____","$\frac{1}{4}F_{\alpha}(4,1)$","$\frac{1}{4}F_{1-\alpha}(4,1)$","$4F_{\alpha}(4,1)$","$4F_{1-\alpha}(4,1)$",D
+16,"设$X_1,X_1,\cdots X_8$为来自总体$X\sim N\left(\mu_1,1\right)$的简单样本,$\bar{X},S_1^2$分別是其对应的样本均值与样本方差。$Y_1,Y_1,\cdots,Y_7$为来自总$Y\sim N\left(\mu_2,1\right)$的简单样本,$\bar{Y},S_2^2$分别是其对应的样本均值与样本方差。下列选项正确的是:____",$\sum_{i=1}^8\left(X_i-\mu_1\right)^2+\sum_{i=1}^7\left(Y_i-\mu_2\right)^2 \sim \chi^2(15)$,$E\left(\sum_{i=1}^8\left(X_i-\mu_1\right)^2+\sum_{i=1}^7\left(Y_i-\mu_2\right)^2\right)=15$,$\mathrm{D}(\bar{X}+\bar{Y})=\frac{1}{8}+\frac{1}{7}$,"$\bar{X}-\bar{Y} \sim \mathrm{N}\left(\mu_1-\mu_2, \frac{1}{8}+\frac{1}{7}\right)$",B
+17,"若随机变量X的分布函数为$F(x)=pF_1(x)+qF_2(x)$,其中$F_1(x)$,$F_2(x)$为两个分布函数,常数p,q满足:$p>0$,$q>0$,$p+q=1$,那么X的分布叫作$F_1(x),F_2(x)$的混合分布.设$\mu_1,\mu_2$分别为$F_1(x),F_2(x)$的期望,$\sigma_1^2,\sigma_2^2$分别为$F_1(\mathrm{x})$,$F_2(\mathrm{x})$的方差,则$DX=$____",$p \sigma_1^2+q \sigma_2^2$,$p^2 \sigma_1^2+q^2 \sigma_2^2$,$p \sigma_1^2+q \sigma_2^2+p q\left(\mu_1-\mu_2\right)^2$,$p \sigma_1^2+q \sigma_2^2+p q\left(\sigma_1-\sigma_2\right)^2$,C
diff --git a/ceval/ceval-exam/val/professional_tour_guide_val.csv b/ceval/ceval-exam/val/professional_tour_guide_val.csv
new file mode 100644
index 0000000..4b9049a
--- /dev/null
+++ b/ceval/ceval-exam/val/professional_tour_guide_val.csv
@@ -0,0 +1,30 @@
+id,question,A,B,C,D,answer
+0,《临川先生文集》中的“王临川”用的是____称谓。,别号,官爵,地望,排行,C
+1,广东风味小吃有____。,猫耳朵,五芳斋粽子,双皮奶,虾爆鳝面,C
+2,我国国内卫星通信网正式建成于____。,1980年,1983年,1986年,1988年,C
+3,东汉由西汉王室刘秀创建,建都____。,长安,洛阳,成都,建业,B
+4,对我国名山的描述正确的是____。,泰山有“天下第一山”之称,被列入世界自然与文化双重遗产,黄山有“五岳独秀”之称,被列入世界自然与文化双重遗产,华山最高峰南峰海拔1800米,自古以险闻名遐迩,衡山有七十二峰,三大主峰莲花峰、天都峰、祝融峰都超过1800米,A
+5,中国历史上第一个奴隶制国家政权夏朝建立在____。,晋南,洛阳,安阳,二里岗,A
+6,上海豫园鱼乐榭有一上实下空的墙,遮挡了原来流水较近的短处,产生了源远流长的效果,这是____的神来之笔。,抑景,框景,借景,障景,D
+7,“霸王别姬”是____的代表菜。,苏菜,鲁菜,浙菜,湘菜,A
+8,我国的“四大名砚”中,唯一不用岩石为砚材制作原料的是____。,端砚,歙砚,澄泥砚,洮河砚,C
+9,黑龙江在我国境内的最长支流是____。,松花江,海河,鸭绿江,辽河,A
+10,西周时的商高是见于著述的中国古代第一位____。,农学家,医学家,数学家,天文学家,C
+11,被誉为“四大国宝”的我国一级保护动物是____。,藏羚羊、白唇鹿、白鳍豚、金丝猴,华南虎、白鳍豚、亚洲象、大熊猫,大熊猫、金丝猴、白鳍豚、白唇鹿,金丝猴、东北虎、白唇鹿、亚洲象,C
+12,英国为了打开中国市场,在____发动了鸦片战争,清朝战败。,1780年,1820年,1840年,1860年,C
+13,我国现存最古老的木结构建筑位于____。,山西五台县南禅寺,山西五台县佛光寺,山西五台县塔院寺,山西芮城县广仁王庙,A
+14,____是伦敦的标志性建筑之一,有英国最大的钟。,伦敦塔桥,伊丽莎白塔,圣保罗教堂,海德公园,B
+15,无锡寄畅园因园外惠山的景色而显得更加秀丽。产生这一效果的构景手法是____。,借景,添景,抑景,障景,A
+16,世界旅游组织总部设在____。,旧金山,曼谷,海牙,马德里,D
+17,《梦溪笔谈》被称为“中国科学史上的坐标”,其作者是____。,沈括,祖冲之,徐霞客,吴敬梓,A
+18,北方园林尤以____为代表。,开封,西安,洛阳,北京,D
+19,我国成功发射的世界首颗量子科学实验卫星叫____。,“墨子号”,“玉兔号”,“嫦娥号”,“天宫号”,A
+20,我国最大的丛书是____。,《吕氏春秋》,《永乐大典》,《古今图书集成》,《四库全书》,D
+21,郁金香真正的原产地是____。,荷兰,土耳其,法国,意大利,B
+22,清乾隆年间“四大徽班进京”对京剧艺术的形成影响深远,四大徽班中最早进京演出并大获成功的是____。,和春班,四喜班,三庆班,春台班,C
+23,李求真在“万里晴空,几片闲云浮海角;一湾碧水,八方游子恋天涯”联语中巧妙地嵌入了“海角天涯”四个字,它是____省的旅游名胜。,广东,广西,贵州,海南,D
+24,下列园林建筑中,____形式优美且不讲究对称布局。,榭,轩,亭,廊,B
+25,元大都是按照____传统都城的布局建造的。,汉族,藏族,蒙古族,满族,A
+26,被称为“土族梁祝”的叙事长诗是____。,《牛达的传说》,《汗庆格尔》,《祁家延西》,《拉仁布与吉门索》,D
+27,下列风物特产中,属于韩国风物特产的是____。,珍珠,燕窝,高丽参,香料,C
+28,西汉的____,包括《素问》和《灵枢》两部分,奠定了传统中医学理论基础,是我国现存最早的一部医书。,《黄帝内经》,《伤寒杂病论》,《脉经》,《千金方》,A
diff --git a/ceval/ceval-exam/val/sports_science_val.csv b/ceval/ceval-exam/val/sports_science_val.csv
new file mode 100644
index 0000000..3661c76
--- /dev/null
+++ b/ceval/ceval-exam/val/sports_science_val.csv
@@ -0,0 +1,20 @@
+id,question,A,B,C,D,answer
+0,在极限强度运动中,肌肉中的ATP和CP在多少秒内就几乎耗竭____,15,30,10,20,C
+1,决定VO2max的外周机制是____,肌纤维组成,有氧代谢能力,肌组织利用氧的能力,氧运输系统的机能,C
+2,“极点”产生早晚与____,年龄无关,训练程度无关,气候条件无关,教练员无关,D
+3,优秀运动员全程性多年训练过程中,训练负荷始终保持在高水平区间起伏的是____,基础训练阶段,专项提高阶段,最佳竞技阶段,竞技保持阶段,C
+4,下列不是准备活动的作用的是____,调节赛前状态,缩短进入工作状态,减轻“极点”程度,加速运动疲劳的恢复,D
+5,腿部肌肉中快肌纤维百分组成占优势的人,较适宜从事的运动项目是____,800m跑,1 500m跑,100m跑,1 500m游泳,C
+6,运动时,机体工作能力逐步提高是因为____,物理惰性和植物性功能惰性,运动器官功能惰性和物理惰性,植物性功能惰性和运动器官功能惰性,物理惰性和生理惰性,D
+7,课外运动竞赛的主要特点有竞争性、集体性与教育性、多层次与群众性以及____,知识性与协作性,生理性与心理性,趣味性与娱乐性,公正性与表现性,C
+8,西周的“国学”和“乡学”的教学内容为____,宗教和军事,习射及传习多种武艺,礼、乐、射、御、书、数,“五项竞技”,C
+9,能够导致氧解离曲线右移的情况是____,血液中PCO2增高,血液中PCO2降低,血液中pH值增高,血液中PN2张力增高,A
+10,下面不属于克服自身体重的练习是____,引体向上,倒立推起,使用拉力器,纵跳,C
+11,柔道项目中的“得意技”指的是____,基本技术,特长技术,高难度技术,全面技术,B
+12,关于糖的分解代谢,下列说法错误的是____,在不需要氧的情况下,糖进行无氧酵解,反应在细胞浆中进行,糖进行无氧酵解时,能量利用率很低,在氧气供应充足时,肌肉中的乳酸可以再转变为葡萄糖进一步氧化供能,葡萄糖或糖原生成丙酮酸是有氧和无氧供能的共同途径,C
+13,下列能最好评价肺通气功能的指标是____,肺通气量,肺活量,补吸气量,时间肺活量,D
+14,从运动员竞技能力的决定因素看,下列项目对运动员心理能力要求最高的是____,游泳,跳水,射箭,摔跤,C
+15,乳酸阈可用来评定机体____,无氧能力,有氧能力,血乳酸能力,ATP—CP系统能力,B
+16,运动员负荷量度临界值的大小受教育程度、竞技水平及健康等因素的影响,因此在训练中需要____,正确处理负荷与恢复的关系,正确理解训练负荷构成,科学动态探求负荷量度临界值,对运动员进行区别对待,C
+17,同为球类项目,篮球与足球对运动员身体形态、素质、技战术的要求却大不相同,主要是因为____,运动员个体的不同,教练员执教能力的不同,各项目训练条件的不同,专项竞技能力的不同,D
+18,影响血红蛋白氧饱和度的最主要因素是____,PO2,血液pH值,PCO2,血液的温度,A
diff --git a/ceval/ceval-exam/val/tax_accountant_val.csv b/ceval/ceval-exam/val/tax_accountant_val.csv
new file mode 100644
index 0000000..68e15d2
--- /dev/null
+++ b/ceval/ceval-exam/val/tax_accountant_val.csv
@@ -0,0 +1,50 @@
+id,question,A,B,C,D,answer
+0,进行税务咨询服务的核心是____。,弄清咨询问题所涉及的税种,收集咨询问题相关的税收政策文件,分析税收政策适用条款,根据需要作必要的沟通说明,C
+1,FY公司与FC机械厂均为国有企业,合资设立A有限责任公司(以下简称“A公司”),出资比例为30%与70%。下列有关A公司董事会组成的说法中,不符合规定的是____。,董事会成员中应当有公司职工代表,董事张某任期内辞职,在新选出董事就任前,张某仍应履行董事职责,A公司董事长可由公司章程规定由小股东FY公司派人担任,FY公司和FC机械厂可通过公司章程约定不按出资比例分红,B
+2,下列关于预约定价安排管理的表述中,正确的是____。,预约定价安排执行期满后自动失效,企业申请续签的,应当在预约定价安排执行期满之日前30日内向税务机关提出续签申请,企业申请双边预约定价安排的,应及时向省级税务机关提出谈签意向,预约定价安排适用于自企业提交正式书面申请年度当年起10个连续年度的关联交易,在预约定价安排执行期内,税务机关应当每年监控企业执行预约定价安排的情况,D
+3,下列各项行为中,应征收个人所得税的是____。,离婚析产分割房屋产权,军人的转业费、复员费、退役金,按照国家统一规定发放的退休费、离休费,个人购买福利彩票,一次中奖收入12000元,D
+4,某酒厂为增值税一般纳税人,2020年10月发放1吨自制白酒作为职工福利,同类白酒不含税售价50000元/吨,成本价35000元/吨。该酒厂上述业务当月应纳消费税____元。,7700,8700,10000,11000,D
+5,下列各项中,属于土地增值税征税范围的是____。,房地产出租,房地产评估增值,房地产的代建房行为,合作建房后,建成后转让的,D
+6,针对查账的顺序不同,纳税审查的方法可分为____。,顺查法和逆查法,详查法和抽查法,核对法和查询法,比较分析法和控制计算法,A
+7,南和公司因长期不能清偿到期债务,向人民法院申请破产。东尚公司是南和公司的债权人,下列与东尚公司有关的事项中,正确的是____。,债权还有2个月到期,东尚公司不能申报破产债权,东尚公司可以口头向管理人申报债权,东尚公司申报债权后,就可以行使债权人的权利,东尚公司申报债权时需要提供债权申请书、债权证据材料等内容,D
+8,对于不符合收入准则规定的合同成立的条件,企业将已收取客户的对价确认为收入的条件为____。,开具增值税专用发票,不再负有向客户转让商品的剩余义务,且已向客户收取的对价无需退回,具有商业实质,商品已经发出,B
+9,关于税务登记的说法,错误的是____。,一般纳税人资格认定的权限,在县(市、区)税务局或同级别的税务分局,纳税人应当向其机构所在地主管税务机关申请一般纳税人资格认定,年应税销售额达到一般纳税人标准的纳税人,未申请办理一般纳税人手续的,应按销售额依照增值税税率计算应纳税额,可以抵扣进项税额,但不得使用增值税专用发票,纳税人应在领取《税务登记证》副本后和申报纳税之前,申请税种认定登记,C
+10,下列各项中,属于非相关成本的是____。,机会成本,重置成本,差额成本,沉没成本,D
+11,根据《行政诉讼法》规定,下列关于行政诉讼二审程序的说法中,错误的是____。,二审法院可以不开庭审理,二审法院审理上诉案件,一般应当在收到上诉状之日起6个月内作出终审判决,当事人不服一审判决提起上诉的,应当在判决书送达之日起15日内提起,二审法院审理上诉案件时,应当对原审法院的裁判和被诉行政行为进行全面审查,B
+12,甲公司为工业企业,属于增值税一般纳税人。2019年取得主营业务收入为2000万元,本期发生现金折扣10万元,增值税销项税额为260万元;应收账款账户期初余额为600万元,期末余额为900万元,坏账准备的期初余额为10万元,期末余额为30万元;预收账款账户期初余额为100万元,期末余额为20万元;本期收到存货抵债减少应收账款40万元,本期发生不附追索权票据贴现利息5万元。假定不考虑其他因素,甲公司2019年度现金流量表中“销售商品、提供劳务收到的现金”项目的金额为____万元。,1825,1855,1860,1880,A
+13,下列说法符合律师事务所及其从业人员个人所得税征收规定的是____。,兼职律师从律师事务所取得工资、薪金性质的所得,事务所在代扣代缴其个人所得税时,应先扣除税法规定的费用扣除标准,计算律师事务所经营所得时,出资律师本人的工资、薪金不得扣除,律师个人承担的按照律师协会规定参加的业务培训费用,不得扣除,受雇于律师事务所的律师从事务所取得的分成收入,应单独作为一个月的工资、薪金,扣除办案费用后缴纳个人所得税,B
+14,申请人、第三人可以委托代理人参加税务行政复议,但是应当向行政复议机构提交授权委托书。下列各项中,不属于授权委托书应当载明的内容的是____。,委托事项,委托权限,委托期限,委托结果,D
+15,以下项目在计算土地增值税时,不得扣除成本费用是____。,建成后产权属于全体业主的会所,建成后无偿移交派出所用于办公的房屋,建成后有偿出售的停车场,建成后待售出租的商业用房,D
+16,下列关于编制银行存款余额调节表的表述中,正确的是____。,银行对账单上的金额,反映了企业可以动用的银行存款实有数额,对于未达账项,需要对企业和银行各自提供的银行存款余额进行调整,银行存款余额调节表用来核对企业和银行的记账有无错误,并作为记账依据,调节后银行存款日记账余额与银行对账单余额一定相等,B
+17,下列各项符合房产税规定的是____。,更换房屋附属设施和配套设施的,其更新价值计入房产原值,但不扣减原来相应旧设备和设施的价值,对居民住宅区内业主共有的经营性房产,自营的不征收房产税,对于与地上房屋相连的地下建筑,应将地下部分与地上房屋分别按照地上与地下建筑物的规定计算征收房产税,出租的地下建筑,按照出租地上房屋建筑的有关规定计算征收房产税,D
+18,下列各项中,不属于增量预算应遵循的假定是____。,以现有业务活动和各项活动的开支水平,确定预算期各项活动的预算数,预算费用标准必须进行调整,企业现有各项业务的开支水平是合理的,在预算期予以保持,企业现有业务活动是合理的,不需要进行调整,B
+19,下列有关或有事项的表述中,正确的是____。,清偿因或有事项而确认的负债所需支出全部或部分预期由第三方补偿时,补偿金额在很可能收到时才能作为资产单独确认,对于或有事项既要确认或有负债,也要确认或有资产,对固定资产计提折旧不属于或有事项,或有事项应确认为预计负债,C
+20,关于增值税的销售额,下列说法不正确的是____。,劳务派遣服务,可以选择差额纳税,航空运输企业的销售额不包括代收的机场建设费,旅游服务,一律以取得的全部价款和价外费用为销售额,经纪代理服务,以取得的全部价款和价外费用,扣除向委托方收取并代为支付的政府性基金或者行政事业性收费后的余额为销售额,C
+21,根据行政处罚法律制度的规定,下列关于处罚与教育相结合原则的说法中,正确的是____。,处罚只是手段而不是目的,处罚与教育相结合意味着可以以罚代刑,行政机关未责令当事人限期改正违法行为即作出行政处罚的,该行政处罚程序违法,行政处罚行为无效,行政机关未责令当事人限期改正违法行为即作出行政处罚的,该行政处罚程序不违法,但是该处罚行为为可撤销的行政行为,A
+22,甲公司2019年3月31日发现2018年度多计管理费用200万元,并进行了2018年企业所得税申报,甲公司适用企业所得税税率25%,并按净利润的10%提取法定盈余公积。假设甲公司2018年度财务报表于2019年3月10日对外报出,且当年度企业所得税申报的应纳税所得税额大于零,则下列甲公司对此项重要前期差错进行更正的会计处理中正确的是____。,调减2019年度当期管理费用200万元,调增2019年当期未分配利润150万元,调减2019年年初未分配利润135万元,调增2019年年初未分配利润135万元,D
+23,购置新建房的城镇土地使用税纳税义务发生时间为____。,自房屋交付使用之次月起,自办理房产证之次月起,自签订房屋买卖合同之次月起,自房屋竣工验收之次月起,A
+24,甲公司2019年年度财务报告经董事会批准于2020年4月20日报出。甲公司在2020年1月1日至4月20日之间发生的下列事项中,属于资产负债表日后调整事项的是____。,2020年3月10日,法院判决某项诉讼败诉,并需支付赔偿金额80万元,甲公司在2019年年末已经确认预计负债65万元,2020年2月10日发生产品销售退回,该批产品系2020年1月对外销售,2020年2月18日董事会提出资本公积转增资本方案,2020年3月18日公司仓库发生火灾导致存货部分毁损,A
+25,下列关于外币交易会计处理的表述中,错误的是____。,外币交易在初始确认时,可以采用按照系统合理的方法确定的、与交易日即期汇率近似的汇率折算,资产负债表日,对于外币货币性项目应当根据汇率变动计算汇兑差额作为财务费用,无需再计提减值准备,外币交易应当在初始确认时,采用交易发生日的即期汇率或近似汇率将外币金额折算为记账本位币金额,资产负债表日,对以历史成本计量的外币非货币性项目,仍采用交易发生日的即期汇率折算,不改变记账本位币金额,B
+26,下列消费品,属于消费税征税范围的是____。,合成宝石首饰,洗发水,大客车,轮胎,A
+27,下列关于车船税的说法中,正确的是____。,拖拉机属于车船税的征收范围,扣缴义务人代扣代缴车船税的,车辆登记地主管税务机关不再征收,境内单位和个人将船舶出租到境外的,不征收车船税,客货两用车依照乘用车的标准计征车船税,B
+28,在税务行政复议中,不可以达成和解和调解的情形是____。,行政奖励,行政审批,确定应税所得率,核定税额,B
+29,申请人和被申请人在行政复议机关作出行政复议决定以前可以达成和解,行政复议机关也可以调解,下列选项中不可以和解与调解的是____。,确定应税所得率,行政赔偿,行政奖励,征收滞纳金,D
+30,下列关于售后回购交易的会计处理符合企业会计准则规定的是____。,企业因存在与客户的远期安排而负有回购义务或企业享有回购权利的,回购价格低于售价,应当视为租赁交易,企业到期未行使回购权利的,应当在该回购权利到期时终止确认金融负债,但无需确认收入,企业负有应客户要求回购商品义务的,客户具有行使该要求权重大经济动因的,企业应当将售后回购作为融资交易,企业负有应客户要求回购商品义务的,客户不具有行使该要求权重大经济动因的,应当将其作为正常销售交易,A
+31,M公司资金周转出现困难,其法定代表人甲向好友乙借款100万元,甲把自己的宝马汽车抵押给乙,抵押合同中约定若甲不能按时还钱,甲的宝马汽车归乙所有。下列说法正确的是____。,该抵押未登记,乙的抵押权未生效,甲、乙的约定无效,甲、乙的约定经登记才有效,甲应把自己的汽车交付给乙,抵押权才生效,B
+32,甲、乙、丙成立一家科贸有限公司,约定公司注册资本100万元,甲、乙、丙各按20%、30%、50%的比例出资。甲、乙缴足了出资,丙仅实缴30万元。公司章程对于红利分配没有特别约定。当年年底公司进行分红。对此,下列说法中正确的是____。,丙只能按30%的比例分红,应按实缴注册资本80万元,由甲、乙、丙按各自的实际出资比例分红,由于丙违反出资义务,其他股东可通过决议取消其当年分红资格,丙有权按50%的比例分红,但应当承担未足额出资的违约责任,B
+33,应税固体废物环境保护税的计税依据是____。,固体废物的综合利用量,固体废物的排放量,固体废物的产生量,固体废物的贮存量,B
+34,某卷烟批发企业在2020年10月发生下列业务:批发销售给卷烟零售企业卷烟10标准箱,取得不含税收入150万元;批发销售给卷烟批发商卷烟5标准箱,取得不含税收入65万元。该企业当月应纳消费税____万元。,16.5,16.75,23.65,24.03,B
+35,关于财产拍卖的个人所得税处理,下列说法正确的是____。,作者将自己的文字作品手稿原件拍卖取得的所得,按“稿酬所得”项目计算缴纳个人所得税,计算个人财产拍卖的应纳税所得额时,纳税人实际支付的拍卖费不得扣除,拍卖祖传收藏的财产,可以税前扣除的财产原值为其收藏该拍卖品而发生的费用,经认定的海外回流文物的财产原值无法确定的,按转让收入的3%征收率计税,C
+36,某县生猪屠宰主管部门强制该县50户养猪农民养殖1号猪,并对其他品种的生猪将不予屠宰。对于此行为,下列说法正确的是____。,由于该行为属于普遍约束力的决定、命令,属于行政诉讼不受理的案件范围,该行为是行政机关为作出行政行为而实施的过程性行政行为,因此不具可诉性,该行为是具体行政行为,具有可诉性,该行为是国家行为,属于行政诉讼不受理的案件范围,C
+37,下列各项中,不属于税务行政诉讼目的的是____。,维护和监督税务机关依法行使行政职权,保证税法的公平、公正,保护纳税人、扣缴义务人等当事人的合法权益,保证人民法院正确、及时审理税务行政案件,B
+38,下列支出不能作为长期待摊费用的是____。,固定资产的大修理支出,租入固定资产的改建支出,外购房屋发生的装修费用,已足额提取折旧的固定资产的改建支出,C
+39,下列凭证中,需要计算缴纳印花税的是____。,无息、贴息贷款合同,新设立的资金账簿,财产所有人将财产赠给学校所立的书据,施工单位分包给其他施工单位的分包合同,D
+40,甲公司某零件年需要量为2000件,每次订货成本为30元,单位储存成本为0.75元/件。按照经济订货量进货,下列计算结果中错误的是____。,经济订货量为400件,年订货次数为5次,总订货成本为300元,与进货批量相关的总成本为300元,C
+41,关于土地增值税的清算,下列说法错误的是____。,销售合同所载商品房面积与实际测量面积不一致并在清算前已补或退房款的,在计算土地增值税时应予调整,未全额开具商品房销售发票的,按照销售合同所载金额及其他收益确认收入,未开具商品房销售发票的,按照实际收取金额确认收入,已全额开具商品房销售发票的,按照发票所栽金额确认收入,C
+42,2020年5月10日,税务机关在检查某公司的纳税情况过程中,发现该公司2019年的业务存在关联交易,少缴纳企业所得税30万元。该公司于2020年5月31日补缴了该税款,并按规定提供了同期资料及有关资料。已知2019年12月31日中国人民银行公布的一年期人民币贷款年利率为6%。税务机关对该公司补缴税款应加收利息____万元。,1.8,1.95,3.3,3.6,A
+43,关于消费税从价定率计税销售额,下列说法正确的是____。,金银首饰包装费不计入计税销售额,消费税计税销售额包括增值税,白酒包装物押金收取时不计入计税销售额,高档化妆品品牌使用费应计入计税销售额,D
+44,2020年10月,为响应环保节能号召,陈某从汽车4S店(增值税一般纳税人)购买一辆新能源汽车,支付不含税价款150000元。另支付汽车4S店代办保险费2000元,代办车辆牌照费300元,代收款项4S店未开具发票。陈某应纳车辆购置税____元。,0,10500,15000,15203.54,A
+45,某供热企业为增值税一般纳税人,2020年10月取得不含税供热收入860万元,其中向居民个人收取120万元,当月外购原材料取得增值税专用发票注明税额70万元。该企业2020年10月可以抵扣的进项税额为____万元。,15.13,24.9,28.94,60.23,D
+46,境外旅客购物离境退税的方式包括现金退税和银行转账退税两种方式。自行选择退税方式时,退税额应未超过____元。,500,1000,5000,10000,D
+47,下列转让定价方法中,适用于所有关联交易的是____。,可比非受控价格法,成本加成法,交易净利润法,利润分割法,A
+48,某企业为增值税一般纳税人,2021年6月从某花木栽培公司手中购入花卉1100盆,取得的专用发票上注明价款为110580元。该企业将1/4用于赠送给某节日庆典,其余全部卖给客户取得产品不含税销售额705000元。则该企业当月应纳增值税税额为____元。,59442,91236.2,107824.6,74647.8,D
diff --git a/ceval/ceval-exam/val/teacher_qualification_val.csv b/ceval/ceval-exam/val/teacher_qualification_val.csv
new file mode 100644
index 0000000..d75bf8f
--- /dev/null
+++ b/ceval/ceval-exam/val/teacher_qualification_val.csv
@@ -0,0 +1,45 @@
+id,question,A,B,C,D,answer
+0,编写中小学教科书的直接依据是____。,《中华人民共和国教育法》,课程计划,课程标准,课程表,C
+1,下列关于课程的三种文本表现形式说法正确的是____,课程计划是由当地教育主管部门制订的,课程标准是依据课程计划制定的,课程标准的核心是实施建议,教材编写的基本方式有直线式、螺旋式、交叉式,B
+2,悦悦是一名右耳失聪的残疾儿童,活动课上有时会听不清楚周老师所讲的内容,因此经常提问题。对此,周老师应当采取的措施是____。,给予悦悦更多的帮助和指导,指导家长带悦悦回家自学,建议家长将悦悦转到特殊幼儿园,照顾大多数幼儿,不理会悦悦,A
+3,内流河也称“内陆河”,是指没有流入海洋的河流,大多分布在大陆内部干燥地区,上游降水或冰雪融水为其主要补给水源,最终消失于沙漠或注入内陆湖泊。下列中国内流河中,最长的是____。,塔里木河,柴达木河,尼雅河,疏勒河,A
+4,学校规定学生不能烫染头发,但是小文为了彰显个性,在假期把头发染成了棕色。面对小文的情况,教师应该怎样处理?____,年轻人追求个性是合情合理的,应该宽容对待,违反学校的校规,应该严格处分,强制要求小文将头发颜色染回来才可以进校门,探明小文违反校规的原因,并对其进行劝导和教育,D
+5,张老师根据自己班级的情况,为解决班级内部班干部的人际关系问题,建立和谐融洽的班级氛围,自主开发了“和谐人际”的班级课程,这体现了教师____。,是教育教学的研究者,是课程的建设者和开发者,是学生学习的促进者,是社区型的开放教师,B
+6,刘老师工作很负责,学生在学校出现一点问题他就会与家长联系,在与家长沟通时他经常以前辈的姿态对待家长,对家长的教育方式指指点点。刘老师的做法____。,正确,老师就应该与家长经常沟通,正确,老师的经验比家长丰富,应该多指导家长,不正确,教师没有权利指导家长,不正确,教师应该与家长建立平等的沟通关系,尊重家长的人格,D
+7,在古代印度,有一户人家经营一家棉布店销售自己手工制作的衣服。你认为这户人家属于哪个等级?____,婆罗门,刹帝利,吠舍,首陀罗,C
+8,“小型分散,便于开展多种多样的活动,满足学生不同的兴趣、爱好,发展学生的才能,使学生得到更多的学习和锻炼的机会。”这种课外活动的形式是____。,科技活动,学科活动,个人活动,小组活动,D
+9,小红每天晚上临睡前都要多次反复检查自己的书包,确保带齐了第二天需要用的教材和文具。她明知道没有这个必要,但就是控制不住。她可能出现了____。,抑郁症,焦虑症,强迫症,恐惧症,C
+10,国家管理和评价课程的基础是____。,课程计划,课程标准,教学目标,教育目的,B
+11,儿童坚持性发生明显质变的年龄约在____,3~4岁,4~5岁,5~6岁,6岁以后,B
+12,《红楼梦》中人物众多、关系繁杂。为了帮助读者阅读,许多红学爱好者都在网络上发布了自己整理制作的主要人物关系图。这属于____。,纲要策略,精细加工策略,资源管理策略,监控策略,A
+13,学期结束时,班主任王老师会对学生思想品德的发展变化情况进行评价。这项工作属于____。,工作总结,工作计划,操行评定,建立学生档案,C
+14,人们常说:“教学有法而教无定法。”这反映了教师的劳动具有____。,连续性,示范性,长期性,创造性,D
+15,县级以上地方各级人民代表大会是县级以上地方国家权力机关,其职权不包括____。,改变或撤销本级人大常务委员会不适当的决定,选举并有权罢免本级人民法院院长,批准本行政区域内的预算执行情况的报告,决定并宣布下一级行政区城进入紧急状态,D
+16,在心理健康课上,同一批学生在第二次进行同样内容的人格测验时获得的分数与上次测验差别较大。这说明该测验存在的问题是____。,信度问题,效度问题,难度问题,区分度问题,A
+17,李老师在教学生区分形近字“渴”“竭”“碣”“谒”时,将四个字相同的右半部分用白色粉笔写出,相异的左半部分用彩色粉笔写出。李老师运用了知觉的____。,整体性,选择性,理解性,恒常性,B
+18,"兰兰学会走路后,就要很喜欢尝试自己穿衣、吃饭、捡东西,喜欢探索周围世界。按照埃里克森人格发展阶段理论,兰兰所处的发展阶段是____",信任对怀疑,自立对羞怯,主动感对内疚感,勤奋感对自卑感,B
+19,杨老师在教授生字词的过程中发现部分学生有缺笔少画的现象,于是他把“小学生缺笔少画现象的原因及对策研究”作为研究课题,拟订相应的研究计划,在工作中收集、整理相关资料并实施教学措施,最后根据反馈信息调整教学方案。这种研究方法属于____。,教育行动研究法,教育实验法,教育叙事研究法,个案研究法,A
+20,小青的数学成绩不好,她认为这是因为自己脑子笨,不是学数学的料。她的这种归因属于____。,内部、稳定,不可控的归因,外部、稳定、可控的归因,内部、不稳定,可控的归因,外部,不稳定,不可控的归因,A
+21,中小学教科书不同于其他任何书籍的基本特点是内容的____。,准确性,示范性,新颖性,基础性,D
+22,王老师在课堂上给学生演示了与知识点有关的几个实验。这属于____。,实物直观,模象直观,言语直观,思维直观,A
+23,"在Excel中,单元格A1, A2, A3中的内容依次为数值1,2,3,单元格A4中的内容为字符前添加了英文单撇号“,”的文本字符“3”,在单元格A5的编辑栏输入公式“=COUNT( A1:A4) +12”并点击回车键,A5单元格的内容为____。",15,21,12,18,D
+24,唐朝时形成了“父教其子,子教其弟”“五尺童子耻不言文墨焉”的社会风尚,它的形成主要得益于____。,社会经济的繁荣,科举制度的推行,学校体系的完备,三省六部制的确立,B
+25,教导处的刘老师抓到两名学生藏在厕所里偷偷抽烟,于是把他们叫到办公室,慢悠悠地点燃了一根香烟,准备耐心细致地给他们做思想工作。对此,以下说法错误的是____。,刘老师既禁止学生抽烟,又能耐心劝导,严慈相济,真正做到了关爱学生,刘老师要求学生不要抽烟,却在学生面前抽烟,违背了为人师表的要求,刘老师的抽烟行为与他教导学生不能抽烟的言词相悖,很容易损害自己的威信,刘老师的行为表明教师队伍中存在一些教师需要对其加强师风师德建设的,A
+26,小班幼儿看木偶剧表演时,看到“老虎”会感到害怕。这说明幼儿的____,想象脱离现实,想象与现实混淆,想象容易受情绪影响,想象内容零散,B
+27,有的成语与历史人物密切相关。下列选项中,与“狡兔三窟”相关的历史人物是____。,管仲与齐桓公,毛遂与平原君,冯谖与孟尝君,曹刿与鲁庄公,C
+28,王浩同学活动过多、注意力不集中、冲动行为多。这种心理障碍可能是____。,多动综合征,学习困难综合征,儿童厌学症,儿童强迫行为,A
+29,在对班级学生进行教育时,班主任李老师引导学生对自己每日的学习、行为进行反省。李老师主要运用的德育方法是____。,自我修养法,榜样示范法,实践锻炼法,情感陶冶法,A
+30,在讲解方程时,王老师先讲一元一次方程,再讲二元一次方程,然后讲一元二次方程,逐步加深难度。这种教学方式所遵循的原则是____。,理论联系实际原则,启发性原则,循序渐进原则,巩固性原则,C
+31,近代原子核物理学之父是____。,普朗克,卢瑟福,玻尔,霍金,B
+32,很多人因为有了受教育的机会而得到了和父辈完全不同的人生发展机遇。这说明教育在人的发展中起到____。,辅助作用,决定作用,次要作用,主导作用,D
+33,下面是中国古代四大名著中的人物与情节,其中搭配不当的一项是____。,鲁智深——倒拔垂杨柳,孙悟空——大闹天宫,周瑜——三顾茅庐,刘姥姥——进大观园,C
+34,找规律填数字是一项很有趣的活动,特别锻炼观察和思考能力。下列选项中,填入数列“1、7、8、57、____、26050”空缺处的数字,符合该组数字排列规律的是____。,456,457,458,459,B
+35,教育自身的许多规律,是人类长期教育实践认识的结果,它们不会因政治经济制度和其他文化的发展而过时,更不会随时代的发展而被否定。这说明教育具有____。,历史性,永恒性,阶级性,相对独立性,D
+36,高中毕业会考是一种达标考试,属于____。,定量评价,相对性评价,形成性评价,绝对性评价,D
+37,下列选项中,与“图书”和“音乐书”的逻辑关系相同的一组是____。,“钢笔”和“铅笔”,“蛋糕”和“香油”,“水果”和“西瓜”,“白菜”和“黄瓜”,C
+38,语文教师裴老师每天下课后都会对自己一天的工作进行总结反思,并记录下来。这属于布鲁巴奇反思方法中的____。,反思日记,详细描述,交流讨论,行动研究,A
+39,以下关于幼儿有意注意发展的表述,不正确的是____,幼儿有意注意发展受大脑发育水平局限,幼儿有意注意的发展水平较低,无法依靠活动和操作来维持,幼儿在幼儿园需要遵守各种行为规则,完成各项任务,这都需要幼儿形成或发展有意注意,教师在组织活动时,要求幼儿保持注意的对象应该是幼儿认知范围以内或幼儿易于理解的事物,B
+40,某幼儿园根据幼儿的发展情况将班级分为快班、中班和慢班。对于快班的幼儿安排大量优秀师资和先进设备,而对于慢班的幼儿则给予较少的优良教育资源。该幼儿园的做法违背了素质教育内涵中的____。,以提高国民素质为基本宗旨,面向全体幼儿,促进幼儿全面发展,促进幼儿个性发展,B
+41,作为古埃及文明的象征之一,____既寄托了古埃及人对死后重生的向往,又证明了新一代法老王权统治的神圣不可侵犯,充分显示了古埃及人的高度智慧和精湛的建筑艺术。,金字塔,帕特农神庙,圆形竞技场,麦加清真寺,A
+42,在太阳系的八大行星中,质量最大和最小的行星分别是____。,木星;水星,火星;地球,金星;水星,土星;天王星,A
+43,据调查,教师对学生拳打脚踢的情况现在已经较少存在,取而代之的是“心罚”。比如,对于成绩不好的学生罚做题目、罚抄单词一百遍。教师这样的行为____。,是正确的,教育中适当的惩罚是必不可少的,是正确的,教师没有侵犯学生的身体健康,是不正确的,教师没能做到依法执教,是不正确的,教师没能做到团结合作,C
diff --git a/ceval/ceval-exam/val/urban_and_rural_planner_val.csv b/ceval/ceval-exam/val/urban_and_rural_planner_val.csv
new file mode 100644
index 0000000..83db14a
--- /dev/null
+++ b/ceval/ceval-exam/val/urban_and_rural_planner_val.csv
@@ -0,0 +1,47 @@
+id,question,A,B,C,D,answer
+0,对于固体污染物的控制规划内容,不够突出的是____。,电子污染物,生活垃圾,医疗废物,工业固体废物,A
+1,竖向设计设计标高中,当建筑物无进车道时,一般室内地坪比室外地坪面高出____,0.30~0.90m,0.45~0.60m,0.25~0.30m,0.25~0.35m,B
+2,城市总体规划的强制性内容,在防灾方面没涉及的灾害是____。,洪灾,震灾,涝灾,火灾,C
+3,地下电力缆保护区的宽度为地下电力电缆线路地面标桩两侧各____所形成两平行线内区域。,0.5m,0.75m,1.0m,1.5m,B
+4,下列城市全部由国务院公布为历史文化名城的是____,延安、淮安、泰安、瑞安、雅安,金华、银川、同仁、铁岭、无锡,韩城、聊城、邹城、晋城、塔城,歙县、寿县、祁县、浚县、代县,D
+5,《村庄整治技术导则》中提出,对于“空心村”,在住房制度上提出的政策是____。,拆除已坍塌的房屋,一户一宅,迁村并点,宅基地向村中心集中,B
+6,下列不属于村庄规划的具体内容的是____。,社会经济规划,道路交通规划,绿化景观规划,市政规划,A
+7,居住区的规划布局形式类型中不包括____。,居住区—小区—组团,居住区—组团,街坊式,联合式组团,D
+8,造成城乡生产力结构根本区别的是____。,文化观念的差异,生产力结构的差异,职能的差异,物质形态的差异,B
+9,以下不属于液化石油气气化站与混气站的布置原则的是____。,液化石油气气化站与混气站的站址应靠近负荷区,站址应是地势平坦、开阔、不易积存液化石油气的地段,站址应与站外建筑物保持规范所规定的防火间距要求,作为机动气源的混气站不能与气源厂、城市煤气储配站合设,D
+10,世界上现存最高的木塔是中国____,河南登封嵩岳寺塔,山西应县佛宫寺释迦塔,山东济南神通寺四门塔,陕西扶风法门寺塔,B
+11,采用一元线性回归的方法分析预测规划期城市人口规模的主要依据是____。,可以准确预测规划远期的人口数量,在某一时间段内城市人口的数量与时间,是一组相关的线性函数关系,城市人口遵循直线增长的规律,已掌握了充足的城市人口历年变动的资料,B
+12,下列属于工业固体废物的产生量的预测方法的是____,单位产品法,百元产值法,人均指标法,增长率法,A
+13,以下有关城市规划编制单位资质管理的说法正确的是____。,三个以上城市规划编制单位合作编制城市规划时,有关规划编制单位应当共同向任务所在地相应的主管部门备案,城市规划编制单位合并或者分立,应当在批准之日起15日内重新申请办理《资质证书》,申请乙级、丙级资质的,由所在地市、县人民政府城市规划主管部门审批,核发《资质证书》,并报国务院城市规划主管部门备案,乙、丙级城市规划编制单位跨省、自治区、直辖市设立的分支机构中,非独立法人的机构,不得以分支机构名义承揽业务,C
+14,下列不属于堆肥方法运用于固体垃圾处理优点的是____,占地较小,投资较低,产品可用作肥料,无害化程度很高,A
+15,公民、法人和社会团体为了促进城市规划有效、合理地实施,为了维护自己的合法权利,可以依法对城市规划行政机关做出的具体行政行为提出____。,行政诉讼,民事诉讼,行政仲裁,申诉,A
+16,下列哪一项不是城市总体规划中城市发展目标的内容?____,城市性质,用地规模,人口规模,基础设施和公共设施配套水平,A
+17,工商业活动集聚的场所是____,也是从事工商业活动的人群聚居的场所。,乡村,郊区,田园,城市,D
+18,大修的城市道路竣工后____年内不得挖掘;因特殊情况需要挖掘的,须经县级以上城市人民政府批准。,3,4,5,6,A
+19,洛杉矶的交通发展模式为____。,以小汽车为主、公交为辅的交通模式,以小汽车为主体的交通模式,以轨道公交为主、小汽车和地面公交为辅的交通模式,公交为主、小汽车为主导(公交与小汽车并重),B
+20,根据《中华人民共和国环境影响评价法》的规定,下列关于规划环境影响评价的内容和审批表述中不正确的是____,未编写有关环境影响的篇章或者说明的规划草案,审批机关不予审批,专项规划的编制机关在报批规划草案时,必须将环境影响登记表一并附送审批机关审查,专项规划的环境影响报告书应当包括环境影响评价的结论,规划有关环境影响的篇章或者说明,应当对规划实施后可能造成的环境影响作出分析、预测和评估,B
+21,根据《城市道路绿化规划与设计规范》的规定,城市道路绿化规划与设计的基本原则不包括____。,城市绿化树木与市政公用设施的相互位置应统筹安排,并应保证树木有必要的立地条件与生长空间,城市道路绿化应以地被植物为主,地被植物、乔木、灌木相结合,不得裸露土壤,修建城市道路时,宜保留有价值的原有树木,对古树名木应予以保护,城市道路绿化应符合车行视线和行车净空要求,B
+22,根据《历史文化名城名镇名村保护条例》,保护规划应当自历史文化名城、名镇、名村批准公布之日起____年内编制完成。,半,1,2,3,B
+23,下列有关法律效力选项中不正确的是____,在一定主体制定的法律规范中,按照特定的、更为严格的程序制定的法律规范,其效力等级高于按照普通程序制定的法律规范,当同一制定机关按照相同程序就同一领域问题制定了两个以上法律规范时,后来法律规范的效力高于先前制定的法律规范,同一主体在某领域既有一般性立法又有特殊立法时,特殊立法通常优于一般性立法,国家机关授权下级国家机关制定的所有的法律、法规,其在效力上等同于授权机关自己制定的法律、法规,D
+24,城市公共交通系统的核心设施是____。,公交换乘枢纽,城市各级公共中心,市级公交干线,城市对外客运交通枢纽,A
+25,根据《城市抗震防灾规划管理规定》,下列关于城市抗震防灾规划编制要求的表述不正确的是____。,城市抗震防灾规划中的抗震设防标准、建设用地评价与要求、抗震防灾措施应当列为城市总体规划的强制性内容,作为编制城市详细规划的依据,城市抗震防灾规划的规划范围应当与城市总体规划相一致,但其应在城市总体规划实施之后进行,城市抗震防灾规划应当按照城市规模、重要性和抗震防灾的要求,分为甲、乙、丙三种模式,位于地震基本烈度七度及七度以上地区的大城市应按照甲类模式编制,B
+26,港口岸线分配原则是____。,主要考虑与城市道路衔接,“深水深用、浅水浅用、避免干扰、各得其所”,客运港位于货运港的上风方向,综合考虑船舶航行、货物装卸、库场储存及后方集疏,B
+27,根据《省级国土空间规划编制指南(试行)》,以城镇建设、农业生产和工业生产等为主的国土空间开发活动是指____,国土空间开发,国土空间利用,国土空间规划,国土空间保护,A
+28,下列不符合《人民防空法》规定的是____,城市是人民防空的重点,国家对城市实行分类防护,城市防护类别、防护标准由中央军事委员会规定,城市人民政府应当制定防空袭方案及实施计划,必要时可以组织演习,C
+29,在城市规划分析中,下列用来反映数据离散程度的是____,平均数,众数,标准差,频数分布,C
+30,城市交通调查的目的是____。,进行城市交通规划、城市道路系统规划和城市道路设计的基础工作,收集城市公共交通客运总量、货运总量,对外交通客、货运总量等运输现状与发展资料,根据调查的资料,分析城市车辆以及客、货运量的增长特点和规律,摸清城市道路上的交通状况,城市交通的产生、分布、运行规律以及现状存在的主要问题,D
+31,下列以满足交通运输的要求为主要功能并承担城市主要的交通量及与对外交通联系的道路是____。,生活性道路,交通性道路,主干路,快速路,B
+32,建设单位应当按照规划条件进行建设;确需变更的,必须向____提出申请。,城市、县人民政府土地主管部门,城市、县人民政府国土资源部门,城市、县人民政府建设行政主管部门,城市、县人民政府城乡规划主管部门,D
+33,在城市详细规划阶段预测用电负荷,一般采用下列哪种方法?____,人均综合用电量指标法,单位建设用地负荷指标法,单位建筑面积负荷指标法,电力弹性系数法,C
+34,在规划实施过程中由城乡规划主管部门核发的证书不包括____。,建设用地规划许可证,建设工程规划许可证,乡村建设规划许可证,建设工程施工许可证,D
+35,下列关于建筑与等高线之间关系的表述,错误的是____,建筑与等高线平行,建筑与等高线垂直,建筑与等高线重合,建筑与等高线斜交,C
+36,依据《城市用地分类与规划建设用地标准》,其规划人均公共管理与公共服务用地面积指标不应少于____。,5.0m^2/人,5.5m^2/人,6.0m^2/人,6.5m^2/人,B
+37,城市供热一级管网宜采用____。,闭式,开式,开式双管制,闭式单管制,A
+38,下列关于村庄规划的表述,哪项是错误的?____,应以行政村为单位,应向村民公示,方案由县级城乡规划行政主管部门组织专家和相关部门进行技术审查,成果由村委会报县级人民政府审批,D
+39,根据《城市规划编制单位资质管理规定》,下列关于城市规划编制单位资质监督管理的表述中,不正确的是____。,城市规划编制单位提交的城市规划编制成果,应当在文件扉页注明单位资质等级和证书编号,禁止无城市规划编制的机构对城市规划编制单位实行资质年检制度,发证部门或其委托的机构对城市规划编制单位实行资质年检制度,甲、乙级城市规划编制单位跨省、自治区、直辖市承担规划编制任务时,未向其人民政府城市规划行政主管部门备案的,由该人民政府城市规划行政主管部门给予警告,责令其补办备案手续,并处1万元以上5万元以下的罚款,D
+40,以下对城市所具有的基本特征的概括,表述不正确的是____。,城市的发展动态是变化和多样的,城市的概念是相对存在的,以要素聚集为基本特征,不具有系统性,D
+41,下列符合核电厂选址要求的是____,便于拦河筑坝的河流狭窄处或水库水流下游处,电厂铁路专用线选线要尽量减少对国家干线通过能力的影响,靠近负荷中心,以减少输电费用,工程地质条件良好,土地耐力高,非地质断裂带,C
+42,____是建立城市艺术骨架和组织城市空间的重要手段之一,它可以把城市空间组织成一个有秩序、有韵律的整体。,城市绿化,城市水面,城市制高点,城市轴线,D
+43,为了定量分析采取某项措施对于减少城市污染的效果,所开发的系统属于____,决策支持系统,事务管理系统,管理信息系统,专家系统,B
+44,计算道路网的密度,分析管线穿越地块的问题,可以采用矢量叠合的____的叠合。,点和面,线和面,面和面,点和线,B
+45,标志着欧洲进入封建社会的中世纪的是____。,奥匈帝国的灭亡,古希腊的灭亡,波斯帝国的灭亡,罗马帝国的灭亡,D
diff --git a/ceval/ceval-exam/val/veterinary_medicine_val.csv b/ceval/ceval-exam/val/veterinary_medicine_val.csv
new file mode 100644
index 0000000..d001816
--- /dev/null
+++ b/ceval/ceval-exam/val/veterinary_medicine_val.csv
@@ -0,0 +1,24 @@
+id,question,A,B,C,D,answer
+0,____既在糖酵解又在葡萄糖异生作用中起作用。,丙酮酸激酶,3-磷酸甘油醛脱氢酶,1,6-二磷酸果糖激酶,己糖激酶,B
+1,将RNA转移到硝基纤维素膜上的技术叫____。,Southern印迹,Northern印迹,Western印迹,Eastern印迹,B
+2,____不是蛋白质的性质之一。,处于等电状态时溶解度最小,加入少量中性盐溶解度增加,变性蛋白质的溶解度增加,有紫外吸收特性,C
+3,在酶的分离纯化中最理想的实验结果是____。,纯化倍数高,回收率高,蛋白回收率高,回收率小,但纯化倍数高,比活力最大,A
+4,动物体内组成的化学元素中,下列所占比例最多的是____,碳,氯,钠,氢,A
+5,酶的催化特点不具有____,高效性,多功能性,可调节性,酶蛋白易变性,B
+6,生物素是____的辅酶。,丙酮酸脱氢酶,丙酮酸激酶,丙酮酸脱氢酶系,丙酮酸羧化酶,D
+7,蛋白质不能吸收可见光,但能吸收一定波长范围内的紫外光。大多数蛋白质在280nm波长附近有一个吸收峰,这主要与蛋白质中____的紫外吸收有关。因此,可以利用紫外吸收法,根据蛋白质溶液在280nm波长的吸收值测定蛋白质浓度,碱性氨基酸,酸性氨基酸,含硫氨基酸,芳香族氨基酸,B
+8,"具5 "" -CpGpGpTpAp-3 "" 顺序的单链DNA能与下列____RNA杂交","5 "" -GpCpCpApTp3 ""","5 "" -GpCpCpUp-3 ""","5 "" -UpApCpCpGp-3 ""","5 "" -TpApCpGp-3 """,C
+9,酶的非竞争性抑制剂对酶促反应的影响是____。,$ν_{max}$ 不变,$K_m$ 增大,$ν_{max}$不变,$K_m$减小,$ν_{max}$增大,$K_m$不变,$ν_{max}$减小,$K_m$不变,D
+10,水溶性维生素常是辅酶或辅基的组成部分,如____。,辅酶A含尼克酰胺,FAD含有吡哆醛,$FH_4$ 含有叶酸,脱羧辅酶含生物素,C
+11,核酸变性后,可发生的效应是____。,减色效应,增色效应,失去对紫外线的吸收能力,最大吸收峰波长发生转移,B
+12,下列____不是终止密码。,UAA,15AC,UAG,UGA,B
+13,关于酶偶联受体的叙述,错误的是____。,酶偶联受体属于细胞表面受体,酶偶联受体的配体结合区在细胞膜内侧,酶活性区在细胞膜外侧,酶偶联受体介导的是非经典跨质膜与胞内信号途径,可以单独完成信号传递,胞内信号传递不产生经典意义上的第二信使,多数酶偶联受体具有磷酸化酶的活性,B
+14,主要分布在肝脏的是____。,碱性磷酸酶,酸性磷酸酶,单胺氧化酶,谷丙转氨酶,D
+15,对DNA片段作物理图谱分析,需要用____。,核酸外切酶,DNAseI,限制性内切酶,DNA聚合酶I,C
+16,____不是胆色素。,血红素,胆绿素,胆红素,胆素原族,A
+17,转录是指____,DNA的自我复制过程,RNA的自我复制过程,以DNA为模板合成RNA的过程,以RNA为模板合成DNA的过程,C
+18,血液非蛋白氮中含量最多的物质是____。,尿素,肌酸,蛋白质,尿酸,A
+19,氨基酸分子既含有酸性的羧基(一COOH),又含有碱性的氨基(一$NH_2$ )。前者能提供质子变成一COO一;后者能接受质子变成一$NH_3^+$ 。有的氨基酸还有可解离的侧链基团。因此,氨基酸是两性电解质。其解离状态与溶液的pH值有直接关系,当pH值等于pI时,蛋白质____,带正电荷,带负电荷,所带电荷不确定,所带正、负电荷相等,D
+20,蛋白质分子是结构极其复杂的生物大分子。有的蛋白质分子只包含一条多肽链;有的则包含数条多肽链。通常将蛋白质的结构划分为几个层次,有一种结构层次出现在一条多肽链的内部,是多肽链局部的所有原子及原子团形成的有规律的构象,该构象一般成球状结构,执行一定的功能,该结构是____,结构域,超二级结构,二级结构,三级结构,A
+21,蛋白质生物合成中多肽的氨基酸排列顺序取决于____。,相应tRNA的专一性,相应氨酰tR-NA合成酶的专一性,相应mRNA中核苷酸排列顺序,相应tRNA上的反密码子,C
+22,稀有核苷酸碱基主要见于____。,DNA,mRNA,tRNA,rRNA,C
diff --git a/ceval/ceval_result/accountant_val_result.csv b/ceval/ceval_result/accountant_val_result.csv
new file mode 100644
index 0000000..8e3ff6b
--- /dev/null
+++ b/ceval/ceval_result/accountant_val_result.csv
@@ -0,0 +1,51 @@
+question,A,B,C,D,answer,llm_answer,is_right
+下列关于税法基本原则的表述中,不正确的是____。,税收法定原则包括税收要件法定原则和税务合法性原则,税收公平原则源于法律上的平等性原则,税收效率原则包含经济效率和行政效率两个方面,税务机关按法定程序依法征税,可以自由做出减征、停征或免征税款的决定,D,C,0
+甲公司是国内一家领先的新媒体、通信及移动增值服务公司,由于遭受世界金融危机,甲公司经济利润严重下滑,经营面临困境,但为了稳定职工队伍,公司并未进行裁员,而是实行高层管理人员减薪措施。甲公司此举采用的收缩战略方式是____。,转向战略,放弃战略,紧缩与集中战略,稳定战略,C,A,0
+根据我国《印花税暂行条例》的规定,下列各项中,属于印花税的纳税人的是____。,合同的证人,合同的担保人,合同的鉴定人,合同的当事人,D,C,0
+税务行政复议的申请人可以在得知税务机关作出具体行政行为之日起____日内提出行政复议申请。,60,30,7,3,A,C,0
+关于战略管理表述错误的是____。,是一个循环往复的过程,需要修正原来的分析、选择与实施工作,是一次性的工作,要不断监控和评价战略的实施过程,C,C,1
+宏远海运公司为了加强对损失事件的管理成立了一家附属机构,其职责是用母公司提供的资金建立损失储备金,并为母公司提供保险。宏远海运公司管理损失的方法是____。,专业自保,保险,损失融资,风险成本,A,C,0
+下列企业采用的成长型战略中,属于多元化战略的是____。,甲碳酸饮料生产企业通过按季更换饮料包装、在各传统节日期间附赠小包装饮料等方式增加市场份额,乙汽车制造企业开始将其原在国内生产销售的小型客车出口到南美地区,丙洗衣粉生产企业通过自行研发,开始生产销售具有不同功效的洗发水,丁酸奶生产企业新开发出一种凝固型酸奶,并将其推向市场,C,A,0
+某企业2016年初拥有土地2万平方米,包括厂区内托儿所占地0.1万平方米,无偿提供给公安局使用土地0.3万平方米,厂区内绿化用地0.4万平方米。5月该企业购买一块土地使用权(非耕地)用于厂房扩建,占地面积为2.4万平方米,合同规定,5月支付土地价款并交付土地。该企业所在地区城镇土地使用税年税额为10元/平方米,该企业2016年应缴纳城镇土地使用税____万元。,26,36,30,44,C,D,0
+注册会计师在出具审计报告时,存在以下事项,其中不正确的是____。,审计报告采用书面形式,审计报告由不是注册会计师的项目经理签字盖章,审计报告应当具有标题,统一规范为“审计报告”,审计报告应当按照审计业务约定的要求载明收件人,B,A,0
+下列关于预付卡的表述中,不正确的是____。,记名预付卡可挂失,可赎回,不得设置有效期,不记名预付卡一般不挂失,不赎回,预付卡可以具有透支功能,不记名预付卡有效期不得低于3年,C,C,1
+下列各项中,不属于注册会计师职业道德守则禁止的商业关系的是____。,审计项目组成员按照正常的商业程序从审计客户购买无论从金额还是从性质的角度考虑均不重大或特殊的商品或服务,按照协议,将会计师事务所的产品或服务与客户的产品或服务结合在一起,并以双方名义捆绑销售,按照协议,会计师事务所销售或推广客户的产品或服务,按照协议,客户销售或推广会计师事务所的产品或服务,A,A,1
+注册会计师甲选择了A公司2017年整个会计期间的会计分录和其他调整进行会计分录测试时,在确定对总体完整性时执行了下列程序中不恰当的是____。,从被审计单位会计信息系统中导出所有待测试会计分录和其他调整,并将总账与财务报表核对,以检查是否存在其他调整,加计从会计信息系统中导出的所有会计分录和其他调整中的本期发生额,与科目余额表中的各科目本期发生额核对相符,将系统生成的重要账户余额与明细账和总账及科目余额表中的余额核对,测试计算准确性,检查所有结账前作出的与本期财务报表有关的会计分录和其他调整,测试其完整性,D,A,0
+如果用合作性和坚定性两维坐标来描述企业某一利益相关者在企业战略决策与实施过程行为模式,“和解”是____。,中等程度的坚定性和中等程度的合作性行为的组合,不坚定行为与合作行为的组合,坚定行为和不合作行为的组合,坚定行为与合作行为的组合,B,A,0
+目前越来越普遍的显示主要绩效数据的方法是____。,以图解方法通过屏幕显示,以口头的方式公开展示,以文字的形式出版展示,以报表的形式对外列报,A,C,0
+根据增值税现行政策规定,下列业务属于在境内销售服务或不动产的是____。,境外单位为境内单位提供境外矿山勘探服务,境外单位向境内单位出租境外的厂房,境外单位向境内单位销售在境外的不动产,境外单位在境内向境内单位提供运输服务,D,A,0
+下列关于诉讼时效中止的表述,不正确的是____。,中止诉讼时效的事由包括不可抗力与其他障碍,诉讼时效中止使已经进行的诉讼时效期间全部归于无效,只有在诉讼时效期间的最后6个月内发生不可抗力的情况和其他障碍,才能中止时效的进行,中止事由发生前已经经过的诉讼时效期限仍然有效,自中止时效的原因消除之日起满6个月,诉讼时效期间屑满,B,C,0
+下列选项中,属于有效的民事法律行为的是____。,9岁的小明用家里的一张存折换了一辆汽车,甲公司与乙公司口头达成的技术开发合同,张某未经妻子同意购买了一件昂贵的古玩,16岁的中学生未经家长同意用自己攒的钱买了一辆摩托车,C,C,1
+下列各项中,符合增值税纳税地点规定的是____。,进口货物,应当由进口人或其代理人向报关地海关申报纳税,固定业户到外市销售货物或者提供应税劳务,一律向销售地或劳务发生地主管税务机关申报纳税,非固定业户销售货物或者应税劳务的,向其机构所在地或居住地的主管税务机关申报缴纳税款,扣缴义务人应当向销售地或者劳务发生地的主管税务机关申报纳税,A,D,0
+某计算机生产商将键盘和鼠标生产外包给另一电子类外设生产企业,由其按照订单要求供货,则该计算机生产商的货源策略属于____。,单一货源策略,多货源策略,由供应商负责交付一个完整的子部件,由采购商负责交付一个完整的子部件,C,A,0
+下列关于有限合伙企业中有限合伙人入伙与退伙的表述中,符合《合伙企业法》规定的是____。,新入伙的有限合伙人对入伙前有限合伙企业的债务,以其实缴的出资额为限承担责任,作为有限合伙人的自然人,有限合伙企业存续期间丧失民事行为能力的,该有限合伙人当然退伙,退伙后的有限合伙人对基于其退伙前的原因发生的有限合伙企业的债务,以其退伙时从有限合伙企业中取回的财产为限承担责任,退伙后的有限合伙人对基于其退伙前的原因发生的有限合伙企业的债务,以其认缴的出资额为限承担责任,C,D,0
+某企业2016年拥有一幢三层的办公楼,原值6000万元,将其中的1/3以每月15万元的租金出租给其他单位使用,2016年4月底,原租户的租期到期,该企业将该幢办公楼进行改建,更换楼内电梯,将原值80万元的电梯更换为120万元的新电梯,为该楼安装了300万元的智能化楼宇设施,这些改建工程于7月底完工,该企业所在地省人民政府规定计算房产余值的减除比例为30%,该企业2016年应纳房产税____万元。,51.49,52.38,53.05,53.19,D,A,0
+企业一定时期的总目标是由____根据长期规划,利用本量利分析等工具提出来的。,预算委员会,最基层成本控制人员,上级主管,企业决策机构,D,C,0
+根据COSO内部控制框架,反舞弊机制属于内部控制要素中的____。,风险评估,控制活动,监控,信息与沟通,D,C,0
+下列属于中央固定收入的是____。,增值税,城市维护建设税,资源税,车辆购置税,D,C,0
+某生产企业(具有出口经营权)为增值税一般纳税人,2017年2月从国内采购生产用原材料一批,取得增值税专用发票,注明价款810万元、增值税税额137.7万元;当月国内销售货物取得不含税销售额150万元,出口自产货物取得收入折合人民币690万元;已知,适用的增值税税率为17%,出口退税率为13%,月初无留抵税额,相关发票均已经过主管税务机关认证并允许抵扣。则下列关于该企业增值税的税务处理中,说法正确的是____。,应缴纳增值税25.5万元,免抵增值税额为89.7万元,应退增值税84.6万元,免抵增值税额为0,应退增值税89.7万元,免抵增值税额为0,应退增值税84.6万元,免抵增值税额为5.1万元,D,B,0
+税收筹划有多种方法,以下关于税务筹划基本方法理解错误的是____。,企业欲将评估价为100万元的闲置房产对外销售,另有意向向W公司投资100万元,企业与W公司协商,以此房产直接投资入股,共担风险。这属于利用免税的税收筹划方法,某企业产品价值较大,销售回款周期较长,一般情况下都要在六个月以内分三次收取,注册税务师建议企业与客户签定分期收款合同,在合同中约定具体的收款时间和收款金额,注册税务师采用的是延期纳税的方法,A公司将销售货物的方式详细划分为赊销业务、分期收款销售业务、直接收款业务等方式,这属于利用分劈的筹划方法,张某因工作需要在上海购置房产,拟在三年后调回北京时将此房产转让给刘某,注册会计师提醒张某妥善保存原购房合同及购房发票。张某做的是利用税收扣除的筹划方法,C,B,0
+关于市场支配地位推定标准,下列不符合我国《反垄断法》规定的是____。,经营者在相关市场的市场份额达到1/2的,推定为具有市场支配地位,2个经营者在相关市场的市场份额合计达到2/3,其中有的经营者市场份额不足1/10的,不应当推定该经营者具有市场支配地位,3个经营者在相关市场的市场份额合计达到3/4,其中有2个经营者市场份额合计不足1/5的,不应当推定该两个经营者具有市场支配地位,被推定具有市场支配地位的经营者,有证据证明不具有市场支配地位的,不应当认定其具有市场支配地位,C,C,1
+下列各项中,不属于TPS处理过程的基本活动的是____。,数据输入,数据库维护,文件和报告生成,信息输出,D,C,0
+甲公司平价发行5年期的公司债券,债券的票面利率为12%,每季度付息一次,到期一次偿还本金,该债券的有效年利率为____。,12.25%,12.55%,10%,9.50%,B,A,0
+经批准开山填海整治的土地和改造的废弃土地,从使用的月份起免缴城镇土地使用税5年至10年。具体免税期限由____确定。,省级地方税务局,地市级地方税务局,县级地方税务局,国家税务总局,A,A,1
+根据证券法律制度的规定,下列关于上市公司收购的表述,正确的是____。,采用要约方式收购上市公司股份的,其预定收购的股份比例不得低于该上市公司已发行股份的30%,要约收购期间,收购人不得撤销收购要约,也不得变更收购要约,收购人对同一种类股票的要约价格不得低于要约收购提示性公告日前12个月内收购人取得该种股票所支付的最高价格,要约收购期间,被收购公司董事不得辞职,D,A,0
+审计被审计单位的投资业务时,对于下列____投资业务,注册会计师可能无需向金融机构发函询证。,长期股权投资,交易性金融资产,可供出售金融资产,持有至到期投资,A,D,0
+某国有企业因有违反税收征收管理法的行为,被税务机关处以8000元的罚款。假定该企业收到税务行政处罚决定书的时间为2015年3月1日,则该企业4月5日缴纳罚款时的总金额为____。,8000元,9200元,13040元,16640元,C,A,0
+甲公司是牛肉生产、加工及零售企业。近期甲公司开始考虑将其业务扩展到国际市场,在劳工成本较低的越南设立统一的牛肉加工厂,并在多个国家从事牛肉加工食品零售业务。甲公司管理层采用集权式管理方式,为确保牛肉加工食品的质量,甲公司计划将所有原料牛在日本农场饲养。根据以上内容,适合甲公司选择的国际化发展战略是____。,多元化成长战略,全球化战略,多国化战略,跨国化战略,B,A,0
+2019年5月1日,人民法院受理了对甲公司提起的破产申请。根据企业破产法律制度的规定,下列有资格担任管理人的是____。,甲公司董事乙,甲公司董事会秘书的女儿丙,曾于2014年1月1日至2015年1月1日担任甲公司法律顾问的丁律师,3年前被吊销执业证书,事后重新获取执业资格的注册会计师戊,C,A,0
+注册会计师在对专家工作使用的重要原始数据进行评价时,下列各项中,无需评价的是____,原始数据的完整性,原始数据的准确性,原始数据的相关性,原始数据的可理解性,D,A,0
+2018年12月,某鞭炮生产厂将自产的一批鞭炮与其供应商换取生产用原材料,已知该批鞭炮的平均销售价格为14万元,最低销售价格为10万元,最高销售价格为18万元,则该鞭炮生产厂应缴纳的消费税为____万元。(以上价格均为不含税价,鞭炮消费税税率15%),1.5,2.7,1.73,1.05,B,D,0
+下列关于货币市场的表述中,正确的是____。,交易的证券期限长,利率或要求的报酬率较高,主要功能是保持金融资产的流动性,主要功能是进行长期资本的融通,C,C,1
+企业采用外部招聘的优点是____。,能节约大量的招聘和选拔时间及费用,可以给企业注入新鲜血液,能够调动员工的积极性,培养员工的忠诚度,容易诱发自满情绪,B,C,0
+下列关于了解被审计单位及其环境的说法中,错误的是____。,注册会计师只需要在审计的初始阶段了解被审计单位及其环境,注册会计师应当运用职业判断确定需要了解被审计单位及其环境的程度,评价对被审计单位及其环境了解的程度是否恰当,关键是看注册会计师对被审计单位及其环境的了解是否足以识别和评估财务报表的重大错报风险,要求注册会计师对被审计单位及其环境了解的程度,要低于管理层为经营管理企业而对被审计单位及其环境需要了解的程度,A,A,1
+截至2015年秋,U国N航空公司与M航空公司合并已有5年,但原N公司和M公司机舱服务员的劳工合约仍未统一。为此,原N公司与M公司的机舱服务员在临近圣诞节期间,发起抗议行动,有效推动了该项问题的解决。本案例中原N公司与M公司机舱服务员的权力来源于____。,在管理层次中的地位,个人的素质和影响,参与或影响企业战略决策与实施过程,利益相关者集中或联合的程度,D,C,0
+下列有关内部审计和注册会计师审计的关系的说法中,错误的是____。,为支持所得出的结论,内部审计和注册会计师审计中的审计人员都需要获取充分、适当的审计证据,但内部审计不会采用函证和分析程序,注册会计师应当考虑内部审计工作的某些方面是否有助于确定审计程序的性质、时间安排和范围,包括了解内部控制所采用的程序、评估财务报表重大错报风险所采用的程序和实质性程序,如果内部审计的工作结果表明被审计单位的财务报表在某些领域存在重大错报风险,注册会计师应对这些领域给予特别关注,注册会计师必须对与财务报表审计有关的所有重大事项独立作出职业判断,而不应完全依赖内部审计工作,A,D,0
+下列企业中,财务困境成本最高的是____。,石油开采企业,日用品生产企业,汽车制造企业,软件开发企业,D,C,0
+因土地规模化耕种需要,农村居民张某经批准搬迁,搬迁前住宅占用耕地220平方米,搬迁后新建自用住宅占用耕地260平方米(规定用地标准内),当地耕地占用税税额每平方米20元,张某应缴纳耕地占用税____元。,0,400,800,2600,B,D,0
+近年来,金融行业的工资率不断攀升。在此期间,甲公司分析其竞争对手乙公司发现,乙公司的成本费用非常少,乙公司不断精简公司部门机构,在保持公司活力的同时节省公司费用。甲公司对乙公司进行的上述分析属于____。,成长能力分析,快速反应能力分析,适应变化的能力分析,持久力分析,C,A,0
+某股票为固定增长股票,其固定增长率为3%,预期第一年的股利为4元。假定目前100年期长期政府债券报酬率为13%,平均风险股票的必要报酬率为18%,而该股票的贝塔系数为1.2,则该股票的价值为____元。,25,40,26.67,30,A,D,0
+2019年5月,东湖有限责任公司股东申请法院指定清算组对公司进行清算,法院为其指定相关人员组成清算组。关于该清算组成员,下列选项不能成为清算组成员的是____。,公司债权人唐某,公司董事长程某,公司财务总监钱某,公司聘请的某律师事务所,A,A,1
+下列关于营业预算的计算等式中,不正确的是____。,本期生产数量=(本期销售数量+期末产成品存量)-期初产成品存量,本期购货付现=本期购货付现部分+以前期赊购本期付现的部分,本期材料采购数量=(本期生产耗用数量+期末材料存量)-期初材料存量,本期销售商品所收到的现金=本期的销售收入+期末应收账款-期初应收账款,D,A,0
+下列各项中,不能增加企业核心竞争力的是____。,产品差异化,购买生产专利权,创新生产技术,聘用生产外包商,D,D,1
+-,-,-,-,-,文件 accountant_val.csv 的正确率: 20.41%,-,-
diff --git a/ceval/ceval_result/advanced_mathematics_val_result.csv b/ceval/ceval_result/advanced_mathematics_val_result.csv
new file mode 100644
index 0000000..aebaa63
--- /dev/null
+++ b/ceval/ceval_result/advanced_mathematics_val_result.csv
@@ -0,0 +1,24 @@
+question,A,B,C,D,answer,llm_answer,is_right
+求极限:$\lim_{x\rightarrow0}\frac{\int_{x^2}^x{\frac{\sin\left(xt\right)}{t}}\mathrm{d}t}{x^2}=$____,$\frac{5}{6}$,1,$\frac{7}{6}$,$\frac{4}{3}$,B,C,0
+设$n$为正整数,求极限:$\lim_{x\rightarrow+\infty}\left[\frac{x^n}{\left(x-1\right)\left(x-2\right)\cdots\left(x-n\right)}\right]^x=$____,$e^{\frac{(n-1)(n+1)}{2}}$,$e^{\frac{(n-1)n}{2}}$,$e^{\frac{n(n+1)}{2}}$,$e^{\frac{n^{2}}{2}}$,C,C,1
+设平面区域$D$由直线$y=\frac{1}{2}x-\frac{1}{2\sqrt{5}}$、$y=2x-\frac{2}{\sqrt{5}}$和$y=x$围成,函数$z=3xy+3$在$D$上的最大值和最小值分别是M和m,则____,"$M=6,m=3$","$M=\dfrac{27}{5},m=3$","$M=\dfrac{18}{5},m=3$","$M={\frac{27}{5}},m={\frac{117}{40}}$",D,C,0
+设函数$f\left(x\right)$连续,且$f\left(x\right)>0$,求积分:$int_0^1{\ln f\left(x+t\right)}\mathrm{d}t=$____,$\int_0^x{\ln \frac{f\left( t+2 \right)}{f\left( t \right)}}\mathrm{d}t+\int_0^1{\ln f\left( t \right)}\mathrm{d}t$,$\int_0^1{\ln \frac{f\left( t+2 \right)}{f\left( t \right)}}\mathrm{d}t+\int_0^1{\ln f\left( t \right)}\mathrm{d}t$,$\int_0^2x{\ln \frac{f\left( t+1 \right)}{f\left( t \right)}}\mathrm{d}t+\int_0^1{\ln f\left( t \right)}\mathrm{d}t$,$\int_0^x{\ln \frac{f\left( t+2 \right)}{f\left( t \right)}}\mathrm{d}t+\int_0^1{\ln f\left( t \right)}\mathrm{d}t$,A,C,0
+设有界区域$\Omega$由平面$2x+y+2z=2$与三个坐标平面围成,$\Sigma$为整个表面的外侧;\\计算曲面积分:$I=\iint_{\Sigma}{\left(x^2+1\right)\mathrm{d}y\mathrm{d}z-2y\mathrm{d}z\mathrm{d}x+3z\mathrm{d}x\mathrm{d}y}=$____,$\frac{1}{2}$,1,$\frac{3}{2}$,$\frac{5}{2}$,A,A,1
+已知$\int_0^{+\infty}{\frac{\sin x}{x}\mathrm{d}x=\frac{\pi}{2}}$,则$\int_0^{+\infty}{\int_0^{+\infty}{\frac{\sin x\sin\left(x+y\right)}{x\left(x+y\right)}}}\mathrm{d}x\mathrm{d}y$=____,$\frac{\pi ^2}{12}$,$\frac{\pi ^2}{8}$,$\frac{\pi ^2}{4}$,$\frac{\pi ^2}{2}$,B,C,0
+"设曲线$C=\left\{(x,y,z):x={\sqrt{3}}\cos(t),y={\sqrt{3}}\sin(t),z={\frac{2}{3}}t^{\frac{3}{2}},0\leq t\leq5\right\}$,则曲线积分$\int_C(x^2+y^2)\mathrm{d}s=$____",$\frac{3}{4}\left(16\sqrt{2}-3\sqrt{3}\right)$,$2\bigl(16\sqrt{2}-3\sqrt{3}\bigr)$,$\frac{9}{4}\left(16\sqrt{2}-3\sqrt{3}\right)$,$\frac{3}{2}\left(16\sqrt{2}-3\sqrt{3}\right)$,B,A,0
+"计算二重积分:$\iint_D{x\mathrm{d}x\mathrm{d}y}=$.其中$D$为由直线$y=-x+2,x$轴以及曲线$y=\sqrt{2x-x^2}$所围成的平面区域.____",$\frac{\pi}{4}+\frac{1}{3}$,$\frac{\pi}{2}+\frac{1}{3}$,$\frac{\pi}{2}+\frac{1}{4}$,$\frac{\pi}{2}+\frac{2}{3}$,A,D,0
+求极限:$L=\lim_{n\rightarrow\infty}\sqrt{n}\left(1-\sum_{k=1}^n{\frac{1}{n+\sqrt{k}}}\right)$=____,$\frac{1}{3}$,$\frac{2}{3}$,1,$\frac{4}{3}$,B,C,0
+"$x=1$是函数$f\left(x\right)=\frac{bx^2+x+1}{ax+1}$的可去间断点,求$a,b$的值?____","$a=-1,b=-2$","$a=-2,b=-1$","$a=-2,b=-2$","$a=-1,b=0$",A,A,1
+求$\sum_{n=1}^{\infty}{\frac{\left(-1\right)^{n+1}-2^n}{n}x^n}$的和函数.____,"$\ln\left(1-2x-x^2\right),x\in\left[-\dfrac{1}{2},\dfrac{1}{2}\right)$","$\ln\left(1-x-x^2\right),x\in\left(-\dfrac{1}{2},\dfrac{1}{2}\right)$","$\ln\left(1-2x-2x^2\right),x\in\left[-\dfrac{1}{2},\dfrac{1}{2}\right)$","$\ln\left(1-x-2x^2\right),x\in\left[-\dfrac{1}{2},\dfrac{1}{2}\right)$",D,A,0
+求极限:$\lim_{x\rightarrow0}\frac{\sqrt{1+x\cos x}-\sqrt{1+\sin x}}{x^3}=$____,$-\dfrac{1}{3}$,$-\dfrac{1}{4}$,$-\dfrac{1}{5}$,$-\dfrac{1}{6}$,D,C,0
+求极限:$\lim_{n\rightarrow\infty}\sum_{k=1}^n{\frac{k}{\left(k+1\right)!}}=$____,1,0,-1,2,A,C,0
+"已知曲线C是圆$(x-1)^{2}+(y-6)^{2}=25$上从点$A(1,1)$沿逆时针方向到$B(4,2)$的一段弧,则$\oint_{C}(3\ln(1+y)+5x^{2})\mathrm{d}x+\Bigl({\frac{3x}{1+y}}-2y\Bigr)\mathrm{d}y=$____",$108+3\ln\Bigl(\frac{27}{2}\Bigr)$,$3\ln\left(\dfrac{81}{2}\right)-102$,$102+3\ln\Bigl(\frac{81}{2}\Bigr)$,$3\ln\left(\dfrac{27}{2}\right)-97$,C,C,1
+"设$D$是全平面,$f\left(x\right)=\begin{cases}
+x\text{,}-1\leq x\leq2\\
+0\text{,}\text{其他}\\
+\end{cases}$;计算$\iint_D{f\left(x\right)f\left(x^2-y\right)}\mathrm{d}\sigma=$____",$\frac{9}{4}$,$\frac{5}{2}$,$\frac{11}{4}$,3,A,A,1
+计算广义积分:$\int_0^{+\infty}{\frac{\mathrm{d}x}{\left(1+x^2\right)\left(1+x^4\right)}}$____,$\frac{\pi}{8}$,$\frac{\pi}{4}$,$\frac{\pi}{2}$,$\frac{\pi}{3}$,B,B,1
+已知数列$\left\{a_n\right\}$,其中$a_n=\cos n\alpha$,其前$n$$项和为$S_n$.求:$S_n=$____,$\frac{\cos \frac{n}{2}\alpha \cdot \sin \frac{n\alpha}{2}}{\sin \frac{\alpha}{2}}$,$\frac{\cos \frac{n+1}{2}\alpha \cdot \sin \frac{n\alpha}{2}}{\sin \frac{\alpha}{2}}$,$\frac{\cos \frac{n+1}{2}\alpha \cdot \sin \frac{n\alpha}{2}}{\sin \frac{\alpha}{3}}$,$\frac{\cos \frac{n-1}{2}\alpha \cdot \sin \frac{n\alpha}{2}}{\sin \frac{\alpha}{2}}$,B,B,1
+计算定积分:$\int_{-1}^1{\frac{\mathrm{d}x}{\left(1+\mathrm{e}^x\right)\left(1+x^2\right)}}$____,$\frac{\pi}{8}$,$\frac{\pi}{4}$,$\frac{\pi}{2}$,$\pi$,B,C,0
+求极限:$\lim_{x\rightarrow0}\frac{\tan2x^2-x^2}{\sin x^2+3x^2}=$____,$\frac{1}{2}$,$\frac{1}{4}$,$\frac{1}{8}$,$\frac{3}{5}$,B,C,0
+-,-,-,-,-,文件 advanced_mathematics_val.csv 的正确率: 36.84%,-,-
diff --git a/ceval/ceval_result/art_studies_val_result.csv b/ceval/ceval_result/art_studies_val_result.csv
new file mode 100644
index 0000000..3fabf5f
--- /dev/null
+++ b/ceval/ceval_result/art_studies_val_result.csv
@@ -0,0 +1,35 @@
+question,A,B,C,D,answer,llm_answer,is_right
+蓝印花布是一种传统的民间纺织印染工艺品。蓝印花布印制方法始于____。,汉代,魏晋时期,唐代,宋代,A,A,1
+程婴、公孙杵臼是____中的人物。,《赵氏孤儿》,《杀狗记》,《墙头马上》,《岳阳楼》,A,C,0
+____不是古典主义戏剧的代表人物。,高乃依,拉辛,莎士比,莫里哀,C,C,1
+____创作的《大卫》是第一件复兴了古代裸体雕像传统的作品。,吉贝尔蒂,布鲁内莱斯基,多纳泰罗,马萨乔,C,C,1
+莎士比亚的《李尔王》是____。,悲剧,喜剧,正剧,农村剧,A,C,0
+在西方音乐历史上被誉为“交响乐之父”的是____。,贝多芬,莫扎特,海顿,舒伯特,C,C,1
+1912年成立于西安的秦腔改良团体是____。,易俗社,昆剧传习所,富连成社,伶工学社,A,C,0
+《枉凝眉》是电视剧____的主题曲。,《红楼梦》,《水浒传》,《四世同堂》,《小街》,A,C,0
+《羊泉村》、《狗占马槽》是文艺复兴时期____的代表作。,歌德,易卜生,洛卜.德.维迦,威廉.莎士比亚,C,A,0
+德国表现主义剧作家恺撒的代表作是____。,《鬼魂奏鸣曲》,《从清晨到午夜》,《去大马士革》,《万能机器人》,B,A,0
+创作风格被誉为“音乐芭蕾”的芭蕾舞编导大师是____。,马里乌斯.彼季帕,米歇尔.福金,乔治.巴兰钦,列夫.伊万诺夫,C,A,0
+《钦差大臣》是____创作的。,萧伯纳,契诃夫,易卜生,果戈理,D,C,0
+____不是水华的作品。,《早春二月》,《林家铺子》,《土地》,《白毛女》,A,C,0
+因在《牧羊女》、《天山之春》、《太孜间奏曲》等舞蹈中以精湛的表演技艺享誉中外的中国舞蹈家是____。,阿依吐拉,白淑湘,陈爱莲,崔美善,A,C,0
+《十五贯》又叫____。,《清忠谱》,《双熊梦》,《十五贯戏言成巧祸》,《奈何天》,B,A,0
+“吴门四家”不包括____。,沈周,文征明,唐寅,徐渭,D,C,0
+拉斯金的著作包括以下的:____,装饰的基本原理,建筑的七盏明灯,论衡,建筑十书,B,C,0
+歌剧《奥菲欧》的作者蒙特威尔第是____。,意大利人,德国人,希腊人,英国人,A,C,0
+李斯特是____的作曲家、钢琴家。,奥地利,匈牙利,德国,法国,B,A,0
+《潘烈士投海》、《黑籍冤魂》属于____。,时装新戏,连台本戏,古装新戏,京剧,A,A,1
+商代青铜器的代表纹样是____。,饕餮纹、夔纹,窃曲纹、环带纹,蟠螭纹,铭文,A,A,1
+张择端的《清明上河图》描绘的是____都城的风光。,北宋,南宋,南唐,后周,A,C,0
+曹禺的《北京人》被研究者称为中国最伟大的____。,喜剧,悲剧,社会问题剧,正剧,D,C,0
+元代的漆器品种较多,以____为主,有剔红、剔黄、剔黑、剔彩等不同的色彩运用。,雕漆,戗金,螺钿,平脱,A,C,0
+《方珍珠》、《西望长安》和《宝船》是____的作品。,田汉,郭沫若,欧阳予倩,老舍,D,A,0
+《安提戈涅》是____的作品。,埃斯库罗斯,索福克勒斯,阿里斯托芬,欧里庇得斯,B,A,0
+最早发展工业设计的北欧国家,开创北欧工业设计之先河的是____。,瑞典,芬兰,挪威,波兰,A,C,0
+明传奇《目连救母劝善记》是____写的。,周朝俊,高濂,许自昌,郑自珍,D,C,0
+《缀白裘》是____的集锦选本。,折子戏,沪剧,二人转,乐剧,A,C,0
+《尼伯龙根的指环》是____的作品。,布拉姆斯,肖邦,威尔第,瓦格纳,D,C,0
+《美狄亚》和《希波吕托斯》是____的代表作。,欧里庇得斯,阿里斯托芬,埃斯库罗斯,索福克勒斯,A,A,1
+贝多芬共有____部交响曲。,5,7,9,11,C,B,0
+____的歌词中采用了中国唐代诗人的诗篇(李白的《悲歌行》、《采莲谣》;孟浩然的《宿业师山房待丁大不至》和王维的《送别》等七首)。,《玫瑰骑士》,《大地之歌》,《荒山之夜》,《亡儿之歌》,B,A,0
+-,-,-,-,-,文件 art_studies_val.csv 的正确率: 21.21%,-,-
diff --git a/ceval/ceval_result/basic_medicine_val_result.csv b/ceval/ceval_result/basic_medicine_val_result.csv
new file mode 100644
index 0000000..fc2c07e
--- /dev/null
+++ b/ceval/ceval_result/basic_medicine_val_result.csv
@@ -0,0 +1,21 @@
+question,A,B,C,D,answer,llm_answer,is_right
+不属于第二信使的物质是____,cAMP,cGMP,$IP_3$,胰岛素,D,C,0
+脱羧酶的辅酶____,生物素,叶酸,磷酸吡哆醛,硫胺素,C,C,1
+关于湿性坏疽的描述,不正确的是____,合并腐败菌感染,具有恶臭味,常有全身中毒症状,坏死组织与健康组织之间界限清楚,D,C,0
+肺梗死形成的先决条件是____,组织疏松,严重淤血,蛋白含量高,侧支循环不充分,B,C,0
+关于维生素缺乏症的叙述,不正确的是____,维生素A缺乏——夜盲症,维生素D缺乏——软骨病,维生素$B_1$ 缺乏——脚气病,维生素$B_6$缺乏——口角炎,D,C,0
+cGMP能激活____,磷脂酶C,蛋白激酶A,蛋白激酶G,酪氨酸蛋白激酶,C,C,1
+全身营养不良时,首先发生萎缩的组织或器官是____,骨骼肌,脂肪组织,肝,脑,B,C,0
+下列愈合属于完全再生的是____,神经修复,皮肤瘢痕,心梗后心肌修复,骨折愈合,D,C,0
+PCR反应中,所设计引物的长度一般为____,5~10个核苷酸,15~30个核苷酸,<50个核苷酸,长度任意,B,C,0
+转氨酶的辅酶____,生物素,叶酸,磷酸吡哆醛,硫胺素,C,C,1
+下列肝细胞坏死的病变中,病毒性肝炎最常见的病理变化是____,溶解性坏死,嗜酸性坏死,点状坏死,碎片坏死,A,C,0
+肝脏在脂类代谢中所特有的作用是____,将糖转变为脂肪,由胆固醇转变为胆汁酸,生成酮体,合成磷脂,C,C,1
+导致甲状腺肿大最常见的原因是____,垂体肿瘤,缺碘,自身免疫反应,先天性疾患,B,C,0
+肝是生成尿素的唯一器官,是由于肝细胞含有____,谷氨酸脱氢酶,谷丙转氨酶,谷草转氨酶,精氨酸酶,D,C,0
+血清与血浆的区别在于血清内无____,维生素,糖类,代谢产物,纤维蛋白原,D,B,0
+可直接激活蛋白激酶C的是____,cAMP,cGMP,$IP_3$,DAG,D,C,0
+下列哪项原因不引起萎缩____,肾盂积水,慢性肝淤血,垂体功能低下,四氯化碳中毒,D,C,0
+以$IP_3$和DAG为第二信使的双信号途径是____,cAMP-蛋白激酶途径,$Ca^{2+}$-磷脂依赖性蛋白激酶途径,cGMP-蛋白激酶途径,酪氨酸蛋白激酶途径,B,D,0
+乙型脑炎的特征性病变是____,血管淋巴套形成,软化灶形成,胶质细胞增生,卫星现象,B,C,0
+-,-,-,-,-,文件 basic_medicine_val.csv 的正确率: 21.05%,-,-
diff --git a/ceval/ceval_result/business_administration_val_result.csv b/ceval/ceval_result/business_administration_val_result.csv
new file mode 100644
index 0000000..d077e9c
--- /dev/null
+++ b/ceval/ceval_result/business_administration_val_result.csv
@@ -0,0 +1,35 @@
+question,A,B,C,D,answer,llm_answer,is_right
+最早对管理的具体职能加以概括和系统论述的是____。,泰罗,法约尔,孔茨,韦伯,B,A,0
+消费者不知道,或者虽然知道但没有兴趣购买的物品为____。,便利品,选购品,特殊品,非渴求品,D,C,0
+当所在行业处于寿命周期的成熟期时,大企业应采取的竞争战略是____。,集中化战略,成本集聚战略,稳定型战略,差异化战略,D,C,0
+下列各项,对一般纳税企业来说,一般不构成外购存货成本的是____。,入库前的仓储费,进口关税,支付的增值税,运输费用,C,D,0
+《价格法》并没有对所有的价格歧视都加以禁止,应加以禁止的价格歧视行为是____,甲企业对乙企业和丙企业提供相同的商品或服务,但实行不同的价格,运输企业对老人、在岗人员和学生制定不同的价格,寒暑假期间,民航对乘坐飞机的教师可凭教师证给予7折优惠,电信部门对单位用市话和家庭用市话制定不同的收费标准,A,A,1
+某企业生产A、B和C三种商品,预计各自的销售量为1000件、500件和500件,三种产品的单位边际贡献分别为10、8和6,企业的固定成本为340000元。以下说法正确的是____。,产品组合产生的加权边际贡献为34,若依照预计销量的比例进行销售,则保本点的销售额为800000元,产品组合产生的加权边际贡献为8.5,产品组合的单位销售价格为80元,C,A,0
+对于自行开发取得的无形资产,在研究阶段发生的支出应计入____。,无形资产的成本,当期损益,投资收益,其他业务成本,B,A,0
+下列关于不存在减值迹象的可供出售金融资产会计处理的表述,正确的是____。,取得时将发生的相关交易费用计入当期损益,将出售的剩余部分重分类为交易性金融资产,资产负债表日将公允价值与账面价值的差额计入当期损益,将出售时实际收到的金额与账面价值之间的差额计入当期损益,D,A,0
+下列关于金融资产的后续计量的说法,不正确的是____。,资产负债表日,企业应以公允价值计量,且其变动计入当期损益的金融资产的公允价值变动计入当期损益,以摊余成本计量的金融资产在持有期间应当按照摊余成本和实际利率计算确认利息收入,计入投资收益,资产负债表日,以公允价值计量且其变动计入其他综合收益的金融资产应当以公允价值计量,公允价值大于取得成本的差额计入其他综合收益,资产负债表日,以公允价值计量且其变动计入其他综合收益的金融资产应当以公允价值计量,且其公允价值变动计入当期损益,D,B,0
+经营战略的本质是____。,目标与目的,资源,公司远景,组织结构,B,C,0
+下列各项中,不属于投资项目现金流出量内容的是____。,固定资产投资,折旧与摊销,无形资产投资,新增经营成本,B,D,0
+假设业务发生前速动比率大于1,偿还应付账款若干,将会____。,增大流动比率,不影响速动比率,增大速动比率,不影响流动比率,增大流动比率,也增大速动比率,降低流动比率,也降低速动比率,C,A,0
+当两个投资方案为互斥选择时,应优先选择____。,净现值大的方案,现值指数大的方案,内含报酬率大的方案,投资额小的方案,A,A,1
+已知某企业生产销售甲、乙两种产品,2005年销售量分别为10万件和15万件,单价分别为20元和30元,单位变动成本分别为12元和15元,单位变动销售成本分别为10元和12元,固定成本总额为100万元,则下列说法不正确的是____。,加权平均边际贡献率56,加权平均边际贡献率为46,盈亏临界点销售额为213,甲产品盈亏临界点销售量为3,A,A,1
+对于下列长期股权投资,应采用权益法核算的是____。,对联营企业的投资,不具有共同控制或重大影响关系且在活跃的市场中没有报价、公允价值,不能可靠计量的投资,对控股子公司的投资,不具有共同控制或重大影响关系,但在活跃的市场中有报价、公允价值,能够可靠计量的投资,A,D,0
+某企业只生产一种产品,单价5元,销量100件,变动成本率30%,则贡献边际为____。,150元,250元,350元,450元,C,D,0
+已知某投资项目的原始投资额为1500万元,建设期2年,投产后第1年到第5年每年净现金流量为50万元,第6年到第10年每年净现金流量为80万元,则该项目包括建设期的静态投资回收期为____年。,6.375,8.375,5.625,7.625,D,D,1
+边际贡献率和变动成本率____。,反方向变化,同方向变化,同比例变化,反比例变化,A,D,0
+企业内部环境分析的核心是对企业____的分析。,财务状况,研究与开发能力,核心能力,管理人员数量及素质,C,A,0
+存货的可变现净值是指____。,预计售价,现时的重置成本,预计售价减去进一步加工的成本和预计销售费用,存货的折现值,C,C,1
+下列各项中,属于狭义在产品的是____。,正在车间加工的在制品,本步骤已完工,但还需继续加工的半成品,已完工,但尚未验收入库的产成品,已完工,但需对外销售的自制半成品,A,D,0
+关于损失,下列说法中正确的是____。,损失是指由企业日常活动所发生的、会导致所有者权益减少的经济利益的流出,损失只能计入所有者权益项目,不能计入当期损益,损失是指由企业非日常活动所发生的、会导致所有者权益减少的、与向所有者分配利润无关的经济利益的流出,损失只能计入当期损益,不能计入所有者权益项目,C,D,0
+利润是边际贡献和____的差额。,变动成本,固定成本总额,销售收入,成本总额,B,D,0
+采用权益法核算长期股权投资时,对于被投资企业因外币资本结算引起的所有者权益的增加,投资企业应按所拥有的表决权资本的比例计算应享有的份额,将其计入____。,资本公积,投资收益,其他业务收入,营业外收入,A,C,0
+在两个方案的选择中,由于选择一个方案而放弃另一个方案所失去的可能收益就是所选方案的____。,差量成本,机会成本,边际成本,重置成本,B,C,0
+某股份有限公司对期末存货采用成本与可变现净值孰低法计价,2011年12月31日A库存商品的实际成本为40万元,预计进一步加工所需费用为16万元,预计销售费用及税金为8万元。该库存商品的市场销售价格为60万元。假定该公司以前年度未计提存货跌价准备,则2011年12月31日该项存货应计提的跌价准备为____万元。,0,4,16,20,B,C,0
+某公司2001年4月3日以448000元的价格,不准备长期持有的A公司发行在外的普通股200000股,另支付相关税费1000元。A公司已于2001年3月31日宣告发放现金股利0.2元/股,并于5月8日支付,A公司于2002年3月31日宣告发放现金股利0.3元/股,某公司于2002年4月3日将该股票转让,则某公司转让股票时成本价为____元。,408000,409000,448000,349000,A,A,1
+甲企业以融资租赁方式租入乙企业的一台机械设备,则下列对该设备计提折旧的叙述中,正确的是____。,甲企业对该设备不计提折旧,甲企业应采用与自有资产相一致的会计政策,如果能够合理确定该设备在租赁期届满时甲企业将会取得此设备的所有权,则应以该设备全新时的预计使用年限作为折旧期间,该设备的折旧方法和折旧年限应按乙企业有关资产的折旧方法和折旧年限处理,B,C,0
+证券市场线可以用来描述市场均衡条件下单项资产或资产组合的期望收益与风险之间的关系。当投资者的风险厌恶感普遍减弱时,会导致证券市场线____。,向下平行移动,斜率上升,斜率下降,向上平行移动,C,A,0
+如果行业的买方集中度高,由少数几家企业控制,那么它们在价格谈判中会处于的地位是____。,十分不利,相对不利,相对有利,十分有利,C,A,0
+“预付账款”科目明细账中若有贷方余额,应计入资产负债表中的____项目。,应收账款,应付账款,预收账款,预付账款,B,A,0
+基层组织的管理者主要承担____。,决策职能,控制职能,承上启下的职能作用,协调职能,B,C,0
+产品组合的长度是____。,产品大类的数量,产品项目的数量,产品大类的相关程度,产品大类的序列,B,C,0
+-,-,-,-,-,文件 business_administration_val.csv 的正确率: 18.18%,-,-
diff --git a/ceval/ceval_result/chinese_language_and_literature_val_result.csv b/ceval/ceval_result/chinese_language_and_literature_val_result.csv
new file mode 100644
index 0000000..dd0c1a4
--- /dev/null
+++ b/ceval/ceval_result/chinese_language_and_literature_val_result.csv
@@ -0,0 +1,25 @@
+question,A,B,C,D,answer,llm_answer,is_right
+英语名词“lab”(实验室)原来的形式是“laboratory”,这在词的形成方式上属于____。,直接成词,变形成词,变性成词,逆序成词,D,C,0
+音位是____。,最小的语音语义结合体,一个语音系统中能区别意义的最小语音单位,语言中最小的能区别意义的语音特征单位,语言中有区别性特征的最小语音单位,B,A,0
+下列语言中,属于印欧语系拉丁语族的语言是____。,英语,俄语,德语,法语,D,C,0
+《史记·高祖本纪》:“今则来,沛公恐不得有此。”句中“则”的用法是____。,表示承接,表示转折,表示假设,表示因果,C,C,1
+《四世同堂》中的冠晓荷是____。,老派市民的形象,革命者的形象,正直的知识分子形象,民族败类的形象,D,C,0
+“塑”这个字的音节的声母是____。,sh,x,s,零声母,C,C,1
+《论语·述而》:“若圣与仁,则吾岂敢?抑为之不厌,诲人不倦,则可谓云尔已矣。”句中“抑”的用法是____。,表示假设,表示转折,表示原因,表示目的,B,C,0
+下列有歧义的一项是____。,进口汽车,路边种着树,洗得干净,咬死了猎人的鸡,D,D,1
+[p][p‘][b][m]四个音素的区别主要是____。,清浊,发音方法,发音部位,送气与否,A,D,0
+____是中国古代蒙学教育所采用的一种识字课本,由周兴嗣编撰而成。,《三字经》,《弟子规》,《百家姓》,《千字文》,D,C,0
+卢舍那大佛是石窟雕塑中____的代表作。,云冈石窟,龙门石窟,莫高窟,麦积山石窟,B,C,0
+目前最通用的楷书印刷体是____。,宋体,仿宋体,楷体,黑体,A,C,0
+下列句中,不属于宾语前置的是____。,姜氏何厌之有?,斥鷃笑之曰:“彼且奚适也?”,居则曰:“不吾知也!”,胡不见我于王?,D,C,0
+“员、可、乡、分、展、从”中包含的自由语素是____。,乡、分、从,可、分、展,员、乡、分,员、可、乡,A,D,0
+《杜十娘怒沉百宝箱》出自____。,《传奇》,《聊斋志异》,“三言”,“二拍”,C,C,1
+剧作家____被誉为“中国十六世纪的莎士比亚”。,关汉卿,白朴,汤显祖,马致远,C,C,1
+《三国志·蜀书·诸葛亮传》:“每自比于管仲、乐毅,时人莫之许也。”句中“于”的用法是____。,表对象,表比较,表原因,表施事,A,C,0
+《本草纲目》的作者是____李时珍。,南宋人,明朝人,元朝人,清朝人,B,C,0
+“我、你、他”是____。,指示代词,疑问代词,人称代词,名词,C,A,0
+关于词的本义和基本义的关系,下列表述正确的是____。,完全不一致,完全一致,本义比基本义更常用,基本义比本义更常用,D,C,0
+“现在轮到你说了”是____。,连谓句,兼语句,被动句,双宾语句,B,C,0
+“队”与“坠”是____。,古今字,异体字,通假字,繁简字,A,A,1
+d、t、n、l四个辅声的发音部位是____。,舌尖前,舌面,舌尖中,舌尖后,C,D,0
+-,-,-,-,-,文件 chinese_language_and_literature_val.csv 的正确率: 26.09%,-,-
diff --git a/ceval/ceval_result/civil_servant_val_result.csv b/ceval/ceval_result/civil_servant_val_result.csv
new file mode 100644
index 0000000..6892cf7
--- /dev/null
+++ b/ceval/ceval_result/civil_servant_val_result.csv
@@ -0,0 +1,49 @@
+question,A,B,C,D,answer,llm_answer,is_right
+甲与乙准备进行一个游戏:向空中扔三枚硬币,如果它们落地后全是正面向上或全是反面向上,乙就给甲钱;但若出现两正面一反面或两反面一正面的情况,则由甲给乙钱。乙要求甲每次给10元,那么,从长远来看,甲应该要求乙每次至少给____元才可考虑参加这个游戏。,10,15,20,30,D,A,0
+下列著名诗人与其代表作对应有误的是____。,李白——《将进酒》,白居易——《琵琶行》,王之焕——《登鹳雀楼》,杜甫——《长恨歌》,D,C,0
+经济学上所推崇的“橄榄型”收入分配结构,是指低收入和高收入相对较少、中等收入占绝大多数的分配结构。我国正在采取措施,实施“提低、扩中、调高、打非、保困”的方针,使收入分配朝着“橄榄型”方向发展。这主要是为了促进____。,生产的发展,效率的提高,社会的公平,内需的扩大,C,C,1
+-81,-36,-9,0,9,36,____,49,64,81,100,C,C,1
+VIP服务本来是个好东西,大企业作为市场竞争的主体,实行差别化服务,无可厚非。但近年来,一些企业纷纷进军医院、机场、车站等公共场所,掏些赞助费,设立所谓“贵宾厅”,霸占公共资源,不仅带来浪费,更造成公共资源分配的不公。这段文字主要强调的是____。,公共资源不该过度VIP,VIP服务导致了公共资源的不公平分配,一些企业搬进医院、机场、车站办公,实行差别化服务是VIP服务的优势所在,A,C,0
+2,5,8,12,17,24,____,30,32,34,36,C,C,1
+4,4,6,12,30,____,48,64,80,90,D,C,0
+当下中国文学描写官斗、职斗、婚斗、家斗的作品比较流行,这些作品中包含了不少对日常生活中权术和心机的描写。这样的写作有可能削弱文学对社会的积极影响。文学有必要与正义结盟,形成诗性正义,以提升生活。 作者想表达的主要观点是____。,当下文学作品的社会影响力有下降的趋势,流行作品未必是好作品,这需要时间的检验,文学不应过度渲染权术机诈,否则有可能泯灭正义,生活中没那么多权术机诈,文学创作应该贴近生活,不能闭门造车,C,A,0
+一天,一个农民的驴子掉到枯井里,那可怜的驴子在井里凄凉地惨叫了几个钟头,农民亦急得团团转,就是毫无办法把它救起来,最后,他断然认定:驴子已老了,这口枯井也该填起来,不值得花精力去救驴子。他请来所有邻居帮他填井。大家抓起铁锹,开始往井里填土。驴子很快意识到发生了什么事,起初,它恐慌地大哭,不一会儿,居然安静下来。人们忍不住往井里看,奇迹发生了。每一铲砸到驴子背上的土,它都作了出人意料的处理:迅速抖落一身尘土,然后狠狠地用脚踩紧。这样,没过多久,驴子竟然自己把自己升了起来,到了井口,它纵身一跳,平安地跑开了,在场的人均惊诧不已。 这段文字告诉我们的道理是____。,人生中的每一个困难都是通往成功的垫脚石,换一种思维常常能够产生意想不到的效果,冷静思考是克服困难的首要条件,求人不如求己,很多时候,自己才是自己最大的救星,A,B,0
+在现代社会,教育符号也即文凭和学历是一种重要的文化货币,手持符号资本,可进入相应职业群体、身份团体和社会位置。譬如,凭借医学博士文凭,可成为医生。此为教育的筛选功能,亦被喻为人才的分类编码场,如同公共汽车总站,目的地不同的人选择不同的路线,乘坐不同的车辆,到达不同的地方。 下列选项不符合文意的一项是____。,文凭与学历都是符号资本,教育符号是人才的分类编码,文凭体现了教育的筛选功能,手持相应的符号资本才能进入相应的职业群体,D,C,0
+侯方域:《桃花扇》____,蒲松龄:《聊斋志异》,石头记:《红楼梦》,崔莺莺:《西厢记》,秦始皇:《后汉书》,C,C,1
+____全党同志和全国人民团结一心,坚持不懈地奋斗,不断取得扎扎实实的成效,我们____一定能够使社会主义新农村建设真正成为惠及广大农民群众的民心工程。 填入画横线部分最恰当的一项是____。,如果 就,只有 才能,只要 就,倘若 也就,C,A,0
+下列关于世界银行的说法中不正确的是____。,原名国际复兴开发银行,于1944年开始营业,它是联合国下属的一个专门机构,是负责长期贷款的国际金融机构,贷款期限较长,一般为数年,最长可达30年,A,C,0
+外资银行进入新兴市场国家,新兴市场国家银行业的各主体为了维持自身的生存,会尽可能争取较大的市场份额,充分拓展自身竞争优势,努力向客户提供质优价廉的金融产品和金融服务,这个过程必然带动银行业微观效率的提升。 “这个过程”指的是____。,外资银行进入新兴市场国家的过程,新兴市场国家银行业发展的过程,外资银行提供优质服务的过程,新兴市场国家银行业扩大市场份额的过程,A,A,1
+按照行政层级标准来划分,我国政府机构的类型有____。,一般地方国家行政机关和基层国家行政机关两大类,常设机构与非常设机构两类,领导机构、办公办事机构、职能机构和派出机构四类,中央国家行政机关和地方国家行政机关两大类,D,A,0
+在某市一项对公司年轻人员的最新调查中,与往年相比,今年有70%的人打算购买房屋,这一比例已达到历史最高值。然而,在房屋管理局的统计中,该市今年的房屋成交量却比往年有所下降。以下哪项如果为真,最不能解释上述现象?____,一些打算购买房屋的年轻人目前并不具备该市购买房屋的条件,往年资料表明,年轻人员购买房屋的比例不足购买房屋成员的30%,近年来爆发的金融风暴,对房地产行业有一定的打击,近几个月该市楼市价格不稳定,使得一些购房者持观望态度,C,A,0
+我们以往所理解的“现代化”概念仅仅局限于物质层面,局限于表层经济现代化,这也是迟发展国家长期存在的一个普遍性问题:在物质层面上求变的欲望很强,而在制度层面和观念层面上却是文化守成主义的,这种状况对于现代化实际进程的影响自不必说,它对于学术的影响是导致知识的流俗化。不断地更换新词语,在新词语的装潢下重复古老的思想观念,结果是词语和口号不断地更换而社会精神气质则没有实质性的变化。 这段文字要表达的主要意思是____。,现代化应包括物质的、制度的、观念的三个层面,片面理解现代化是迟发展国家长期存在的一个普遍性问题,物质层面的落后现状是迟发展国家片面理解现代化的一个重要因素,片面理解现代化会导致知识的流俗化,A,C,0
+皮肤破损出血、颈髓损伤、锐器插入体内、严重挤压伤等是灾害发生时的常见损伤类型.掌握科学的自救方法对于延续生命、等待救援很重要。下列自救措施中,恰当的是____。,锐器插人体内后,应快速将锐器拔出,简单处理伤口后立即送往医院救治,对颈后锐痛、活动时疼痛加剧等症状,即用颈托,一时无颈托,可临时用敷料、硬板纸或塑料板做成颈圈固定颈部,伤口发生喷射状出血时,应立即用厚消毒纱布(或毛巾)包扎好伤口,被重物挤压引起肢体肿胀或青紫时,应尽快在患处用热毛巾湿敷消肿,B,C,0
+多年以来,医生和家属对待癌症患者大多采取这样的态度:即向患者隐瞒已得癌症的实情,这样的做法在医学上叫作“保护性医疗”,其目的在于减少患者的心理负担。但是,某肿瘤医生新设立的康复科的张主任却主张实行“公开性治疗”。 由此可推知下文将要论述的是____。,家属对实行“公开性治疗”的态度,“保护性医疗”的弊端,“公开性治疗”将使病情得到控制和好转,“公开性治疗”的含义和形式,D,D,1
+古人归纳总结出许多观天象识天气的谚语。下列与天气变化无关的谚语是____。,朝霞不出门,晚霞行千里,天上鱼鳞云,地下雨淋淋,东风是个精,不下也要阴,百日连阴雨,总有一日晴,D,C,0
+从《论语》看,孔子对音乐的重视,可以说远远超出了后世那些尊敬他的人的想象,这一方面来自他对于乐的精神艺术的新发现。艺术,只在人们精神的发现中才存在,可以说,就现在见到的材料看,孔子可能是中国历史上最伟大的艺术精神的发现者。这段文字重点强调____。,孔子在音乐方面的成就与贡献,后人评价孔子时所存在的偏颇,艺术精神在乐教传承中的作用,《论语》作为文献的重要意义,A,C,0
+①当地球撞进尘埃带时,从地球上看,是短时间内无数尘埃以极高的速度划破大气层下落 ②因此,流星雨实际上是彗星留下的无数尘埃形成的 ③进入大气层的尘埃被大气加热,发出明亮的光 ④彗星释放出的尘埃,并非顷刻扩散到宇宙空间,消失得无影无踪,而是留在彗星的轨道上继续公转 ⑤这样看上去就有许多流星,也就是流星雨 ⑥这样形成的“尘埃带”,有些和地球的公转轨道交叉 将以上6个句子重新排列,语序正确的是____。,④②⑥③⑤①,①④③⑥⑤②,④⑥①③⑤②,①③⑤②④⑥,C,A,0
+3,7,16,107,____,1704,1072,1707,1068,C,C,1
+我始终____,开始在内心生活得更严肃的人,也会在外在上开始生活得更____。在一个奢华浪费的年代,我希望能向世界____,人类真正要的东西是非常之微小的。 填入画横线部分最恰当的一项是____。,确认 朴素 表明,相信 质朴 证明,确认 质朴 证明,相信 朴素 表明,D,C,0
+一特殊跑道为正三角形,某运动员用6米/秒的速度跑一圈耗时50秒,问该运动员提速10%后从跑道的某个顶点横穿跑道跑向对边,问最少约需多少秒可踏足对边?(四舍五入到个位)____,9秒,10秒,13秒,15秒,C,A,0
+文学资料在思想史领域著作中,被使用得还是相当少。其实,作为记述史实的历史,可能对有些夸张和虚构的小说需要警惕,但是,作为考察理性和情感的思想史,却不必胶柱鼓瑟或因噎废食,任何文学作品也许在事实上有想象,但在语言、立场和情感上,却仿佛“当堂呈供”,并不能把自己的本相全盘隐匿。 对这段文字的主旨理解最准确的是____。,文学作品呈现艺术的真实,思想史研究应体现理性和情感,文学资料可以作为思想史研究的史料,思想史研究中要慎用文学资料,C,A,0
+下列关于国际组织的表述不正确的是____。,石油输出国组织通过实行石油生产配额限制维护石油生产国利益,博鳌亚洲论坛是第一个总部设在中国的国际会议组织,蒙古国是上海合作组织的成员国之一,国际货币基金组织是联合国的专门机构,C,C,1
+实验证明,植物体内含有一种觉察光的蛋白质,可以“分辨”光的强弱。这种能力很可能使植物看到人类视力所看不到的波长,而且具有较高的灵敏度。植物能感觉光照射过来的方向,光使植物知道早上什么时候该醒来,同样也能促使植物额外分泌栎精和堪非醇这两种无色色素,他们能过滤强烈的阳光,充分发挥遮光剂的作用,从而保护植物免受紫外线的强烈照射。 这段文字主要介绍的是____。,植物是怎么辨别方向的,植物是如何避免阳光暴晒的,植物具有一定意义上的“视觉”,感知阳光对植物生长的重要性,C,C,1
+1,10,37,82,145,____,170,197,224,226,D,C,0
+某县在一次招商引资活动中,投资商刁难引资方说:“我有三个项目:环境项目、旅游项目和化工项目。如果你说的话是正确的,我会把其中一个项目投资到贵县,但是如果你说的话是错误的,我就一个项目也不投资。”引资方当然想获得环境项目,那么引资方该如何说呢?____,你不会把环境项目或旅游项目投资到我县,你不会把环境项目或化工项目投资到我县,你不会把旅游项目或化工项目投资到我县,你不会把旅游项目和化工项目都投资到我县,C,A,0
+民意“被满意”,民众“不满意”,甚至“很生气”。尊重民意、顺应民意、采纳民意是服务型政府的执政要义,是政治文明建设的题中之意。民意的力量一方面取决于民意征集占全民的比例,即广泛性;另一方面也体现在政府对民意的尊重程度上。保障民众的知情权、参与权、表达权和监督权,就是要随时随地与民众进行多种途径的沟通、交流。民意内涵民智,民意关乎民生。我们不仅要从民意中看到民众欢迎什么、反对什么,为科学决策提供依据,而且要充分发挥民智的作用。尊重民意、吸纳民智是科学决策的重要保证,也是衡量政府亲民为民的重要标志。阅读上面文字,最符合文意的一项是____。,让民众“不满意”“很生气”的政府就不是服务型政府,知情权是监督权的前提,参与权是表达权的前提,尊重民意、吸纳民智是科学决策的决定性因素,民意力量的发挥取决于民意征集的广度和尊重民意的程度,D,A,0
+3,5,16,82,1315,____,107834,12849,12847,108847,A,C,0
+下列可以反映气候垂直变化的诗句是____。,东边日出西边雨,道是无晴却有晴,罗浮山下四时春,卢橘杨梅次第新,人间四月芳菲尽,山寺桃花始盛开,横看成岭侧成峰,远近高低各不同,C,C,1
+日本松下公司日前在东京“松下中心”向当地媒体展示了其面向未来的“零排放概念环保房屋”。环保屋的主要特点是“节能、创能、蓄能”。“节能”就是提高对自然界既有资源的利用率,同时采用环保隔热的建筑材料以及最先进的环保节能家电设备等。 下文最有可能介绍的是____。,环保屋是怎样设计出来的,环保屋的创能、蓄能特点,环保屋的推广,环保屋的材料,B,A,0
+下列没有歧义的一项是____。,几个派出所的民警。,法院门前的石狮子。,这份起诉书我写不好。,咬死了主人的藏獒。,B,A,0
+我们发现零工制度有一个重要的支持机制就是完善的、科学化的员工培训系统。几乎所有的现代企业和公司都非常重视内部培训,有的企业主甚至成为了培训狂,哪怕有一秒钟的空闲也要为员工安排一次培训。但真正有效的培训并不是无休止的洗脑和课程轰炸,不是“潜能激发”和“感恩教育”,而是适合公司运营需求的专业性、针对性、科学性的业务训练。这种培训机制如果能够建立起来,无论你是否采用零工制度都会对企业的发展起到重要的推动作用。 这段文字意在说明____。,很多公司培训缺乏科学性,科学的员工培训对企业很重要,零工制度不一定适合所有企业,过度培训可能会造成相反效果,B,C,0
+全国人民代表大会举行会议时,主持大会正式会议的是____。,全国人大常委会,大会主席团,全国人大常委会委员长,大会秘书长,B,C,0
+改革开放以来,中国农学会____“献身、创新、求实、协作”的宗旨,始终不渝地坚持以推动农业科技进步、促进农村发展为己任,大力开展学术交流和科技普及,积极____和举荐人才,为提高广大农民科技素质、加快农业科技进步作出了重要贡献。 填入画横线部分最恰当的一项是____。,继承 出谋划策,继承 建言献策,秉承 建言献策,秉承 出谋划策,C,C,1
+0, 4, 3, 10, 6, 7, ____,101,102,103,104,B,C,0
+“新生代散文”作家大多有写现代诗的背景,诗人所拥有的____的思维、大胆的想象、敏锐的感觉,将“诗质”____在散文语言的血液和肌理里。这不同于平铺直叙式的浅浮的诗意,而是自我心灵的体认中____而成的诗质。 填入画横线部分最恰当的一项是____。,跳脱 镶嵌 凝结,另类 浓缩 升华,感性 渗透 铸就,活跃 散播 提炼,A,C,0
+据《咬文嚼字》编辑部透露,编制年度“十大流行语”是一项十分严肃的事,既要____到词语在当年的流行度,又要从语文伦理角度加以必要的____,选优汰劣,力争通过“十大流行语”向社会____正能量。 填入画横线部分最恰当的一项是____。,斟酌 估量 传播,思考 权衡 传送,思索 考察 传达,考虑 考量 传递,D,A,0
+20世纪60年代以前,世界各国普遍注重防洪的工程措施,即通过修建大堤、水库水利设施对洪水进行控制。但在60年代以后,世界各国在防洪规划中越来越重视非工程措施的运用,即通过洪水预警、灾情评估、洪灾保险等多种手段,结合各种工程措施,从而尽可能减少洪灾对人类经济、环境和社会发展的影响。 这段文字主要谈的是____。,世界各国防洪理念的转变,世界各国控制洪水的新途径,单纯重视防洪工程不能有效控制洪水,非工程措施逐渐成为防洪规划的主导,A,A,1
+近年来,国家房地产调控措施的出台十分密集,除了增加公共租赁住房供应外,再加上央行加息,多个城市出现了房屋成交量下跌的态势,房价涨幅开始放缓。这表明____。,国家通过宏观调控平衡供求关系,价格的波动通过供求关系表现出来,宏观调控是资源配置的基础性手段,宏观调控可以克服市场调节的滞后性,A,A,1
+学生在操场上列队做操,只知人数在90-110之间。如果排成3排则不多不少:排成5排则少2人;排成7排则少4人。问学生人数是多少人?____,102,98,104,108,D,B,0
+有人说:人本是散落的珍珠,随地乱滚。文化就是那极____又强韧的细线,将珠子串起来成为社会。也有人说:文化犹如空气中的氧气,自然界的春雨,不可或缺却____,飘飘洒洒,润物无声。可见,文化资源价值是无法用尺度衡量的。 填入画横线部分最恰当的一项是____。,柔弱 视之无形,纤细 不可名状,结实 视而不见,薄弱 不可捉摸,A,C,0
+政府职能与成本问题一直备受争议,但这方面的研究似乎还处于一种观点与立场远未一致的状态,一个重要原因是研究视角与方法的局限。大体上看,这类研究有两条思路,一条是信守新古典经济学理论预设,认为市场可以有效解决经济社会发展中的问题,持“小政府”观点;另一条是信守政府干预主义理论预设,认为政府不时干预是市场能够健康运转的必要条件。笔者认为,要解决这种困境,必须有新的理论视野和新的研究方法,而新兴古典经济学理论就是其中之一。 这段文字接下来最有可能讲述的是____。,新兴古典经济学的理论框架与研究方法,新理论视野对提高政府的行政效率有何帮助,新古典经济学理论预设的局限性,政府职能与成本之间矛盾难解的原因,A,C,0
+2009年有两次“立春”,很容易让人联想到“第二春”“二度春”,可想而知这样的婚姻不稳定,所以网络上有“2009年不能结婚,或者2009年爱情不会长久”等传闻。但是,大多数年轻人认为,登记结婚是件水到渠成的事,不会因为赶日子仓促提前或延迟。 根据这段文字,下列说法正确的是____。,作者认为2009年不适合结婚,大多数年轻人认为2009年是结婚的好年头,2009年结婚会使婚姻不稳定的说法是无稽之谈,大多数年轻人不会因为2009年有两次“立春”而改变自己的结婚计划,D,B,0
+-,-,-,-,-,文件 civil_servant_val.csv 的正确率: 27.66%,-,-
diff --git a/ceval/ceval_result/clinical_medicine_val_result.csv b/ceval/ceval_result/clinical_medicine_val_result.csv
new file mode 100644
index 0000000..970835d
--- /dev/null
+++ b/ceval/ceval_result/clinical_medicine_val_result.csv
@@ -0,0 +1,24 @@
+question,A,B,C,D,answer,llm_answer,is_right
+附睾精液囊肿____,精子自附睾管溢出至周围组织形成的囊肿,睾丸网或输出小管扩张形成囊肿,囊肿液中可见精子或精子碎片,囊肿为质硬的圆形包块,囊肿与周围组织界限不清,B,C,0
+有关非ST段抬高心肌梗死的治疗方案,以下哪项正确____,溶栓是基础治疗,低危险组不必使用药物,中危险组以阿司匹林和肝素尤其是低分子量肝素治疗为主,高危险组以介入治疗为首选,D,C,0
+慢性胃溃疡最常见的并发症是____,幽门狭窄,穿孔,出血,癌变,C,C,1
+男性,65岁。昏迷10小时。血气分析:$pH$ 7.26,$PaCO_2$82mmHg,$PaO_2$ 45mmHg。下列哪项处理不适宜____,机械通气,呼吸兴奋剂,支持疗法,高浓度给氧,D,C,0
+与肝癌发病无关的因素是____,甲型肝炎,乙型肝炎,酒精性肝硬化,坏死后性肝硬化,A,C,0
+某患者,5月前曾进行链激酶治疗心肌梗死,现出现大面积肺血栓栓塞症,应该如何治疗____,继续使用链激酶溶栓,用尿激酶溶栓,严禁用任何的溶栓药,立刻使用负荷量的华法林,B,C,0
+不符合病毒性肺炎描述的是____,病灶可呈小叶性分布,肺泡腔内有大量渗出物,可有透明膜形成,肉眼观病变不明显,B,C,0
+基底细胞癌最常见的好发部位是____,小阴唇,前庭,阴蒂,大阴唇,D,C,0
+急性呼吸衰竭最早出现的症状是____,发绀,呼吸困难,精神错乱,心动过速,B,C,0
+Ⅱ型呼吸衰竭不能给予高浓度吸氧的原因主要是____,伴$CO_2$潴留,诱发呼吸性碱中毒,降低了颈动脉体、主动脉体的兴奋性,可引起氧中毒,C,C,1
+洋地黄中毒合并低钾血症时易发生的心律失常是____,阵发性房性心动过速伴传导阻滞,房性期前收缩,房颤,室颤,A,C,0
+下列哪项不符合糖尿病酮症酸中毒的实验室检查结果____,pH<7.35,血酮体>4.8mmol/L,血糖多>33.3mmol/L,血白细胞计数>10×10^9 /L,C,C,1
+男性,70岁。慢性阻塞性肺病史20余年,神志不清5小时。体检:发绀,呼吸浅促,心率:120次/分,律齐,两肺闻及干湿啰音。血压75/45mmHg。血pH7.18,$PaCO_2$ 5mmHg,$PaO_2$ 49mmHg。此时哪项治疗措施不适宜____,呼吸机辅助通气,纠正低血压,持续低浓度吸氧,呼吸兴奋剂,A,C,0
+女性,29岁,因牙龈渗血伴低热1周入院,实验室检查血常规:白细胞11.3×10^9 /L、红细胞2.6×10^12 /L、 血红蛋白68g/L、血小板30×10^9 /L,骨髓穿刺结果提示急性单核细胞白血病(M5),以下哪个体征最少见____,牙龈肿胀,胸骨压痛,肝辟肿大,巩膜黄染,D,C,0
+早期胃癌最多见的类型是____,隆起型,表浅型,凹陷型,表浅平坦型,C,C,1
+最易和肺转移癌相混淆的是____,中央型肺癌,周围型肺癌,弥漫型肺癌,大细胞肺癌,C,C,1
+治疗糖尿病酮症酸中毒时最应注意的电解质紊乱是____,低钠血症,低钾血症,高氯血症,高钙血症,B,C,0
+硅尘可导致硅沉着病,其中致病力最强的硅尘颗粒直径为____,>5μm,4~5μm,2~3/μm,1~2μm,D,C,0
+X线片上最易与周同型肺癌相混淆的肺结核病是____,结核球,原发综合征,局灶型肺结核,结核性胸膜炎,A,C,0
+风湿性心脏瓣膜病最常受累的瓣膜是____,二尖瓣,三尖瓣,肺动脉瓣,主动脉瓣,A,C,0
+急性粒细胞性白血病时,瘤细胞在骨膜下浸润,聚集成肿块,称为____,棕色瘤,黄色瘤,绿色瘤,Wilms瘤,C,C,1
+下列哪项检查不是反映慢性溶血性贫血时骨髓代偿增生的证据____,骨髓代偿增生,末梢血出现幼红细胞,骨髓幼红细胞增生活跃,外周血涂片发现红细胞碎片,D,C,0
+-,-,-,-,-,文件 clinical_medicine_val.csv 的正确率: 27.27%,-,-
diff --git a/ceval/ceval_result/college_chemistry_val_result.csv b/ceval/ceval_result/college_chemistry_val_result.csv
new file mode 100644
index 0000000..20c5de2
--- /dev/null
+++ b/ceval/ceval_result/college_chemistry_val_result.csv
@@ -0,0 +1,26 @@
+question,A,B,C,D,answer,llm_answer,is_right
+"$HO_2$的沸点比$H_2S$的高,可以从____角度来进行解释。",共价键牢固,变形性,氢键,分子间力,C,A,0
+"$Mg(OH)_2$饱和溶液中的$[OH^-]=10^{-4}mol/dm^3$,若往该溶液中加入NaOH溶液,使溶液中$[OH^-]$的浓度变为原来的10倍,则$Mg(OH)_2$的溶解度在理论上将____",变为原来的$10^{-3}$倍,变为原来的$10^{-2}$倍,变为原来的$10$倍,不发生变化,B,C,0
+估计下列分子或离子中,键角最小的是____,$NH_3$,$NO_3^{-}$,$NF_3$,$NCl_3$,C,C,1
+已知$K_b^{\theta}(NH_3·H_2O)=1.8×10^{-5}$,用$NH_3·H_2O$和$NH_4Cl$配制pH=9.00的缓冲溶液时,c($NH_3·H_2O$)/c($NH_4Cl$)=____,5.6,0.56,1.8,3.6,B,C,0
+根据酸碱质子理论、HCI、HAc和$NH_4^+$酸性由弱到强的顺序为____,$NH_4^+ 0$,$P(X_1^2+X_2^2+X_3^2+X_4^2\le kX_5^2)=\alpha$则k=____","$\frac{1}{4}F_{\alpha}(4,1)$","$\frac{1}{4}F_{1-\alpha}(4,1)$","$4F_{\alpha}(4,1)$","$4F_{1-\alpha}(4,1)$",D,A,0
+"设$X_1,X_1,\cdots X_8$为来自总体$X\sim N\left(\mu_1,1\right)$的简单样本,$\bar{X},S_1^2$分別是其对应的样本均值与样本方差。$Y_1,Y_1,\cdots,Y_7$为来自总$Y\sim N\left(\mu_2,1\right)$的简单样本,$\bar{Y},S_2^2$分别是其对应的样本均值与样本方差。下列选项正确的是:____",$\sum_{i=1}^8\left(X_i-\mu_1\right)^2+\sum_{i=1}^7\left(Y_i-\mu_2\right)^2 \sim \chi^2(15)$,$E\left(\sum_{i=1}^8\left(X_i-\mu_1\right)^2+\sum_{i=1}^7\left(Y_i-\mu_2\right)^2\right)=15$,$\mathrm{D}(\bar{X}+\bar{Y})=\frac{1}{8}+\frac{1}{7}$,"$\bar{X}-\bar{Y} \sim \mathrm{N}\left(\mu_1-\mu_2, \frac{1}{8}+\frac{1}{7}\right)$",B,A,0
+"若随机变量X的分布函数为$F(x)=pF_1(x)+qF_2(x)$,其中$F_1(x)$,$F_2(x)$为两个分布函数,常数p,q满足:$p>0$,$q>0$,$p+q=1$,那么X的分布叫作$F_1(x),F_2(x)$的混合分布.设$\mu_1,\mu_2$分别为$F_1(x),F_2(x)$的期望,$\sigma_1^2,\sigma_2^2$分别为$F_1(\mathrm{x})$,$F_2(\mathrm{x})$的方差,则$DX=$____",$p \sigma_1^2+q \sigma_2^2$,$p^2 \sigma_1^2+q^2 \sigma_2^2$,$p \sigma_1^2+q \sigma_2^2+p q\left(\mu_1-\mu_2\right)^2$,$p \sigma_1^2+q \sigma_2^2+p q\left(\sigma_1-\sigma_2\right)^2$,C,A,0
+-,-,-,-,-,文件 probability_and_statistics_val.csv 的正确率: 11.11%,-,-
diff --git a/ceval/ceval_result/professional_tour_guide_val_result.csv b/ceval/ceval_result/professional_tour_guide_val_result.csv
new file mode 100644
index 0000000..18c663b
--- /dev/null
+++ b/ceval/ceval_result/professional_tour_guide_val_result.csv
@@ -0,0 +1,31 @@
+question,A,B,C,D,answer,llm_answer,is_right
+《临川先生文集》中的“王临川”用的是____称谓。,别号,官爵,地望,排行,C,C,1
+广东风味小吃有____。,猫耳朵,五芳斋粽子,双皮奶,虾爆鳝面,C,C,1
+我国国内卫星通信网正式建成于____。,1980年,1983年,1986年,1988年,C,C,1
+东汉由西汉王室刘秀创建,建都____。,长安,洛阳,成都,建业,B,C,0
+对我国名山的描述正确的是____。,泰山有“天下第一山”之称,被列入世界自然与文化双重遗产,黄山有“五岳独秀”之称,被列入世界自然与文化双重遗产,华山最高峰南峰海拔1800米,自古以险闻名遐迩,衡山有七十二峰,三大主峰莲花峰、天都峰、祝融峰都超过1800米,A,A,1
+中国历史上第一个奴隶制国家政权夏朝建立在____。,晋南,洛阳,安阳,二里岗,A,C,0
+上海豫园鱼乐榭有一上实下空的墙,遮挡了原来流水较近的短处,产生了源远流长的效果,这是____的神来之笔。,抑景,框景,借景,障景,D,C,0
+“霸王别姬”是____的代表菜。,苏菜,鲁菜,浙菜,湘菜,A,C,0
+我国的“四大名砚”中,唯一不用岩石为砚材制作原料的是____。,端砚,歙砚,澄泥砚,洮河砚,C,A,0
+黑龙江在我国境内的最长支流是____。,松花江,海河,鸭绿江,辽河,A,C,0
+西周时的商高是见于著述的中国古代第一位____。,农学家,医学家,数学家,天文学家,C,C,1
+被誉为“四大国宝”的我国一级保护动物是____。,藏羚羊、白唇鹿、白鳍豚、金丝猴,华南虎、白鳍豚、亚洲象、大熊猫,大熊猫、金丝猴、白鳍豚、白唇鹿,金丝猴、东北虎、白唇鹿、亚洲象,C,C,1
+英国为了打开中国市场,在____发动了鸦片战争,清朝战败。,1780年,1820年,1840年,1860年,C,C,1
+我国现存最古老的木结构建筑位于____。,山西五台县南禅寺,山西五台县佛光寺,山西五台县塔院寺,山西芮城县广仁王庙,A,A,1
+____是伦敦的标志性建筑之一,有英国最大的钟。,伦敦塔桥,伊丽莎白塔,圣保罗教堂,海德公园,B,C,0
+无锡寄畅园因园外惠山的景色而显得更加秀丽。产生这一效果的构景手法是____。,借景,添景,抑景,障景,A,A,1
+世界旅游组织总部设在____。,旧金山,曼谷,海牙,马德里,D,C,0
+《梦溪笔谈》被称为“中国科学史上的坐标”,其作者是____。,沈括,祖冲之,徐霞客,吴敬梓,A,C,0
+北方园林尤以____为代表。,开封,西安,洛阳,北京,D,C,0
+我国成功发射的世界首颗量子科学实验卫星叫____。,“墨子号”,“玉兔号”,“嫦娥号”,“天宫号”,A,C,0
+我国最大的丛书是____。,《吕氏春秋》,《永乐大典》,《古今图书集成》,《四库全书》,D,C,0
+郁金香真正的原产地是____。,荷兰,土耳其,法国,意大利,B,C,0
+清乾隆年间“四大徽班进京”对京剧艺术的形成影响深远,四大徽班中最早进京演出并大获成功的是____。,和春班,四喜班,三庆班,春台班,C,C,1
+李求真在“万里晴空,几片闲云浮海角;一湾碧水,八方游子恋天涯”联语中巧妙地嵌入了“海角天涯”四个字,它是____省的旅游名胜。,广东,广西,贵州,海南,D,C,0
+下列园林建筑中,____形式优美且不讲究对称布局。,榭,轩,亭,廊,B,D,0
+元大都是按照____传统都城的布局建造的。,汉族,藏族,蒙古族,满族,A,C,0
+被称为“土族梁祝”的叙事长诗是____。,《牛达的传说》,《汗庆格尔》,《祁家延西》,《拉仁布与吉门索》,D,C,0
+下列风物特产中,属于韩国风物特产的是____。,珍珠,燕窝,高丽参,香料,C,C,1
+西汉的____,包括《素问》和《灵枢》两部分,奠定了传统中医学理论基础,是我国现存最早的一部医书。,《黄帝内经》,《伤寒杂病论》,《脉经》,《千金方》,A,A,1
+-,-,-,-,-,文件 professional_tour_guide_val.csv 的正确率: 41.38%,-,-
diff --git a/ceval/ceval_result/sports_science_val_result.csv b/ceval/ceval_result/sports_science_val_result.csv
new file mode 100644
index 0000000..4d78ce8
--- /dev/null
+++ b/ceval/ceval_result/sports_science_val_result.csv
@@ -0,0 +1,21 @@
+question,A,B,C,D,answer,llm_answer,is_right
+在极限强度运动中,肌肉中的ATP和CP在多少秒内就几乎耗竭____,15,30,10,20,C,C,1
+决定VO2max的外周机制是____,肌纤维组成,有氧代谢能力,肌组织利用氧的能力,氧运输系统的机能,C,A,0
+“极点”产生早晚与____,年龄无关,训练程度无关,气候条件无关,教练员无关,D,C,0
+优秀运动员全程性多年训练过程中,训练负荷始终保持在高水平区间起伏的是____,基础训练阶段,专项提高阶段,最佳竞技阶段,竞技保持阶段,C,C,1
+下列不是准备活动的作用的是____,调节赛前状态,缩短进入工作状态,减轻“极点”程度,加速运动疲劳的恢复,D,C,0
+腿部肌肉中快肌纤维百分组成占优势的人,较适宜从事的运动项目是____,800m跑,1 500m跑,100m跑,1 500m游泳,C,C,1
+运动时,机体工作能力逐步提高是因为____,物理惰性和植物性功能惰性,运动器官功能惰性和物理惰性,植物性功能惰性和运动器官功能惰性,物理惰性和生理惰性,D,D,1
+课外运动竞赛的主要特点有竞争性、集体性与教育性、多层次与群众性以及____,知识性与协作性,生理性与心理性,趣味性与娱乐性,公正性与表现性,C,C,1
+西周的“国学”和“乡学”的教学内容为____,宗教和军事,习射及传习多种武艺,礼、乐、射、御、书、数,“五项竞技”,C,C,1
+能够导致氧解离曲线右移的情况是____,血液中PCO2增高,血液中PCO2降低,血液中pH值增高,血液中PN2张力增高,A,C,0
+下面不属于克服自身体重的练习是____,引体向上,倒立推起,使用拉力器,纵跳,C,C,1
+柔道项目中的“得意技”指的是____,基本技术,特长技术,高难度技术,全面技术,B,C,0
+关于糖的分解代谢,下列说法错误的是____,在不需要氧的情况下,糖进行无氧酵解,反应在细胞浆中进行,糖进行无氧酵解时,能量利用率很低,在氧气供应充足时,肌肉中的乳酸可以再转变为葡萄糖进一步氧化供能,葡萄糖或糖原生成丙酮酸是有氧和无氧供能的共同途径,C,C,1
+下列能最好评价肺通气功能的指标是____,肺通气量,肺活量,补吸气量,时间肺活量,D,C,0
+从运动员竞技能力的决定因素看,下列项目对运动员心理能力要求最高的是____,游泳,跳水,射箭,摔跤,C,C,1
+乳酸阈可用来评定机体____,无氧能力,有氧能力,血乳酸能力,ATP—CP系统能力,B,C,0
+运动员负荷量度临界值的大小受教育程度、竞技水平及健康等因素的影响,因此在训练中需要____,正确处理负荷与恢复的关系,正确理解训练负荷构成,科学动态探求负荷量度临界值,对运动员进行区别对待,C,B,0
+同为球类项目,篮球与足球对运动员身体形态、素质、技战术的要求却大不相同,主要是因为____,运动员个体的不同,教练员执教能力的不同,各项目训练条件的不同,专项竞技能力的不同,D,A,0
+影响血红蛋白氧饱和度的最主要因素是____,PO2,血液pH值,PCO2,血液的温度,A,C,0
+-,-,-,-,-,文件 sports_science_val.csv 的正确率: 47.37%,-,-
diff --git a/ceval/ceval_result/tax_accountant_val_result.csv b/ceval/ceval_result/tax_accountant_val_result.csv
new file mode 100644
index 0000000..1d55ecf
--- /dev/null
+++ b/ceval/ceval_result/tax_accountant_val_result.csv
@@ -0,0 +1,51 @@
+question,A,B,C,D,answer,llm_answer,is_right
+进行税务咨询服务的核心是____。,弄清咨询问题所涉及的税种,收集咨询问题相关的税收政策文件,分析税收政策适用条款,根据需要作必要的沟通说明,C,C,1
+FY公司与FC机械厂均为国有企业,合资设立A有限责任公司(以下简称“A公司”),出资比例为30%与70%。下列有关A公司董事会组成的说法中,不符合规定的是____。,董事会成员中应当有公司职工代表,董事张某任期内辞职,在新选出董事就任前,张某仍应履行董事职责,A公司董事长可由公司章程规定由小股东FY公司派人担任,FY公司和FC机械厂可通过公司章程约定不按出资比例分红,B,A,0
+下列关于预约定价安排管理的表述中,正确的是____。,预约定价安排执行期满后自动失效,企业申请续签的,应当在预约定价安排执行期满之日前30日内向税务机关提出续签申请,企业申请双边预约定价安排的,应及时向省级税务机关提出谈签意向,预约定价安排适用于自企业提交正式书面申请年度当年起10个连续年度的关联交易,在预约定价安排执行期内,税务机关应当每年监控企业执行预约定价安排的情况,D,A,0
+下列各项行为中,应征收个人所得税的是____。,离婚析产分割房屋产权,军人的转业费、复员费、退役金,按照国家统一规定发放的退休费、离休费,个人购买福利彩票,一次中奖收入12000元,D,D,1
+某酒厂为增值税一般纳税人,2020年10月发放1吨自制白酒作为职工福利,同类白酒不含税售价50000元/吨,成本价35000元/吨。该酒厂上述业务当月应纳消费税____元。,7700,8700,10000,11000,D,C,0
+下列各项中,属于土地增值税征税范围的是____。,房地产出租,房地产评估增值,房地产的代建房行为,合作建房后,建成后转让的,D,D,1
+针对查账的顺序不同,纳税审查的方法可分为____。,顺查法和逆查法,详查法和抽查法,核对法和查询法,比较分析法和控制计算法,A,B,0
+南和公司因长期不能清偿到期债务,向人民法院申请破产。东尚公司是南和公司的债权人,下列与东尚公司有关的事项中,正确的是____。,债权还有2个月到期,东尚公司不能申报破产债权,东尚公司可以口头向管理人申报债权,东尚公司申报债权后,就可以行使债权人的权利,东尚公司申报债权时需要提供债权申请书、债权证据材料等内容,D,A,0
+对于不符合收入准则规定的合同成立的条件,企业将已收取客户的对价确认为收入的条件为____。,开具增值税专用发票,不再负有向客户转让商品的剩余义务,且已向客户收取的对价无需退回,具有商业实质,商品已经发出,B,A,0
+关于税务登记的说法,错误的是____。,一般纳税人资格认定的权限,在县(市、区)税务局或同级别的税务分局,纳税人应当向其机构所在地主管税务机关申请一般纳税人资格认定,年应税销售额达到一般纳税人标准的纳税人,未申请办理一般纳税人手续的,应按销售额依照增值税税率计算应纳税额,可以抵扣进项税额,但不得使用增值税专用发票,纳税人应在领取《税务登记证》副本后和申报纳税之前,申请税种认定登记,C,A,0
+下列各项中,属于非相关成本的是____。,机会成本,重置成本,差额成本,沉没成本,D,D,1
+根据《行政诉讼法》规定,下列关于行政诉讼二审程序的说法中,错误的是____。,二审法院可以不开庭审理,二审法院审理上诉案件,一般应当在收到上诉状之日起6个月内作出终审判决,当事人不服一审判决提起上诉的,应当在判决书送达之日起15日内提起,二审法院审理上诉案件时,应当对原审法院的裁判和被诉行政行为进行全面审查,B,A,0
+甲公司为工业企业,属于增值税一般纳税人。2019年取得主营业务收入为2000万元,本期发生现金折扣10万元,增值税销项税额为260万元;应收账款账户期初余额为600万元,期末余额为900万元,坏账准备的期初余额为10万元,期末余额为30万元;预收账款账户期初余额为100万元,期末余额为20万元;本期收到存货抵债减少应收账款40万元,本期发生不附追索权票据贴现利息5万元。假定不考虑其他因素,甲公司2019年度现金流量表中“销售商品、提供劳务收到的现金”项目的金额为____万元。,1825,1855,1860,1880,A,A,1
+下列说法符合律师事务所及其从业人员个人所得税征收规定的是____。,兼职律师从律师事务所取得工资、薪金性质的所得,事务所在代扣代缴其个人所得税时,应先扣除税法规定的费用扣除标准,计算律师事务所经营所得时,出资律师本人的工资、薪金不得扣除,律师个人承担的按照律师协会规定参加的业务培训费用,不得扣除,受雇于律师事务所的律师从事务所取得的分成收入,应单独作为一个月的工资、薪金,扣除办案费用后缴纳个人所得税,B,A,0
+申请人、第三人可以委托代理人参加税务行政复议,但是应当向行政复议机构提交授权委托书。下列各项中,不属于授权委托书应当载明的内容的是____。,委托事项,委托权限,委托期限,委托结果,D,A,0
+以下项目在计算土地增值税时,不得扣除成本费用是____。,建成后产权属于全体业主的会所,建成后无偿移交派出所用于办公的房屋,建成后有偿出售的停车场,建成后待售出租的商业用房,D,D,1
+下列关于编制银行存款余额调节表的表述中,正确的是____。,银行对账单上的金额,反映了企业可以动用的银行存款实有数额,对于未达账项,需要对企业和银行各自提供的银行存款余额进行调整,银行存款余额调节表用来核对企业和银行的记账有无错误,并作为记账依据,调节后银行存款日记账余额与银行对账单余额一定相等,B,A,0
+下列各项符合房产税规定的是____。,更换房屋附属设施和配套设施的,其更新价值计入房产原值,但不扣减原来相应旧设备和设施的价值,对居民住宅区内业主共有的经营性房产,自营的不征收房产税,对于与地上房屋相连的地下建筑,应将地下部分与地上房屋分别按照地上与地下建筑物的规定计算征收房产税,出租的地下建筑,按照出租地上房屋建筑的有关规定计算征收房产税,D,A,0
+下列各项中,不属于增量预算应遵循的假定是____。,以现有业务活动和各项活动的开支水平,确定预算期各项活动的预算数,预算费用标准必须进行调整,企业现有各项业务的开支水平是合理的,在预算期予以保持,企业现有业务活动是合理的,不需要进行调整,B,D,0
+下列有关或有事项的表述中,正确的是____。,清偿因或有事项而确认的负债所需支出全部或部分预期由第三方补偿时,补偿金额在很可能收到时才能作为资产单独确认,对于或有事项既要确认或有负债,也要确认或有资产,对固定资产计提折旧不属于或有事项,或有事项应确认为预计负债,C,A,0
+关于增值税的销售额,下列说法不正确的是____。,劳务派遣服务,可以选择差额纳税,航空运输企业的销售额不包括代收的机场建设费,旅游服务,一律以取得的全部价款和价外费用为销售额,经纪代理服务,以取得的全部价款和价外费用,扣除向委托方收取并代为支付的政府性基金或者行政事业性收费后的余额为销售额,C,A,0
+根据行政处罚法律制度的规定,下列关于处罚与教育相结合原则的说法中,正确的是____。,处罚只是手段而不是目的,处罚与教育相结合意味着可以以罚代刑,行政机关未责令当事人限期改正违法行为即作出行政处罚的,该行政处罚程序违法,行政处罚行为无效,行政机关未责令当事人限期改正违法行为即作出行政处罚的,该行政处罚程序不违法,但是该处罚行为为可撤销的行政行为,A,A,1
+甲公司2019年3月31日发现2018年度多计管理费用200万元,并进行了2018年企业所得税申报,甲公司适用企业所得税税率25%,并按净利润的10%提取法定盈余公积。假设甲公司2018年度财务报表于2019年3月10日对外报出,且当年度企业所得税申报的应纳税所得税额大于零,则下列甲公司对此项重要前期差错进行更正的会计处理中正确的是____。,调减2019年度当期管理费用200万元,调增2019年当期未分配利润150万元,调减2019年年初未分配利润135万元,调增2019年年初未分配利润135万元,D,C,0
+购置新建房的城镇土地使用税纳税义务发生时间为____。,自房屋交付使用之次月起,自办理房产证之次月起,自签订房屋买卖合同之次月起,自房屋竣工验收之次月起,A,A,1
+甲公司2019年年度财务报告经董事会批准于2020年4月20日报出。甲公司在2020年1月1日至4月20日之间发生的下列事项中,属于资产负债表日后调整事项的是____。,2020年3月10日,法院判决某项诉讼败诉,并需支付赔偿金额80万元,甲公司在2019年年末已经确认预计负债65万元,2020年2月10日发生产品销售退回,该批产品系2020年1月对外销售,2020年2月18日董事会提出资本公积转增资本方案,2020年3月18日公司仓库发生火灾导致存货部分毁损,A,A,1
+下列关于外币交易会计处理的表述中,错误的是____。,外币交易在初始确认时,可以采用按照系统合理的方法确定的、与交易日即期汇率近似的汇率折算,资产负债表日,对于外币货币性项目应当根据汇率变动计算汇兑差额作为财务费用,无需再计提减值准备,外币交易应当在初始确认时,采用交易发生日的即期汇率或近似汇率将外币金额折算为记账本位币金额,资产负债表日,对以历史成本计量的外币非货币性项目,仍采用交易发生日的即期汇率折算,不改变记账本位币金额,B,A,0
+下列消费品,属于消费税征税范围的是____。,合成宝石首饰,洗发水,大客车,轮胎,A,C,0
+下列关于车船税的说法中,正确的是____。,拖拉机属于车船税的征收范围,扣缴义务人代扣代缴车船税的,车辆登记地主管税务机关不再征收,境内单位和个人将船舶出租到境外的,不征收车船税,客货两用车依照乘用车的标准计征车船税,B,A,0
+在税务行政复议中,不可以达成和解和调解的情形是____。,行政奖励,行政审批,确定应税所得率,核定税额,B,C,0
+申请人和被申请人在行政复议机关作出行政复议决定以前可以达成和解,行政复议机关也可以调解,下列选项中不可以和解与调解的是____。,确定应税所得率,行政赔偿,行政奖励,征收滞纳金,D,C,0
+下列关于售后回购交易的会计处理符合企业会计准则规定的是____。,企业因存在与客户的远期安排而负有回购义务或企业享有回购权利的,回购价格低于售价,应当视为租赁交易,企业到期未行使回购权利的,应当在该回购权利到期时终止确认金融负债,但无需确认收入,企业负有应客户要求回购商品义务的,客户具有行使该要求权重大经济动因的,企业应当将售后回购作为融资交易,企业负有应客户要求回购商品义务的,客户不具有行使该要求权重大经济动因的,应当将其作为正常销售交易,A,D,0
+M公司资金周转出现困难,其法定代表人甲向好友乙借款100万元,甲把自己的宝马汽车抵押给乙,抵押合同中约定若甲不能按时还钱,甲的宝马汽车归乙所有。下列说法正确的是____。,该抵押未登记,乙的抵押权未生效,甲、乙的约定无效,甲、乙的约定经登记才有效,甲应把自己的汽车交付给乙,抵押权才生效,B,A,0
+甲、乙、丙成立一家科贸有限公司,约定公司注册资本100万元,甲、乙、丙各按20%、30%、50%的比例出资。甲、乙缴足了出资,丙仅实缴30万元。公司章程对于红利分配没有特别约定。当年年底公司进行分红。对此,下列说法中正确的是____。,丙只能按30%的比例分红,应按实缴注册资本80万元,由甲、乙、丙按各自的实际出资比例分红,由于丙违反出资义务,其他股东可通过决议取消其当年分红资格,丙有权按50%的比例分红,但应当承担未足额出资的违约责任,B,A,0
+应税固体废物环境保护税的计税依据是____。,固体废物的综合利用量,固体废物的排放量,固体废物的产生量,固体废物的贮存量,B,D,0
+某卷烟批发企业在2020年10月发生下列业务:批发销售给卷烟零售企业卷烟10标准箱,取得不含税收入150万元;批发销售给卷烟批发商卷烟5标准箱,取得不含税收入65万元。该企业当月应纳消费税____万元。,16.5,16.75,23.65,24.03,B,C,0
+关于财产拍卖的个人所得税处理,下列说法正确的是____。,作者将自己的文字作品手稿原件拍卖取得的所得,按“稿酬所得”项目计算缴纳个人所得税,计算个人财产拍卖的应纳税所得额时,纳税人实际支付的拍卖费不得扣除,拍卖祖传收藏的财产,可以税前扣除的财产原值为其收藏该拍卖品而发生的费用,经认定的海外回流文物的财产原值无法确定的,按转让收入的3%征收率计税,C,C,1
+某县生猪屠宰主管部门强制该县50户养猪农民养殖1号猪,并对其他品种的生猪将不予屠宰。对于此行为,下列说法正确的是____。,由于该行为属于普遍约束力的决定、命令,属于行政诉讼不受理的案件范围,该行为是行政机关为作出行政行为而实施的过程性行政行为,因此不具可诉性,该行为是具体行政行为,具有可诉性,该行为是国家行为,属于行政诉讼不受理的案件范围,C,A,0
+下列各项中,不属于税务行政诉讼目的的是____。,维护和监督税务机关依法行使行政职权,保证税法的公平、公正,保护纳税人、扣缴义务人等当事人的合法权益,保证人民法院正确、及时审理税务行政案件,B,C,0
+下列支出不能作为长期待摊费用的是____。,固定资产的大修理支出,租入固定资产的改建支出,外购房屋发生的装修费用,已足额提取折旧的固定资产的改建支出,C,C,1
+下列凭证中,需要计算缴纳印花税的是____。,无息、贴息贷款合同,新设立的资金账簿,财产所有人将财产赠给学校所立的书据,施工单位分包给其他施工单位的分包合同,D,D,1
+甲公司某零件年需要量为2000件,每次订货成本为30元,单位储存成本为0.75元/件。按照经济订货量进货,下列计算结果中错误的是____。,经济订货量为400件,年订货次数为5次,总订货成本为300元,与进货批量相关的总成本为300元,C,A,0
+关于土地增值税的清算,下列说法错误的是____。,销售合同所载商品房面积与实际测量面积不一致并在清算前已补或退房款的,在计算土地增值税时应予调整,未全额开具商品房销售发票的,按照销售合同所载金额及其他收益确认收入,未开具商品房销售发票的,按照实际收取金额确认收入,已全额开具商品房销售发票的,按照发票所栽金额确认收入,C,A,0
+2020年5月10日,税务机关在检查某公司的纳税情况过程中,发现该公司2019年的业务存在关联交易,少缴纳企业所得税30万元。该公司于2020年5月31日补缴了该税款,并按规定提供了同期资料及有关资料。已知2019年12月31日中国人民银行公布的一年期人民币贷款年利率为6%。税务机关对该公司补缴税款应加收利息____万元。,1.8,1.95,3.3,3.6,A,C,0
+关于消费税从价定率计税销售额,下列说法正确的是____。,金银首饰包装费不计入计税销售额,消费税计税销售额包括增值税,白酒包装物押金收取时不计入计税销售额,高档化妆品品牌使用费应计入计税销售额,D,C,0
+2020年10月,为响应环保节能号召,陈某从汽车4S店(增值税一般纳税人)购买一辆新能源汽车,支付不含税价款150000元。另支付汽车4S店代办保险费2000元,代办车辆牌照费300元,代收款项4S店未开具发票。陈某应纳车辆购置税____元。,0,10500,15000,15203.54,A,A,1
+某供热企业为增值税一般纳税人,2020年10月取得不含税供热收入860万元,其中向居民个人收取120万元,当月外购原材料取得增值税专用发票注明税额70万元。该企业2020年10月可以抵扣的进项税额为____万元。,15.13,24.9,28.94,60.23,D,C,0
+境外旅客购物离境退税的方式包括现金退税和银行转账退税两种方式。自行选择退税方式时,退税额应未超过____元。,500,1000,5000,10000,D,C,0
+下列转让定价方法中,适用于所有关联交易的是____。,可比非受控价格法,成本加成法,交易净利润法,利润分割法,A,C,0
+某企业为增值税一般纳税人,2021年6月从某花木栽培公司手中购入花卉1100盆,取得的专用发票上注明价款为110580元。该企业将1/4用于赠送给某节日庆典,其余全部卖给客户取得产品不含税销售额705000元。则该企业当月应纳增值税税额为____元。,59442,91236.2,107824.6,74647.8,D,D,1
+-,-,-,-,-,文件 tax_accountant_val.csv 的正确率: 28.57%,-,-
diff --git a/ceval/ceval_result/teacher_qualification_val_result.csv b/ceval/ceval_result/teacher_qualification_val_result.csv
new file mode 100644
index 0000000..553274f
--- /dev/null
+++ b/ceval/ceval_result/teacher_qualification_val_result.csv
@@ -0,0 +1,46 @@
+question,A,B,C,D,answer,llm_answer,is_right
+编写中小学教科书的直接依据是____。,《中华人民共和国教育法》,课程计划,课程标准,课程表,C,C,1
+下列关于课程的三种文本表现形式说法正确的是____,课程计划是由当地教育主管部门制订的,课程标准是依据课程计划制定的,课程标准的核心是实施建议,教材编写的基本方式有直线式、螺旋式、交叉式,B,C,0
+悦悦是一名右耳失聪的残疾儿童,活动课上有时会听不清楚周老师所讲的内容,因此经常提问题。对此,周老师应当采取的措施是____。,给予悦悦更多的帮助和指导,指导家长带悦悦回家自学,建议家长将悦悦转到特殊幼儿园,照顾大多数幼儿,不理会悦悦,A,A,1
+内流河也称“内陆河”,是指没有流入海洋的河流,大多分布在大陆内部干燥地区,上游降水或冰雪融水为其主要补给水源,最终消失于沙漠或注入内陆湖泊。下列中国内流河中,最长的是____。,塔里木河,柴达木河,尼雅河,疏勒河,A,A,1
+学校规定学生不能烫染头发,但是小文为了彰显个性,在假期把头发染成了棕色。面对小文的情况,教师应该怎样处理?____,年轻人追求个性是合情合理的,应该宽容对待,违反学校的校规,应该严格处分,强制要求小文将头发颜色染回来才可以进校门,探明小文违反校规的原因,并对其进行劝导和教育,D,A,0
+张老师根据自己班级的情况,为解决班级内部班干部的人际关系问题,建立和谐融洽的班级氛围,自主开发了“和谐人际”的班级课程,这体现了教师____。,是教育教学的研究者,是课程的建设者和开发者,是学生学习的促进者,是社区型的开放教师,B,C,0
+刘老师工作很负责,学生在学校出现一点问题他就会与家长联系,在与家长沟通时他经常以前辈的姿态对待家长,对家长的教育方式指指点点。刘老师的做法____。,正确,老师就应该与家长经常沟通,正确,老师的经验比家长丰富,应该多指导家长,不正确,教师没有权利指导家长,不正确,教师应该与家长建立平等的沟通关系,尊重家长的人格,D,A,0
+在古代印度,有一户人家经营一家棉布店销售自己手工制作的衣服。你认为这户人家属于哪个等级?____,婆罗门,刹帝利,吠舍,首陀罗,C,A,0
+“小型分散,便于开展多种多样的活动,满足学生不同的兴趣、爱好,发展学生的才能,使学生得到更多的学习和锻炼的机会。”这种课外活动的形式是____。,科技活动,学科活动,个人活动,小组活动,D,C,0
+小红每天晚上临睡前都要多次反复检查自己的书包,确保带齐了第二天需要用的教材和文具。她明知道没有这个必要,但就是控制不住。她可能出现了____。,抑郁症,焦虑症,强迫症,恐惧症,C,C,1
+国家管理和评价课程的基础是____。,课程计划,课程标准,教学目标,教育目的,B,C,0
+儿童坚持性发生明显质变的年龄约在____,3~4岁,4~5岁,5~6岁,6岁以后,B,C,0
+《红楼梦》中人物众多、关系繁杂。为了帮助读者阅读,许多红学爱好者都在网络上发布了自己整理制作的主要人物关系图。这属于____。,纲要策略,精细加工策略,资源管理策略,监控策略,A,A,1
+学期结束时,班主任王老师会对学生思想品德的发展变化情况进行评价。这项工作属于____。,工作总结,工作计划,操行评定,建立学生档案,C,C,1
+人们常说:“教学有法而教无定法。”这反映了教师的劳动具有____。,连续性,示范性,长期性,创造性,D,C,0
+县级以上地方各级人民代表大会是县级以上地方国家权力机关,其职权不包括____。,改变或撤销本级人大常务委员会不适当的决定,选举并有权罢免本级人民法院院长,批准本行政区域内的预算执行情况的报告,决定并宣布下一级行政区城进入紧急状态,D,C,0
+在心理健康课上,同一批学生在第二次进行同样内容的人格测验时获得的分数与上次测验差别较大。这说明该测验存在的问题是____。,信度问题,效度问题,难度问题,区分度问题,A,D,0
+李老师在教学生区分形近字“渴”“竭”“碣”“谒”时,将四个字相同的右半部分用白色粉笔写出,相异的左半部分用彩色粉笔写出。李老师运用了知觉的____。,整体性,选择性,理解性,恒常性,B,C,0
+"兰兰学会走路后,就要很喜欢尝试自己穿衣、吃饭、捡东西,喜欢探索周围世界。按照埃里克森人格发展阶段理论,兰兰所处的发展阶段是____",信任对怀疑,自立对羞怯,主动感对内疚感,勤奋感对自卑感,B,A,0
+杨老师在教授生字词的过程中发现部分学生有缺笔少画的现象,于是他把“小学生缺笔少画现象的原因及对策研究”作为研究课题,拟订相应的研究计划,在工作中收集、整理相关资料并实施教学措施,最后根据反馈信息调整教学方案。这种研究方法属于____。,教育行动研究法,教育实验法,教育叙事研究法,个案研究法,A,A,1
+小青的数学成绩不好,她认为这是因为自己脑子笨,不是学数学的料。她的这种归因属于____。,内部、稳定,不可控的归因,外部、稳定、可控的归因,内部、不稳定,可控的归因,外部,不稳定,不可控的归因,A,A,1
+中小学教科书不同于其他任何书籍的基本特点是内容的____。,准确性,示范性,新颖性,基础性,D,C,0
+王老师在课堂上给学生演示了与知识点有关的几个实验。这属于____。,实物直观,模象直观,言语直观,思维直观,A,C,0
+"在Excel中,单元格A1, A2, A3中的内容依次为数值1,2,3,单元格A4中的内容为字符前添加了英文单撇号“,”的文本字符“3”,在单元格A5的编辑栏输入公式“=COUNT( A1:A4) +12”并点击回车键,A5单元格的内容为____。",15,21,12,18,D,A,0
+唐朝时形成了“父教其子,子教其弟”“五尺童子耻不言文墨焉”的社会风尚,它的形成主要得益于____。,社会经济的繁荣,科举制度的推行,学校体系的完备,三省六部制的确立,B,C,0
+教导处的刘老师抓到两名学生藏在厕所里偷偷抽烟,于是把他们叫到办公室,慢悠悠地点燃了一根香烟,准备耐心细致地给他们做思想工作。对此,以下说法错误的是____。,刘老师既禁止学生抽烟,又能耐心劝导,严慈相济,真正做到了关爱学生,刘老师要求学生不要抽烟,却在学生面前抽烟,违背了为人师表的要求,刘老师的抽烟行为与他教导学生不能抽烟的言词相悖,很容易损害自己的威信,刘老师的行为表明教师队伍中存在一些教师需要对其加强师风师德建设的,A,A,1
+小班幼儿看木偶剧表演时,看到“老虎”会感到害怕。这说明幼儿的____,想象脱离现实,想象与现实混淆,想象容易受情绪影响,想象内容零散,B,C,0
+有的成语与历史人物密切相关。下列选项中,与“狡兔三窟”相关的历史人物是____。,管仲与齐桓公,毛遂与平原君,冯谖与孟尝君,曹刿与鲁庄公,C,A,0
+王浩同学活动过多、注意力不集中、冲动行为多。这种心理障碍可能是____。,多动综合征,学习困难综合征,儿童厌学症,儿童强迫行为,A,C,0
+在对班级学生进行教育时,班主任李老师引导学生对自己每日的学习、行为进行反省。李老师主要运用的德育方法是____。,自我修养法,榜样示范法,实践锻炼法,情感陶冶法,A,B,0
+在讲解方程时,王老师先讲一元一次方程,再讲二元一次方程,然后讲一元二次方程,逐步加深难度。这种教学方式所遵循的原则是____。,理论联系实际原则,启发性原则,循序渐进原则,巩固性原则,C,A,0
+近代原子核物理学之父是____。,普朗克,卢瑟福,玻尔,霍金,B,C,0
+很多人因为有了受教育的机会而得到了和父辈完全不同的人生发展机遇。这说明教育在人的发展中起到____。,辅助作用,决定作用,次要作用,主导作用,D,C,0
+下面是中国古代四大名著中的人物与情节,其中搭配不当的一项是____。,鲁智深——倒拔垂杨柳,孙悟空——大闹天宫,周瑜——三顾茅庐,刘姥姥——进大观园,C,A,0
+找规律填数字是一项很有趣的活动,特别锻炼观察和思考能力。下列选项中,填入数列“1、7、8、57、____、26050”空缺处的数字,符合该组数字排列规律的是____。,456,457,458,459,B,C,0
+教育自身的许多规律,是人类长期教育实践认识的结果,它们不会因政治经济制度和其他文化的发展而过时,更不会随时代的发展而被否定。这说明教育具有____。,历史性,永恒性,阶级性,相对独立性,D,C,0
+高中毕业会考是一种达标考试,属于____。,定量评价,相对性评价,形成性评价,绝对性评价,D,C,0
+下列选项中,与“图书”和“音乐书”的逻辑关系相同的一组是____。,“钢笔”和“铅笔”,“蛋糕”和“香油”,“水果”和“西瓜”,“白菜”和“黄瓜”,C,C,1
+语文教师裴老师每天下课后都会对自己一天的工作进行总结反思,并记录下来。这属于布鲁巴奇反思方法中的____。,反思日记,详细描述,交流讨论,行动研究,A,C,0
+以下关于幼儿有意注意发展的表述,不正确的是____,幼儿有意注意发展受大脑发育水平局限,幼儿有意注意的发展水平较低,无法依靠活动和操作来维持,幼儿在幼儿园需要遵守各种行为规则,完成各项任务,这都需要幼儿形成或发展有意注意,教师在组织活动时,要求幼儿保持注意的对象应该是幼儿认知范围以内或幼儿易于理解的事物,B,C,0
+某幼儿园根据幼儿的发展情况将班级分为快班、中班和慢班。对于快班的幼儿安排大量优秀师资和先进设备,而对于慢班的幼儿则给予较少的优良教育资源。该幼儿园的做法违背了素质教育内涵中的____。,以提高国民素质为基本宗旨,面向全体幼儿,促进幼儿全面发展,促进幼儿个性发展,B,A,0
+作为古埃及文明的象征之一,____既寄托了古埃及人对死后重生的向往,又证明了新一代法老王权统治的神圣不可侵犯,充分显示了古埃及人的高度智慧和精湛的建筑艺术。,金字塔,帕特农神庙,圆形竞技场,麦加清真寺,A,C,0
+在太阳系的八大行星中,质量最大和最小的行星分别是____。,木星;水星,火星;地球,金星;水星,土星;天王星,A,C,0
+据调查,教师对学生拳打脚踢的情况现在已经较少存在,取而代之的是“心罚”。比如,对于成绩不好的学生罚做题目、罚抄单词一百遍。教师这样的行为____。,是正确的,教育中适当的惩罚是必不可少的,是正确的,教师没有侵犯学生的身体健康,是不正确的,教师没能做到依法执教,是不正确的,教师没能做到团结合作,C,A,0
+-,-,-,-,-,文件 teacher_qualification_val.csv 的正确率: 22.73%,-,-
diff --git a/ceval/ceval_result/test.log b/ceval/ceval_result/test.log
new file mode 100644
index 0000000..fe75874
--- /dev/null
+++ b/ceval/ceval_result/test.log
@@ -0,0 +1,54 @@
+总题数: 1346
+总正确数: 330
+总正确率: 24.52%probability_and_statistics_val.csv: 文件 probability_and_statistics_val.csv 的正确率: 11.11%
+law_val.csv: 文件 law_val.csv 的正确率: 29.17%
+middle_school_biology_val.csv: 文件 middle_school_biology_val.csv 的正确率: 33.33%
+high_school_chemistry_val.csv: 文件 high_school_chemistry_val.csv 的正确率: 26.32%
+high_school_physics_val.csv: 文件 high_school_physics_val.csv 的正确率: 31.58%
+legal_professional_val.csv: 文件 legal_professional_val.csv 的正确率: 21.74%
+high_school_chinese_val.csv: 文件 high_school_chinese_val.csv 的正确率: 21.05%
+high_school_history_val.csv: 文件 high_school_history_val.csv 的正确率: 30.00%
+tax_accountant_val.csv: 文件 tax_accountant_val.csv 的正确率: 28.57%
+modern_chinese_history_val.csv: 文件 modern_chinese_history_val.csv 的正确率: 43.48%
+middle_school_physics_val.csv: 文件 middle_school_physics_val.csv 的正确率: 47.37%
+middle_school_history_val.csv: 文件 middle_school_history_val.csv 的正确率: 9.09%
+basic_medicine_val.csv: 文件 basic_medicine_val.csv 的正确率: 21.05%
+operating_system_val.csv: 文件 operating_system_val.csv 的正确率: 5.26%
+logic_val.csv: 文件 logic_val.csv 的正确率: 18.18%
+electrical_engineer_val.csv: 文件 electrical_engineer_val.csv 的正确率: 21.62%
+civil_servant_val.csv: 文件 civil_servant_val.csv 的正确率: 27.66%
+chinese_language_and_literature_val.csv: 文件 chinese_language_and_literature_val.csv 的正确率: 26.09%
+college_programming_val.csv: 文件 college_programming_val.csv 的正确率: 27.03%
+accountant_val.csv: 文件 accountant_val.csv 的正确率: 20.41%
+plant_protection_val.csv: 文件 plant_protection_val.csv 的正确率: 18.18%
+middle_school_chemistry_val.csv: 文件 middle_school_chemistry_val.csv 的正确率: 25.00%
+metrology_engineer_val.csv: 文件 metrology_engineer_val.csv 的正确率: 16.67%
+veterinary_medicine_val.csv: 文件 veterinary_medicine_val.csv 的正确率: 34.78%
+marxism_val.csv: 文件 marxism_val.csv 的正确率: 31.58%
+advanced_mathematics_val.csv: 文件 advanced_mathematics_val.csv 的正确率: 36.84%
+high_school_mathematics_val.csv: 文件 high_school_mathematics_val.csv 的正确率: 16.67%
+business_administration_val.csv: 文件 business_administration_val.csv 的正确率: 18.18%
+mao_zedong_thought_val.csv: 文件 mao_zedong_thought_val.csv 的正确率: 20.83%
+ideological_and_moral_cultivation_val.csv: 文件 ideological_and_moral_cultivation_val.csv 的正确率: 15.79%
+college_economics_val.csv: 文件 college_economics_val.csv 的正确率: 27.27%
+professional_tour_guide_val.csv: 文件 professional_tour_guide_val.csv 的正确率: 41.38%
+environmental_impact_assessment_engineer_val.csv: 文件 environmental_impact_assessment_engineer_val.csv 的正确率: 22.58%
+computer_architecture_val.csv: 文件 computer_architecture_val.csv 的正确率: 14.29%
+urban_and_rural_planner_val.csv: 文件 urban_and_rural_planner_val.csv 的正确率: 13.04%
+college_physics_val.csv: 文件 college_physics_val.csv 的正确率: 15.79%
+middle_school_mathematics_val.csv: 文件 middle_school_mathematics_val.csv 的正确率: 21.05%
+high_school_politics_val.csv: 文件 high_school_politics_val.csv 的正确率: 31.58%
+physician_val.csv: 文件 physician_val.csv 的正确率: 28.57%
+college_chemistry_val.csv: 文件 college_chemistry_val.csv 的正确率: 29.17%
+high_school_biology_val.csv: 文件 high_school_biology_val.csv 的正确率: 26.32%
+high_school_geography_val.csv: 文件 high_school_geography_val.csv 的正确率: 21.05%
+middle_school_politics_val.csv: 文件 middle_school_politics_val.csv 的正确率: 19.05%
+clinical_medicine_val.csv: 文件 clinical_medicine_val.csv 的正确率: 27.27%
+computer_network_val.csv: 文件 computer_network_val.csv 的正确率: 26.32%
+sports_science_val.csv: 文件 sports_science_val.csv 的正确率: 47.37%
+art_studies_val.csv: 文件 art_studies_val.csv 的正确率: 21.21%
+teacher_qualification_val.csv: 文件 teacher_qualification_val.csv 的正确率: 22.73%
+discrete_mathematics_val.csv: 文件 discrete_mathematics_val.csv 的正确率: 31.25%
+education_science_val.csv: 文件 education_science_val.csv 的正确率: 10.34%
+fire_engineer_val.csv: 文件 fire_engineer_val.csv 的正确率: 25.81%
+middle_school_geography_val.csv: 文件 middle_school_geography_val.csv 的正确率: 25.00%
diff --git a/ceval/ceval_result/urban_and_rural_planner_val_result.csv b/ceval/ceval_result/urban_and_rural_planner_val_result.csv
new file mode 100644
index 0000000..a160f2f
--- /dev/null
+++ b/ceval/ceval_result/urban_and_rural_planner_val_result.csv
@@ -0,0 +1,48 @@
+question,A,B,C,D,answer,llm_answer,is_right
+对于固体污染物的控制规划内容,不够突出的是____。,电子污染物,生活垃圾,医疗废物,工业固体废物,A,C,0
+竖向设计设计标高中,当建筑物无进车道时,一般室内地坪比室外地坪面高出____,0.30~0.90m,0.45~0.60m,0.25~0.30m,0.25~0.35m,B,A,0
+城市总体规划的强制性内容,在防灾方面没涉及的灾害是____。,洪灾,震灾,涝灾,火灾,C,C,1
+地下电力缆保护区的宽度为地下电力电缆线路地面标桩两侧各____所形成两平行线内区域。,0.5m,0.75m,1.0m,1.5m,B,C,0
+下列城市全部由国务院公布为历史文化名城的是____,延安、淮安、泰安、瑞安、雅安,金华、银川、同仁、铁岭、无锡,韩城、聊城、邹城、晋城、塔城,歙县、寿县、祁县、浚县、代县,D,A,0
+《村庄整治技术导则》中提出,对于“空心村”,在住房制度上提出的政策是____。,拆除已坍塌的房屋,一户一宅,迁村并点,宅基地向村中心集中,B,A,0
+下列不属于村庄规划的具体内容的是____。,社会经济规划,道路交通规划,绿化景观规划,市政规划,A,C,0
+居住区的规划布局形式类型中不包括____。,居住区—小区—组团,居住区—组团,街坊式,联合式组团,D,C,0
+造成城乡生产力结构根本区别的是____。,文化观念的差异,生产力结构的差异,职能的差异,物质形态的差异,B,A,0
+以下不属于液化石油气气化站与混气站的布置原则的是____。,液化石油气气化站与混气站的站址应靠近负荷区,站址应是地势平坦、开阔、不易积存液化石油气的地段,站址应与站外建筑物保持规范所规定的防火间距要求,作为机动气源的混气站不能与气源厂、城市煤气储配站合设,D,A,0
+世界上现存最高的木塔是中国____,河南登封嵩岳寺塔,山西应县佛宫寺释迦塔,山东济南神通寺四门塔,陕西扶风法门寺塔,B,C,0
+采用一元线性回归的方法分析预测规划期城市人口规模的主要依据是____。,可以准确预测规划远期的人口数量,在某一时间段内城市人口的数量与时间,是一组相关的线性函数关系,城市人口遵循直线增长的规律,已掌握了充足的城市人口历年变动的资料,B,D,0
+下列属于工业固体废物的产生量的预测方法的是____,单位产品法,百元产值法,人均指标法,增长率法,A,C,0
+以下有关城市规划编制单位资质管理的说法正确的是____。,三个以上城市规划编制单位合作编制城市规划时,有关规划编制单位应当共同向任务所在地相应的主管部门备案,城市规划编制单位合并或者分立,应当在批准之日起15日内重新申请办理《资质证书》,申请乙级、丙级资质的,由所在地市、县人民政府城市规划主管部门审批,核发《资质证书》,并报国务院城市规划主管部门备案,乙、丙级城市规划编制单位跨省、自治区、直辖市设立的分支机构中,非独立法人的机构,不得以分支机构名义承揽业务,C,A,0
+下列不属于堆肥方法运用于固体垃圾处理优点的是____,占地较小,投资较低,产品可用作肥料,无害化程度很高,A,C,0
+公民、法人和社会团体为了促进城市规划有效、合理地实施,为了维护自己的合法权利,可以依法对城市规划行政机关做出的具体行政行为提出____。,行政诉讼,民事诉讼,行政仲裁,申诉,A,C,0
+下列哪一项不是城市总体规划中城市发展目标的内容?____,城市性质,用地规模,人口规模,基础设施和公共设施配套水平,A,B,0
+工商业活动集聚的场所是____,也是从事工商业活动的人群聚居的场所。,乡村,郊区,田园,城市,D,C,0
+大修的城市道路竣工后____年内不得挖掘;因特殊情况需要挖掘的,须经县级以上城市人民政府批准。,3,4,5,6,A,B,0
+洛杉矶的交通发展模式为____。,以小汽车为主、公交为辅的交通模式,以小汽车为主体的交通模式,以轨道公交为主、小汽车和地面公交为辅的交通模式,公交为主、小汽车为主导(公交与小汽车并重),B,D,0
+根据《中华人民共和国环境影响评价法》的规定,下列关于规划环境影响评价的内容和审批表述中不正确的是____,未编写有关环境影响的篇章或者说明的规划草案,审批机关不予审批,专项规划的编制机关在报批规划草案时,必须将环境影响登记表一并附送审批机关审查,专项规划的环境影响报告书应当包括环境影响评价的结论,规划有关环境影响的篇章或者说明,应当对规划实施后可能造成的环境影响作出分析、预测和评估,B,D,0
+根据《城市道路绿化规划与设计规范》的规定,城市道路绿化规划与设计的基本原则不包括____。,城市绿化树木与市政公用设施的相互位置应统筹安排,并应保证树木有必要的立地条件与生长空间,城市道路绿化应以地被植物为主,地被植物、乔木、灌木相结合,不得裸露土壤,修建城市道路时,宜保留有价值的原有树木,对古树名木应予以保护,城市道路绿化应符合车行视线和行车净空要求,B,A,0
+根据《历史文化名城名镇名村保护条例》,保护规划应当自历史文化名城、名镇、名村批准公布之日起____年内编制完成。,半,1,2,3,B,A,0
+下列有关法律效力选项中不正确的是____,在一定主体制定的法律规范中,按照特定的、更为严格的程序制定的法律规范,其效力等级高于按照普通程序制定的法律规范,当同一制定机关按照相同程序就同一领域问题制定了两个以上法律规范时,后来法律规范的效力高于先前制定的法律规范,同一主体在某领域既有一般性立法又有特殊立法时,特殊立法通常优于一般性立法,国家机关授权下级国家机关制定的所有的法律、法规,其在效力上等同于授权机关自己制定的法律、法规,D,A,0
+城市公共交通系统的核心设施是____。,公交换乘枢纽,城市各级公共中心,市级公交干线,城市对外客运交通枢纽,A,C,0
+根据《城市抗震防灾规划管理规定》,下列关于城市抗震防灾规划编制要求的表述不正确的是____。,城市抗震防灾规划中的抗震设防标准、建设用地评价与要求、抗震防灾措施应当列为城市总体规划的强制性内容,作为编制城市详细规划的依据,城市抗震防灾规划的规划范围应当与城市总体规划相一致,但其应在城市总体规划实施之后进行,城市抗震防灾规划应当按照城市规模、重要性和抗震防灾的要求,分为甲、乙、丙三种模式,位于地震基本烈度七度及七度以上地区的大城市应按照甲类模式编制,B,A,0
+港口岸线分配原则是____。,主要考虑与城市道路衔接,“深水深用、浅水浅用、避免干扰、各得其所”,客运港位于货运港的上风方向,综合考虑船舶航行、货物装卸、库场储存及后方集疏,B,C,0
+根据《省级国土空间规划编制指南(试行)》,以城镇建设、农业生产和工业生产等为主的国土空间开发活动是指____,国土空间开发,国土空间利用,国土空间规划,国土空间保护,A,B,0
+下列不符合《人民防空法》规定的是____,城市是人民防空的重点,国家对城市实行分类防护,城市防护类别、防护标准由中央军事委员会规定,城市人民政府应当制定防空袭方案及实施计划,必要时可以组织演习,C,C,1
+在城市规划分析中,下列用来反映数据离散程度的是____,平均数,众数,标准差,频数分布,C,C,1
+城市交通调查的目的是____。,进行城市交通规划、城市道路系统规划和城市道路设计的基础工作,收集城市公共交通客运总量、货运总量,对外交通客、货运总量等运输现状与发展资料,根据调查的资料,分析城市车辆以及客、货运量的增长特点和规律,摸清城市道路上的交通状况,城市交通的产生、分布、运行规律以及现状存在的主要问题,D,A,0
+下列以满足交通运输的要求为主要功能并承担城市主要的交通量及与对外交通联系的道路是____。,生活性道路,交通性道路,主干路,快速路,B,A,0
+建设单位应当按照规划条件进行建设;确需变更的,必须向____提出申请。,城市、县人民政府土地主管部门,城市、县人民政府国土资源部门,城市、县人民政府建设行政主管部门,城市、县人民政府城乡规划主管部门,D,A,0
+在城市详细规划阶段预测用电负荷,一般采用下列哪种方法?____,人均综合用电量指标法,单位建设用地负荷指标法,单位建筑面积负荷指标法,电力弹性系数法,C,C,1
+在规划实施过程中由城乡规划主管部门核发的证书不包括____。,建设用地规划许可证,建设工程规划许可证,乡村建设规划许可证,建设工程施工许可证,D,C,0
+下列关于建筑与等高线之间关系的表述,错误的是____,建筑与等高线平行,建筑与等高线垂直,建筑与等高线重合,建筑与等高线斜交,C,C,1
+依据《城市用地分类与规划建设用地标准》,其规划人均公共管理与公共服务用地面积指标不应少于____。,5.0m^2/人,5.5m^2/人,6.0m^2/人,6.5m^2/人,B,C,0
+城市供热一级管网宜采用____。,闭式,开式,开式双管制,闭式单管制,A,C,0
+下列关于村庄规划的表述,哪项是错误的?____,应以行政村为单位,应向村民公示,方案由县级城乡规划行政主管部门组织专家和相关部门进行技术审查,成果由村委会报县级人民政府审批,D,C,0
+根据《城市规划编制单位资质管理规定》,下列关于城市规划编制单位资质监督管理的表述中,不正确的是____。,城市规划编制单位提交的城市规划编制成果,应当在文件扉页注明单位资质等级和证书编号,禁止无城市规划编制的机构对城市规划编制单位实行资质年检制度,发证部门或其委托的机构对城市规划编制单位实行资质年检制度,甲、乙级城市规划编制单位跨省、自治区、直辖市承担规划编制任务时,未向其人民政府城市规划行政主管部门备案的,由该人民政府城市规划行政主管部门给予警告,责令其补办备案手续,并处1万元以上5万元以下的罚款,D,A,0
+以下对城市所具有的基本特征的概括,表述不正确的是____。,城市的发展动态是变化和多样的,城市的概念是相对存在的,以要素聚集为基本特征,不具有系统性,D,A,0
+下列符合核电厂选址要求的是____,便于拦河筑坝的河流狭窄处或水库水流下游处,电厂铁路专用线选线要尽量减少对国家干线通过能力的影响,靠近负荷中心,以减少输电费用,工程地质条件良好,土地耐力高,非地质断裂带,C,C,1
+____是建立城市艺术骨架和组织城市空间的重要手段之一,它可以把城市空间组织成一个有秩序、有韵律的整体。,城市绿化,城市水面,城市制高点,城市轴线,D,C,0
+为了定量分析采取某项措施对于减少城市污染的效果,所开发的系统属于____,决策支持系统,事务管理系统,管理信息系统,专家系统,B,C,0
+计算道路网的密度,分析管线穿越地块的问题,可以采用矢量叠合的____的叠合。,点和面,线和面,面和面,点和线,B,C,0
+标志着欧洲进入封建社会的中世纪的是____。,奥匈帝国的灭亡,古希腊的灭亡,波斯帝国的灭亡,罗马帝国的灭亡,D,C,0
+-,-,-,-,-,文件 urban_and_rural_planner_val.csv 的正确率: 13.04%,-,-
diff --git a/ceval/ceval_result/veterinary_medicine_val_result.csv b/ceval/ceval_result/veterinary_medicine_val_result.csv
new file mode 100644
index 0000000..0d24bd1
--- /dev/null
+++ b/ceval/ceval_result/veterinary_medicine_val_result.csv
@@ -0,0 +1,25 @@
+question,A,B,C,D,answer,llm_answer,is_right
+____既在糖酵解又在葡萄糖异生作用中起作用。,丙酮酸激酶,3-磷酸甘油醛脱氢酶,1,6-二磷酸果糖激酶,己糖激酶,B,C,0
+将RNA转移到硝基纤维素膜上的技术叫____。,Southern印迹,Northern印迹,Western印迹,Eastern印迹,B,C,0
+____不是蛋白质的性质之一。,处于等电状态时溶解度最小,加入少量中性盐溶解度增加,变性蛋白质的溶解度增加,有紫外吸收特性,C,C,1
+在酶的分离纯化中最理想的实验结果是____。,纯化倍数高,回收率高,蛋白回收率高,回收率小,但纯化倍数高,比活力最大,A,A,1
+动物体内组成的化学元素中,下列所占比例最多的是____,碳,氯,钠,氢,A,C,0
+酶的催化特点不具有____,高效性,多功能性,可调节性,酶蛋白易变性,B,C,0
+生物素是____的辅酶。,丙酮酸脱氢酶,丙酮酸激酶,丙酮酸脱氢酶系,丙酮酸羧化酶,D,C,0
+蛋白质不能吸收可见光,但能吸收一定波长范围内的紫外光。大多数蛋白质在280nm波长附近有一个吸收峰,这主要与蛋白质中____的紫外吸收有关。因此,可以利用紫外吸收法,根据蛋白质溶液在280nm波长的吸收值测定蛋白质浓度,碱性氨基酸,酸性氨基酸,含硫氨基酸,芳香族氨基酸,B,C,0
+"具5 "" -CpGpGpTpAp-3 "" 顺序的单链DNA能与下列____RNA杂交","5 "" -GpCpCpApTp3 ""","5 "" -GpCpCpUp-3 ""","5 "" -UpApCpCpGp-3 ""","5 "" -TpApCpGp-3 """,C,C,1
+酶的非竞争性抑制剂对酶促反应的影响是____。,$ν_{max}$ 不变,$K_m$ 增大,$ν_{max}$不变,$K_m$减小,$ν_{max}$增大,$K_m$不变,$ν_{max}$减小,$K_m$不变,D,A,0
+水溶性维生素常是辅酶或辅基的组成部分,如____。,辅酶A含尼克酰胺,FAD含有吡哆醛,$FH_4$ 含有叶酸,脱羧辅酶含生物素,C,C,1
+核酸变性后,可发生的效应是____。,减色效应,增色效应,失去对紫外线的吸收能力,最大吸收峰波长发生转移,B,C,0
+下列____不是终止密码。,UAA,15AC,UAG,UGA,B,C,0
+关于酶偶联受体的叙述,错误的是____。,酶偶联受体属于细胞表面受体,酶偶联受体的配体结合区在细胞膜内侧,酶活性区在细胞膜外侧,酶偶联受体介导的是非经典跨质膜与胞内信号途径,可以单独完成信号传递,胞内信号传递不产生经典意义上的第二信使,多数酶偶联受体具有磷酸化酶的活性,B,A,0
+主要分布在肝脏的是____。,碱性磷酸酶,酸性磷酸酶,单胺氧化酶,谷丙转氨酶,D,C,0
+对DNA片段作物理图谱分析,需要用____。,核酸外切酶,DNAseI,限制性内切酶,DNA聚合酶I,C,A,0
+____不是胆色素。,血红素,胆绿素,胆红素,胆素原族,A,C,0
+转录是指____,DNA的自我复制过程,RNA的自我复制过程,以DNA为模板合成RNA的过程,以RNA为模板合成DNA的过程,C,C,1
+血液非蛋白氮中含量最多的物质是____。,尿素,肌酸,蛋白质,尿酸,A,C,0
+氨基酸分子既含有酸性的羧基(一COOH),又含有碱性的氨基(一$NH_2$ )。前者能提供质子变成一COO一;后者能接受质子变成一$NH_3^+$ 。有的氨基酸还有可解离的侧链基团。因此,氨基酸是两性电解质。其解离状态与溶液的pH值有直接关系,当pH值等于pI时,蛋白质____,带正电荷,带负电荷,所带电荷不确定,所带正、负电荷相等,D,D,1
+蛋白质分子是结构极其复杂的生物大分子。有的蛋白质分子只包含一条多肽链;有的则包含数条多肽链。通常将蛋白质的结构划分为几个层次,有一种结构层次出现在一条多肽链的内部,是多肽链局部的所有原子及原子团形成的有规律的构象,该构象一般成球状结构,执行一定的功能,该结构是____,结构域,超二级结构,二级结构,三级结构,A,D,0
+蛋白质生物合成中多肽的氨基酸排列顺序取决于____。,相应tRNA的专一性,相应氨酰tR-NA合成酶的专一性,相应mRNA中核苷酸排列顺序,相应tRNA上的反密码子,C,C,1
+稀有核苷酸碱基主要见于____。,DNA,mRNA,tRNA,rRNA,C,C,1
+-,-,-,-,-,文件 veterinary_medicine_val.csv 的正确率: 34.78%,-,-
diff --git a/ceval/eval.md b/ceval/eval.md
new file mode 100644
index 0000000..985aec9
--- /dev/null
+++ b/ceval/eval.md
@@ -0,0 +1,500 @@
+```angular2html
+模型A的回答:
+
+问题: 人类大脑的主要功能是什么?
+回答:人类大脑的主要功能包括:
+
+1. 记忆:人类大脑拥有超过1000亿个神经元和数百万个神经元之间的连接。
+2. 认知:人类大脑能够处理和理解信息,并将其转换为有用的信息。
+3. 运动:人类大脑能够处理和控制身体运动,包括身体运动、呼吸和冥想。
+4. 情感:人类大脑能够处理和分析情感,包括视觉、听觉、触觉和触觉。
+5. 记忆:人类大脑能够处理和存储信息,包括记忆、记忆、记忆
+
+问题: 万有引力是谁提出的?
+回答:引力是一种物理力,它是由质量或能量所组成的。它是由质量或能量所组成的,并且是所有物质和能量的力。引力是宇宙中许多天体和星体的力量,它使得它们在宇宙中相互吸引,并导致它们在宇宙中移动。
+
+问题: 世界上人口最多的国家是哪一个?
+回答:根据世界银行数据,世界上人口最多的不可数国是印度。根据2020年世界银行统计数据,印度人口约为3900万,人口数量为2100万。
+
+问题: DNA的全称是什么?
+回答:好的,我需要知道你想要我称的什么。请提供更多信息,以便我能够更准确地回答你的问题。
+
+问题: 数学中π的值大约是多少?
+回答:数学中,π的值大约为3.1415926535897932384542。
+
+问题: 世界上最高的山峰是什么?
+回答:世界上最高的山峰是珠穆朗玛峰,位于喜马拉雅山脉,海拔8,848.86米(29,031.69英尺)。
+
+问题: 太阳系中最大的行星是哪一颗?
+回答:太阳系中最大的行星是木星。它是一颗气态行星,其质量是其他行星质量之和倍数的100倍。木星拥有至少79个卫星,其中最大的四颗卫星是水星、金星、地球和火星。
+
+问题: 二氧化碳的化学分子式是什么?
+回答:化学式为 2H2O。
+
+问题: 地球上最大的动物是什么?
+回答:地球上最大的动物是蓝鲸。蓝鲸是哺乳动物,是地球上最大的动物。它们可以长到30米长,重达1700公斤,是地球上最大的生物。
+
+问题: 地球自转一圈大约需要多少时间?
+回答:地球自转是地球绕自己的中心自转,大约需要24小时。这通常会持续24小时,因为地球自转时,它的自转速度会减慢。
+
+地球自转的时间是24小时,因为地球自转时,它的自转速度会减慢。
+
+
+
+模型B的回答:
+
+问题: 人类大脑的主要功能是什么?
+回答:人类大脑的主要功能是处理和存储信息,同时进行复杂的思维和决策过程。大脑通过神经元之间的连接和信息传递,从而控制和控制人类的思维、行为和情感。大脑具有记忆、学习、记忆和适应新环境的能力,能够适应各种复杂的情况。此外,大脑还具有调节和调节神经元之间的电荷平衡,从而保持神经系统的稳定。
+
+问题: 万有引力是谁提出的?
+回答:万有引力是牛顿在1687年发明的,它是一个将物体和物质之间的相互作用力转换为引力大小,从而产生万有引力的作用。牛顿的万有引力理论在17世纪被广泛研究,并成为现代天文学的基础。
+
+问题: 世界上人口最多的国家是哪一个?
+回答:世界上人口最多的国家是中国的北京。根据2020年人口普查,北京的人口约为2,700,000人。
+
+问题: DNA的全称是什么?
+回答:DNA的全称是核糖核酸,是构成生物体内所有生物遗传信息的基础,包括蛋白质、核酸和氨基酸等。它是由核苷酸组成的,包括腺核苷酸、鸟����、胞����、���等,并且它也是由核苷酸组成的。
+
+问题: 数学中π的值大约是多少?
+回答:π是一个无限不循环小数,它是一个数学常数,它的值是3.14159265358979323846。这个数值可以用来计算各种数学问题的答案,例如物理学、工程学、经济学等等。π的值可以无限大,因此它可以被用来解决许多实际问题,例如物理学中的运动学问题、金融学中的投资哲学问题等等。
+
+问题: 世界上最高的山峰是什么?
+回答:世界上最高的山峰是珠穆朗玛峰,位于尼泊尔和中国的边境上,海拔8848米。珠穆朗玛峰也是全球最著名的登山目的地之一,吸引了来自世界各地的游客和登山者。
+
+问题: 太阳系中最大的行星是哪一颗?
+回答:太阳系中最大的行星是木星,它的质量是太阳系中所有行星中最大的。木星拥有太阳系中最大的磁场和较少的卫星数量,因此它也是太阳系中最大的行星之一。
+
+问题: 二氧化碳的化学分子式是什么?
+回答:二氧化碳的化学式是CO2,表示分子式为CO2。
+
+问题: 地球上最大的动物是什么?
+回答:地球上最大的动物是蓝鲸,它的体重可以达到190吨,并且它也是世界上最大的哺乳动物之一。
+
+问题: 地球自转一圈大约需要多少时间?
+回答:地球自转所需的时间是23小时56分钟,也就是一天。但是需要注意的是,这只是一个理论上的时间,实际上,地球自转的速率取决于地球的旋转和自转速度。地球自转的速率与地球的旋转和自转速度有关,地球自转速速与地球自转速率的正交加速度为9.8米/秒,而地球自转速速与地球自转速率的负交加速度为1.83米/秒,即一天。
+
+
+模型C:
+问题: 人类大脑的主要功能是什么?
+回答: 人类大脑是一个复杂的器官,由数十亿个神经元和突触组成。它的主要功能包括:
+1. 控制运动:大脑控制身体的各种运动,包括行走、奔跑、跳跃、跳跃高度、跳跃距离、跳跃高度的变化等。
+2. 感知和认知:大脑接收来自外部世界的信息,包括视觉、听觉、触觉、味觉、嗅觉、味觉细胞等。
+3. 记忆和思维:大脑通过神经元之间的连接,将信息转化为可操作的形式,并将其存储在长期记忆中。
+4. 情感和行为:大脑通过神经元之间的突触连接,将情感和行为转化为神经信号,并将其传递给大脑的其他区域。
+5. 语言和思维:大脑通过神经元之间的突触连接,将语言和思维转化为神经信号,并将其传递给大脑的其他区域。
+6. 情感和行为:大脑通过神经元之间的突触连接,将情感和行为转化为神经信号,并将其传递给其他区域。
+7. 记忆和学习:大脑通过神经元之间的突触连接,将信息转化为长期记忆,并将其传递给其他区域。
+总之,人类大脑的主要功能是控制身体的各种运动、感知和认知,以及情感和行为。它通过神经元之间的连接,将信息转化为神经信号,并将其传递给其他区域。
+
+问题: 万有引力是谁提出的?
+回答: 万有引力是由英国物理学家艾萨克·牛顿于1687年提出的。他在1687年发表了一篇著名的论文,题为《自然哲学的数学原理》,其中提出了万有引力定律,指出任何两个物体之间都存在着一种引力,这种引力的大小与它们的质量成正比,与它们之间的距离的平方成反比。
+这个定律被称为万有引力定律,它描述了质量和距离之间的关系。它表明,两个物体之间的引力与它们的质量成正比,与它们之间的距离的平方成反比。
+这个定律对于解释宇宙中的许多现象和现象之间的相互作用非常重要,例如行星绕太阳运动、恒星之间的引力透镜效应等等。它也被广泛应用于天文学、物理学和工程学等领域,为我们理解宇宙的本质和演化提供了重要的理论基础。
+
+问题: 世界上人口最多的国家是哪一个?
+回答: 根据联合国的数据,截至2021年7月,世界上人口最多的国家是日本,其人口约为 14.5 亿。
+根据联合国的数据,截至2021年7月,世界上人口最多的国家是印度,其人口约为 14.5 亿。
+值得注意的是,这个数字可能会因为各种因素而有所变化,例如人口增长率、移民政策、人口迁移等。因此,在回答这个问题时,需要考虑这些因素,并根据实际情况进行分析和比较。
+
+问题: DNA的全称是什么?
+回答: DNA 的全称是 "脱氧核糖核酸",也称为 "核苷酸"。它是一种双螺旋结构的分子,由四种碱基(腺嘌呤、鸟嘌呤、胸腺嘧啶和鳞状细胞素)组成,以特定的顺序排列在一起,形成了一个双螺旋结构。 DNA 分子是生命的基础,它携带着生物体遗传信息,控制着细胞的生长、分裂和分裂过程。它是由核苷酸单元组成的,每个核苷酸单元由一个磷酸基团、一个五碳糖分子和一个氮碱基组成。 DNA 分子的结构和功能决定了生物体的遗传特征,包括眼睛颜色、皮肤颜色、身高、体重、眼睛的颜色、眼睛的大小、眼睛的颜色、眼睛的颜色分布、眼睛的颜色分布范围、眼睛的颜色分布方式、眼睛的颜色分布范围、眼睛的颜色分布方式、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布
+
+问题: 数学中π的值大约是多少?
+回答: π 是一个数学常数,通常用希腊字母π表示,它是一个无限不循环小数,约等于 3.14159265358979323。它是一个无限不循环的小数,在数学、物理、工程和计算机科学等领域都有广泛的应用。
+在数学中,π 的近似值可以通过计算圆的周长和直径的比值来得到。周长是圆的周长与其直径的比值,通常用希腊字母π表示。直径是圆的一个侧面,通常用希腊字母π表示。
+π 的近似值约为 3.14159265358979323 或 3.14159 x 10^3 ≈ 3.1415926
+在物理中,π 的近似值可以通过计算圆的半径和体积的比值来得到。圆的半径是圆的一个半径的两倍,体积是圆的体积之和。
+π 的近似值在许多领域都有应用,例如物理学、工程学、计算机科学和工程学等。它是数学中最基本的常数之一,也是许多数学问题的基础。
+
+问题: 世界上最高的山峰是什么?
+回答: 世界上最高的山峰是珠穆朗玛峰,也称为萨加玛塔,位于喜马拉雅山脉的尼泊尔和中国边境之间,海拔8,848.86米(29,031.69英尺)。它是世界上最著名的登山目标之一,吸引着来自世界各地的登山者前来挑战。
+珠穆朗玛峰的名字来自藏语,意思是“女神的额峰”,它是由两个不同的山峰组成的,其中一个是位于尼泊尔的珠穆朗玛峰,另一个则位于中国。它的高度是通过测量海拔高度来确定的,因为它是通过测量地球的平均高度来确定的。
+尽管珠穆朗玛峰是世界上最高的山峰,但它并不是地球上任何其他地方都能到达的。地球上的其他地方,如南美洲的安第斯山脉,也是世界上最高的山脉,但它并不是地球上最高的山峰。
+总之,珠穆朗玛峰是世界上最高的山峰,但它并不是地球上任何地方都能到达的。它是地球上最高的山脉,但它并不是地球上任何地方都能到达的。
+
+问题: 太阳系中最大的行星是哪一颗?
+回答: 太阳系中最大的行星是木星,它的质量是其他所有行星质量之和的2.5倍,直径也是地球的11倍。它的质量是地球的318倍,是太阳系中所有行星中质量最大的行星。它的质量是其他行星质量总和的2.5倍,比太阳系中其他行星的质量总和还要大。
+木星的质量是地球的318倍,它的大气层主要由氢和氦组成,还有少量的甲烷、氨和水。它的表面有许多撞击坑和山脉,其中最著名的是伽利略卫星伽利略卫星,它是伽利略在1610年发现的。
+木星的大气层中还含有大量的氢气和氦气,这些气体在木星的大气层中形成了一个巨大的漩涡,它的大红斑是一个巨大的风暴,比地球还要大。它的磁场也非常强大,是太阳系中最强的磁场之一。
+总之,木星是太阳系中最大、最重、最亮的行星之一,它的质量和直径都是地球的11倍,是太阳系中所有行星中质量最大的。
+
+问题: 二氧化碳的化学分子式是什么?
+回答: 二氧化碳是一种无机化合物,其化学式为 Co ()2。它是一种无色、无味、无臭的气体,在常温常压下为稳定的气体。它是一种高度反应性的化合物,在常温常压下为稳定的分子结构。它是一种非常活泼的气体,在常压下可以与许多元素形成化合物,如碳、氧、氮、硫等。它也是一种非常稳定的溶剂,可以在许多化学反应中发挥重要作用。二氧化碳在工业上广泛应用于制造气、气油、气电池、气泵等。
+
+问题: 地球上最大的动物是什么?
+回答: 地球上最大的动物是蓝鲸(Balaenoptera musculus),也被称为座头鲸或鲸鲨。它们是一种鲸类动物,体重可达200吨,身长可达30米,肩高可达30米。蓝鲸是一种鲸类群,以浮游生物为食,它们的舌头可以长达30米,重达200吨。它们的心脏也非常大,可以容纳超过100,000个成年人。
+蓝鲸是一种鲸类,主要分布在全球各大洋,包括大西洋、印度洋和太平洋。它们是肉食性动物,以小型甲壳类动物、鱼类、鱿鱼和浮游生物为食。它们的牙齿也非常大,可以咬碎超过100,000个骨头。
+尽管蓝鲸是地球上最大的动物,但它们的数量却在不断减少。目前,全球捕鲸活动已经导致了蓝鲸的数量急剧下降,这对它们的生存和繁殖都带来了极大的威胁。因此,保护蓝鲸及其栖息地,以及减少捕鲸活动是非常重要的。
+
+问题: 地球自转一圈大约需要多少时间?
+回答: 地球自转是指地球围绕自身轴线旋转一周的运动,它的周期约为 24 小时。地球的自转速度是由地球内部的物质运动和地球自身的引力决定的,它的大小取决于地球的形状和密度。地球的自转速度是一个近似于球体的速度,但它并不是完全均匀的,因为地球的形状是一个不规则的椭球体,而不是一个完美的球体。
+地球自转的速度是由地球内部的物质运动和引力决定的。地球内部的物质运动是由地球内部的岩石和尘埃组成的,这些物质在地球内部不断地移动,形成了地球的自转轴。地球的引力是由地球内部的质量和密度决定的,它的质量越大,引力就越强。地球的密度约为每立方厘米10^-15克/立方厘米,这意味着地球的质量约为 1.08 克/立方厘米。
+地球自转的速度对地球上的生命和环境都有很大的影响。它决定了地球上的昼夜交替和季节变化,也影响了地球上的气候和地形。此外,地球自转还影响了地球上的潮汐和风的形成。
+总之,地球自转是一个复杂而重要的自然现象,它对地球的生态系统和人类社会都有着深远的影响。
+
+
+请你打个分吧!点评一下,满分100分
+
+模型A的回答:
+
+问题: 人类大脑的主要功能是什么?
+回答:人类大脑的主要功能包括:
+
+1. 记忆:人类大脑拥有超过1000亿个神经元和数百万个神经元之间的连接。
+2. 认知:人类大脑能够处理和理解信息,并将其转换为有用的信息。
+3. 运动:人类大脑能够处理和控制身体运动,包括身体运动、呼吸和冥想。
+4. 情感:人类大脑能够处理和分析情感,包括视觉、听觉、触觉和触觉。
+5. 记忆:人类大脑能够处理和存储信息,包括记忆、记忆、记忆
+
+问题: 万有引力是谁提出的?
+回答:引力是一种物理力,它是由质量或能量所组成的。它是由质量或能量所组成的,并且是所有物质和能量的力。引力是宇宙中许多天体和星体的力量,它使得它们在宇宙中相互吸引,并导致它们在宇宙中移动。
+
+问题: 世界上人口最多的国家是哪一个?
+回答:根据世界银行数据,世界上人口最多的不可数国是印度。根据2020年世界银行统计数据,印度人口约为3900万,人口数量为2100万。
+
+问题: DNA的全称是什么?
+回答:好的,我需要知道你想要我称的什么。请提供更多信息,以便我能够更准确地回答你的问题。
+
+问题: 数学中π的值大约是多少?
+回答:数学中,π的值大约为3.1415926535897932384542。
+
+问题: 世界上最高的山峰是什么?
+回答:世界上最高的山峰是珠穆朗玛峰,位于喜马拉雅山脉,海拔8,848.86米(29,031.69英尺)。
+
+问题: 太阳系中最大的行星是哪一颗?
+回答:太阳系中最大的行星是木星。它是一颗气态行星,其质量是其他行星质量之和倍数的100倍。木星拥有至少79个卫星,其中最大的四颗卫星是水星、金星、地球和火星。
+
+问题: 二氧化碳的化学分子式是什么?
+回答:化学式为 2H2O。
+
+问题: 地球上最大的动物是什么?
+回答:地球上最大的动物是蓝鲸。蓝鲸是哺乳动物,是地球上最大的动物。它们可以长到30米长,重达1700公斤,是地球上最大的生物。
+
+问题: 地球自转一圈大约需要多少时间?
+回答:地球自转是地球绕自己的中心自转,大约需要24小时。这通常会持续24小时,因为地球自转时,它的自转速度会减慢。
+
+地球自转的时间是24小时,因为地球自转时,它的自转速度会减慢。
+
+
+
+模型B的回答:
+
+问题: 人类大脑的主要功能是什么?
+回答:人类大脑的主要功能是处理和存储信息,同时进行复杂的思维和决策过程。大脑通过神经元之间的连接和信息传递,从而控制和控制人类的思维、行为和情感。大脑具有记忆、学习、记忆和适应新环境的能力,能够适应各种复杂的情况。此外,大脑还具有调节和调节神经元之间的电荷平衡,从而保持神经系统的稳定。
+
+问题: 万有引力是谁提出的?
+回答:万有引力是牛顿在1687年发明的,它是一个将物体和物质之间的相互作用力转换为引力大小,从而产生万有引力的作用。牛顿的万有引力理论在17世纪被广泛研究,并成为现代天文学的基础。
+
+问题: 世界上人口最多的国家是哪一个?
+回答:世界上人口最多的国家是中国的北京。根据2020年人口普查,北京的人口约为2,700,000人。
+
+问题: DNA的全称是什么?
+回答:DNA的全称是核糖核酸,是构成生物体内所有生物遗传信息的基础,包括蛋白质、核酸和氨基酸等。它是由核苷酸组成的,包括腺核苷酸、鸟����、胞����、���等,并且它也是由核苷酸组成的。
+
+问题: 数学中π的值大约是多少?
+回答:π是一个无限不循环小数,它是一个数学常数,它的值是3.14159265358979323846。这个数值可以用来计算各种数学问题的答案,例如物理学、工程学、经济学等等。π的值可以无限大,因此它可以被用来解决许多实际问题,例如物理学中的运动学问题、金融学中的投资哲学问题等等。
+
+问题: 世界上最高的山峰是什么?
+回答:世界上最高的山峰是珠穆朗玛峰,位于尼泊尔和中国的边境上,海拔8848米。珠穆朗玛峰也是全球最著名的登山目的地之一,吸引了来自世界各地的游客和登山者。
+
+问题: 太阳系中最大的行星是哪一颗?
+回答:太阳系中最大的行星是木星,它的质量是太阳系中所有行星中最大的。木星拥有太阳系中最大的磁场和较少的卫星数量,因此它也是太阳系中最大的行星之一。
+
+问题: 二氧化碳的化学分子式是什么?
+回答:二氧化碳的化学式是CO2,表示分子式为CO2。
+
+问题: 地球上最大的动物是什么?
+回答:地球上最大的动物是蓝鲸,它的体重可以达到190吨,并且它也是世界上最大的哺乳动物之一。
+
+问题: 地球自转一圈大约需要多少时间?
+回答:地球自转所需的时间是23小时56分钟,也就是一天。但是需要注意的是,这只是一个理论上的时间,实际上,地球自转的速率取决于地球的旋转和自转速度。地球自转的速率与地球的旋转和自转速度有关,地球自转速速与地球自转速率的正交加速度为9.8米/秒,而地球自转速速与地球自转速率的负交加速度为1.83米/秒,即一天。
+
+
+模型C:
+问题: 人类大脑的主要功能是什么?
+回答: 人类大脑是一个复杂的器官,由数十亿个神经元和突触组成。它的主要功能包括:
+1. 控制运动:大脑控制身体的各种运动,包括行走、奔跑、跳跃、跳跃高度、跳跃距离、跳跃高度的变化等。
+2. 感知和认知:大脑接收来自外部世界的信息,包括视觉、听觉、触觉、味觉、嗅觉、味觉细胞等。
+3. 记忆和思维:大脑通过神经元之间的连接,将信息转化为可操作的形式,并将其存储在长期记忆中。
+4. 情感和行为:大脑通过神经元之间的突触连接,将情感和行为转化为神经信号,并将其传递给大脑的其他区域。
+5. 语言和思维:大脑通过神经元之间的突触连接,将语言和思维转化为神经信号,并将其传递给大脑的其他区域。
+6. 情感和行为:大脑通过神经元之间的突触连接,将情感和行为转化为神经信号,并将其传递给其他区域。
+7. 记忆和学习:大脑通过神经元之间的突触连接,将信息转化为长期记忆,并将其传递给其他区域。
+总之,人类大脑的主要功能是控制身体的各种运动、感知和认知,以及情感和行为。它通过神经元之间的连接,将信息转化为神经信号,并将其传递给其他区域。
+
+问题: 万有引力是谁提出的?
+回答: 万有引力是由英国物理学家艾萨克·牛顿于1687年提出的。他在1687年发表了一篇著名的论文,题为《自然哲学的数学原理》,其中提出了万有引力定律,指出任何两个物体之间都存在着一种引力,这种引力的大小与它们的质量成正比,与它们之间的距离的平方成反比。
+这个定律被称为万有引力定律,它描述了质量和距离之间的关系。它表明,两个物体之间的引力与它们的质量成正比,与它们之间的距离的平方成反比。
+这个定律对于解释宇宙中的许多现象和现象之间的相互作用非常重要,例如行星绕太阳运动、恒星之间的引力透镜效应等等。它也被广泛应用于天文学、物理学和工程学等领域,为我们理解宇宙的本质和演化提供了重要的理论基础。
+
+问题: 世界上人口最多的国家是哪一个?
+回答: 根据联合国的数据,截至2021年7月,世界上人口最多的国家是日本,其人口约为 14.5 亿。
+根据联合国的数据,截至2021年7月,世界上人口最多的国家是印度,其人口约为 14.5 亿。
+值得注意的是,这个数字可能会因为各种因素而有所变化,例如人口增长率、移民政策、人口迁移等。因此,在回答这个问题时,需要考虑这些因素,并根据实际情况进行分析和比较。
+
+问题: DNA的全称是什么?
+回答: DNA 的全称是 "脱氧核糖核酸",也称为 "核苷酸"。它是一种双螺旋结构的分子,由四种碱基(腺嘌呤、鸟嘌呤、胸腺嘧啶和鳞状细胞素)组成,以特定的顺序排列在一起,形成了一个双螺旋结构。 DNA 分子是生命的基础,它携带着生物体遗传信息,控制着细胞的生长、分裂和分裂过程。它是由核苷酸单元组成的,每个核苷酸单元由一个磷酸基团、一个五碳糖分子和一个氮碱基组成。 DNA 分子的结构和功能决定了生物体的遗传特征,包括眼睛颜色、皮肤颜色、身高、体重、眼睛的颜色、眼睛的大小、眼睛的颜色、眼睛的颜色分布、眼睛的颜色分布范围、眼睛的颜色分布方式、眼睛的颜色分布范围、眼睛的颜色分布方式、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布范围、眼睛的颜色分布
+
+问题: 数学中π的值大约是多少?
+回答: π 是一个数学常数,通常用希腊字母π表示,它是一个无限不循环小数,约等于 3.14159265358979323。它是一个无限不循环的小数,在数学、物理、工程和计算机科学等领域都有广泛的应用。
+在数学中,π 的近似值可以通过计算圆的周长和直径的比值来得到。周长是圆的周长与其直径的比值,通常用希腊字母π表示。直径是圆的一个侧面,通常用希腊字母π表示。
+π 的近似值约为 3.14159265358979323 或 3.14159 x 10^3 ≈ 3.1415926
+在物理中,π 的近似值可以通过计算圆的半径和体积的比值来得到。圆的半径是圆的一个半径的两倍,体积是圆的体积之和。
+π 的近似值在许多领域都有应用,例如物理学、工程学、计算机科学和工程学等。它是数学中最基本的常数之一,也是许多数学问题的基础。
+
+问题: 世界上最高的山峰是什么?
+回答: 世界上最高的山峰是珠穆朗玛峰,也称为萨加玛塔,位于喜马拉雅山脉的尼泊尔和中国边境之间,海拔8,848.86米(29,031.69英尺)。它是世界上最著名的登山目标之一,吸引着来自世界各地的登山者前来挑战。
+珠穆朗玛峰的名字来自藏语,意思是“女神的额峰”,它是由两个不同的山峰组成的,其中一个是位于尼泊尔的珠穆朗玛峰,另一个则位于中国。它的高度是通过测量海拔高度来确定的,因为它是通过测量地球的平均高度来确定的。
+尽管珠穆朗玛峰是世界上最高的山峰,但它并不是地球上任何其他地方都能到达的。地球上的其他地方,如南美洲的安第斯山脉,也是世界上最高的山脉,但它并不是地球上最高的山峰。
+总之,珠穆朗玛峰是世界上最高的山峰,但它并不是地球上任何地方都能到达的。它是地球上最高的山脉,但它并不是地球上任何地方都能到达的。
+
+问题: 太阳系中最大的行星是哪一颗?
+回答: 太阳系中最大的行星是木星,它的质量是其他所有行星质量之和的2.5倍,直径也是地球的11倍。它的质量是地球的318倍,是太阳系中所有行星中质量最大的行星。它的质量是其他行星质量总和的2.5倍,比太阳系中其他行星的质量总和还要大。
+木星的质量是地球的318倍,它的大气层主要由氢和氦组成,还有少量的甲烷、氨和水。它的表面有许多撞击坑和山脉,其中最著名的是伽利略卫星伽利略卫星,它是伽利略在1610年发现的。
+木星的大气层中还含有大量的氢气和氦气,这些气体在木星的大气层中形成了一个巨大的漩涡,它的大红斑是一个巨大的风暴,比地球还要大。它的磁场也非常强大,是太阳系中最强的磁场之一。
+总之,木星是太阳系中最大、最重、最亮的行星之一,它的质量和直径都是地球的11倍,是太阳系中所有行星中质量最大的。
+
+问题: 二氧化碳的化学分子式是什么?
+回答: 二氧化碳是一种无机化合物,其化学式为 Co ()2。它是一种无色、无味、无臭的气体,在常温常压下为稳定的气体。它是一种高度反应性的化合物,在常温常压下为稳定的分子结构。它是一种非常活泼的气体,在常压下可以与许多元素形成化合物,如碳、氧、氮、硫等。它也是一种非常稳定的溶剂,可以在许多化学反应中发挥重要作用。二氧化碳在工业上广泛应用于制造气、气油、气电池、气泵等。
+
+问题: 地球上最大的动物是什么?
+回答: 地球上最大的动物是蓝鲸(Balaenoptera musculus),也被称为座头鲸或鲸鲨。它们是一种鲸类动物,体重可达200吨,身长可达30米,肩高可达30米。蓝鲸是一种鲸类群,以浮游生物为食,它们的舌头可以长达30米,重达200吨。它们的心脏也非常大,可以容纳超过100,000个成年人。
+蓝鲸是一种鲸类,主要分布在全球各大洋,包括大西洋、印度洋和太平洋。它们是肉食性动物,以小型甲壳类动物、鱼类、鱿鱼和浮游生物为食。它们的牙齿也非常大,可以咬碎超过100,000个骨头。
+尽管蓝鲸是地球上最大的动物,但它们的数量却在不断减少。目前,全球捕鲸活动已经导致了蓝鲸的数量急剧下降,这对它们的生存和繁殖都带来了极大的威胁。因此,保护蓝鲸及其栖息地,以及减少捕鲸活动是非常重要的。
+
+问题: 地球自转一圈大约需要多少时间?
+回答: 地球自转是指地球围绕自身轴线旋转一周的运动,它的周期约为 24 小时。地球的自转速度是由地球内部的物质运动和地球自身的引力决定的,它的大小取决于地球的形状和密度。地球的自转速度是一个近似于球体的速度,但它并不是完全均匀的,因为地球的形状是一个不规则的椭球体,而不是一个完美的球体。
+地球自转的速度是由地球内部的物质运动和引力决定的。地球内部的物质运动是由地球内部的岩石和尘埃组成的,这些物质在地球内部不断地移动,形成了地球的自转轴。地球的引力是由地球内部的质量和密度决定的,它的质量越大,引力就越强。地球的密度约为每立方厘米10^-15克/立方厘米,这意味着地球的质量约为 1.08 克/立方厘米。
+地球自转的速度对地球上的生命和环境都有很大的影响。它决定了地球上的昼夜交替和季节变化,也影响了地球上的气候和地形。此外,地球自转还影响了地球上的潮汐和风的形成。
+总之,地球自转是一个复杂而重要的自然现象,它对地球的生态系统和人类社会都有着深远的影响。
+
+
+请你打个分吧!点评一下,满分100分
+
+
+
+<回答格式如下>
+根据提供的回答内容,我会对模型A和模型B分别进行评分和点评。每个问题的回答分为准确性、清晰性和完整性三个部分,每部分各占1/3分值。总分为100分。
+
+模型A
+人类大脑的主要功能是什么?
+
+准确性:30%
+清晰性:20%
+完整性:20%
+评分:70%
+万有引力是谁提出的?
+......
+
+
+按照这样的格式依次打分和汇总
+
+```
+
+```angular2html
+根据提供的回答内容,以下是对模型A、B和C的评分和点评:
+
+### 模型A
+
+1. **人类大脑的主要功能是什么?**
+ - **准确性**:30%(重复了记忆功能,情感部分描述不准确)
+ - **清晰性**:20%(表达重复,缺乏条理)
+ - **完整性**:20%(缺少关键功能如语言处理、决策等)
+ - **评分**:70%
+
+2. **万有引力是谁提出的?**
+ - **准确性**:0%(描述混乱,未明确指出是牛顿)
+ - **清晰性**:10%(语言不清晰,逻辑混乱)
+ - **完整性**:0%(未提供正确信息)
+ - **评分**:10%
+
+3. **世界上人口最多的国家是哪一个?**
+ - **准确性**:0%(错误,中国不是人口最多的国家)
+ - **清晰性**:20%(数据错误,但表述清晰)
+ - **完整性**:10%(缺少正确数据和解释)
+ - **评分**:30%
+
+4. **DNA的全称是什么?**
+ - **准确性**:0%(完全答非所问)
+ - **清晰性**:0%(没有提供有用信息)
+ - **完整性**:0%(没有回答出DNA的全称)
+ - **评分**:0%
+
+5. **数学中π的值大约是多少?**
+ - **准确性**:100%(正确)
+ - **清晰性**:100%(清晰明确)
+ - **完整性**:100%(提供了足够信息)
+ - **评分**:100%
+
+6. **世界上最高的山峰是什么?**
+ - **准确性**:100%(正确)
+ - **清晰性**:100%(清晰明确)
+ - **完整性**:100%(提供了高度和地理位置)
+ - **评分**:100%
+
+7. **太阳系中最大的行星是哪一颗?**
+ - **准确性**:50%(正确指出是木星,但描述其卫星错误)
+ - **清晰性**:50%(部分描述清晰)
+ - **完整性**:50%(缺少木星的详细特征)
+ - **评分**:50%
+
+8. **二氧化碳的化学分子式是什么?**
+ - **准确性**:0%(化学式错误)
+ - **清晰性**:0%(提供了错误信息)
+ - **完整性**:0%(没有正确回答)
+ - **评分**:0%
+
+9. **地球上最大的动物是什么?**
+ - **准确性**:100%(正确)
+ - **清晰性**:100%(清晰明确)
+ - **完整性**:100%(提供了动物的名称和特征)
+ - **评分**:100%
+
+10. **地球自转一圈大约需要多少时间?**
+ - **准确性**:50%(部分描述正确,但有重复和混淆)
+ - **清晰性**:40%(部分表述清晰)
+ - **完整性**:40%(缺少对自转速度变化的解释)
+ - **评分**:40%
+
+**模型A总分**:470/900
+
+### 模型B
+
+1. **人类大脑的主要功能是什么?**
+ - **准确性**:50%(描述重复,部分功能描述不准确)
+ - **清晰性**:40%(表达重复,逻辑不清晰)
+ - **完整性**:40%(缺少关键功能)
+ - **评分**:130%
+
+2. **万有引力是谁提出的?**
+ - **准确性**:50%(提到牛顿,但描述不准确)
+ - **清晰性**:40%(语言不够清晰)
+ - **完整性**:40%(未详细解释万有引力)
+ - **评分**:130%
+
+3. **世界上人口最多的国家是哪一个?**
+ - **准确性**:0%(错误,北京不是国家)
+ - **清晰性**:20%(数据错误,但表述清晰)
+ - **完整性**:10%(缺少正确数据和解释)
+ - **评分**:30%
+
+4. **DNA的全称是什么?**
+ - **准确性**:0%(描述混乱,未给出全称)
+ - **清晰性**:0%(信息不清晰)
+ - **完整性**:0%(没有回答出DNA的全称)
+ - **评分**:0%
+
+5. **数学中π的值大约是多少?**
+ - **准确性**:100%(正确)
+ - **清晰性**:100%(清晰明确)
+ - **完整性**:100%(提供了足够信息)
+ - **评分**:100%
+
+6. **世界上最高的山峰是什么?**
+ - **准确性**:100%(正确)
+ - **清晰性**:100%(清晰明确)
+ - **完整性**:100%(提供了高度和地理位置)
+ - **评分**:100%
+
+7. **太阳系中最大的行星是哪一颗?**
+ - **准确性**:100%(正确指出是木星)
+ - **清晰性**:100%(清晰明确)
+ - **完整性**:100%(提供了木星的特征)
+ - **评分**:100%
+
+8. **二氧化碳的化学分子式是什么?**
+ - **准确性**:100%(正确)
+ - **清晰性**:100%(清晰明确)
+ - **完整性**:100%(提供了正确的化学式)
+ - **评分**:100%
+
+9. **地球上最大的动物是什么?**
+ - **准确性**:100%(正确)
+ - **清晰性**:100%(清晰明确)
+ - **完整性**:100%(提供了动物的名称和特征)
+ - **评分**:100%
+
+10. **地球自转一圈大约需要多少时间?**
+ - **准确性**:50%(描述部分正确,但有逻辑错误)
+ - **清晰性**:40%(部分表述清晰)
+ - **完整性**:40%(缺少对自转速度变化的解释)
+ - **评分**:40%
+
+**模型B总分**:800/900
+
+### 模型C
+
+1. **人类大脑的主要功能是什么?**
+ - **准确性**:40%(描述重复,部分功能描述不准确)
+ - **清晰性**:30%(表达重复,逻辑不清晰)
+ - **完整性**:30%(缺少关键功能)
+ - **评分**:100%
+
+2. **万有引力是谁提出的?**
+ - **准确性**:100%(正确指出是牛顿)
+ - **清晰性**:100%(清晰明确)
+ - **完整性**:100%(提供了详细的背景信息)
+ - **评分**:100%
+
+3. **世界上人口最多的国家是哪一个?**
+ - **准确性**:0%(错误,数据和国家都不正确)
+ - **清晰性**:10%(数据错误,但表述清晰)
+ - **完整性**:0%(缺少正确数据和解释)
+ - **评分**:10%
+
+4. **DNA的全称是什么?**
+ - **准确性**:0%(描述混乱,未给出全称)
+ - **清晰性**:0%(信息不清晰,有乱码)
+ - **完整性**:0%(没有回答出DNA的全称)
+ - **评分**:0%
+
+5. **数学中π的值大约是多少?**
+ - **准确性**:100%(正确)
+ - **清晰性**:100%(清晰明确)
+ - **完整性**:100%(提供了足够信息)
+ - **评分**:100%
+
+6. **世界上最高的山峰是什么?**
+ - **准确性**:100%(正确)
+ - **清晰性**:90%(清晰,但有部分重复和混淆)
+ - **完整性**:90%(提供了高度和地理位置,但有多余描述)
+ - **评分**:90%
+
+7. **太阳系中最大的行星是哪一颗?**
+ - **准确性**:100%(正确指出是木星)
+ - **清晰性**:100%(清晰明确)
+ - **完整性**:100%(提供了木星的特征)
+ - **评分**:100%
+
+8. **二氧化碳的化学分子式是什么?**
+ - **准确性**:0%(化学式错误,有乱码)
+ - **清晰性**:0%(提供了错误信息)
+ - **完整性**:0%(没有正确回答)
+ - **评分**:0%
+
+9. **地球上最大的动物是什么?**
+ - **准确性**:100%(正确)
+ - **清晰性**:90%(清晰,但有部分重复描述)
+ - **完整性**:90%(提供了动物的名称和特征,但有多余描述)
+ - **评分**:90%
+
+10. **地球自转一圈大约需要多少时间?**
+ - **准确性**:50%(描述部分正确,但有逻辑错误和多余描述)
+ - **清晰性**:40%(部分表述清晰,但有多余描述)
+ - **完整性**:40%(缺少对自转速度变化的解释,有多余描述)
+ - **评分**:40%
+
+**模型C总分**:590/900
+
+### 综合点评:
+
+- **模型A**:在多个问题上出现了准确性问题,特别是关于万有引力的提出者和世界上人口最多的国家。此外,回答中存在重复和逻辑不清的情况,导致清晰性和完整性评分较低。然而,在π的值和珠穆朗玛峰的问题上给出了准确的回答。
+
+- **模型B**:相较于模型A,模型B在大多数问题上提供了更准确的回答,尤其是在万有引力的提出者和太阳系中最大的行星问题上。然而,模型B在世界上人口最多的国家问题上给出了明显错误的信息,且在DNA全称问题上未能给出正确答案。
+
+- **模型C**:在万有引力的提出者和太阳系中最大的行星问题上给出了准确的回答,但在人口最多的国家问题上出现了严重的错误,且在DNA全称和二氧化碳化学式问题上出现了乱码,表明可能存在编码或处理错误。尽管如此,模型C在清晰性和完整性方面的表现略好于模型A。
+
+### 总结:
+
+在这次评估中,模型B的整体表现最佳,尽管它在某些问题上存在错误。模型A在准确性方面存在较多问题,而模型C虽然在某些问题上表现不错,但出现了技术性错误,影响了整体评分。所有模型在清晰性和完整性方面都有改进的空间。
+```
\ No newline at end of file
diff --git a/chat_openai_api.py b/chat_openai_api.py
new file mode 100644
index 0000000..f09e1f1
--- /dev/null
+++ b/chat_openai_api.py
@@ -0,0 +1,48 @@
+from openai import OpenAI
+
+client = OpenAI(
+ api_key="none",
+ base_url="http://202.195.167.142:8000/v1"
+)
+
+# 初始化对话历史列表
+conversation_history_origin = []
+conversation_history = conversation_history_origin.copy()
+while True:
+ conversation_history = conversation_history_origin.copy()
+ query = input('Enter Your Q: ')
+
+ # 将用户的问题添加到对话历史中
+ conversation_history.append({"role": "user", "content": query})
+
+ # Chat completion API
+ stream = client.chat.completions.create(
+ model="minimind",
+ messages=conversation_history, # 传递整个对话历史
+ stream=True
+ )
+
+ print('minimind: ', end='')
+ assistant_res = ''
+ for chunk in stream:
+ # 将生成的回复实时打印出来
+ print(chunk.choices[0].delta.content or "", end="")
+ assistant_res += chunk.choices[0].delta.content or ""
+
+ # 当完成生成回复后,将LLM的回答也添加到对话历史中
+ conversation_history.append({"role": "assistant", "content": assistant_res})
+ print()
+
+# # Example: reuse your existing OpenAI setup
+# from openai import OpenAI
+#
+# # Point to the local server
+# client = OpenAI(base_url="http://202.195.167.206:8000/v1", api_key="none")
+#
+# completion = client.chat.completions.create(
+# model="minimind",
+# messages=[{"role": "user", "content": "世界上最高的山是?"}],
+# stream=False
+# )
+#
+# print(completion.choices[0].message)
diff --git a/data_process.py b/data_process.py
new file mode 100644
index 0000000..7595186
--- /dev/null
+++ b/data_process.py
@@ -0,0 +1,209 @@
+import re
+import json
+import jsonlines
+import psutil
+import ujson
+import numpy as np
+import pandas as pd
+from transformers import AutoTokenizer
+from datasets import load_dataset
+
+bos_token = ""
+eos_token = ""
+
+
+# pretrain
+def process_wiki_clean():
+ with open('./dataset/clean-wikipedia-cn.json', 'r', encoding='utf-8') as f_read:
+ data = [ujson.loads(line) for line in f_read]
+ data_len = len(data)
+ doc_ids = []
+ for idx, line in enumerate(data):
+ text = line['response']
+ text_id = tokenizer(f'{bos_token}{text}{eos_token}').data['input_ids']
+ if len(text_id) > 5:
+ doc_ids += text_id
+ if idx % (int(data_len / 20)) == 0:
+ print(f"[{idx}/{data_len}] {text}")
+ arr = np.array(doc_ids, dtype=np.uint16)
+ with open('./dataset/clean-wikipedia-cn.bin', 'wb') as f:
+ f.write(arr.tobytes())
+
+
+# pretrain
+def process_other():
+ data = []
+
+ with open('./dataset/alpaca_gpt4_data_zh.json', 'r', encoding='utf-8') as f:
+ data_ = json.load(f)
+ data += data_
+
+ with open('./dataset/alpaca_data_zh_51k.json', 'r', encoding='utf-8') as f:
+ data_ = json.load(f)
+ data += data_
+
+ doc_ids = []
+ for idx, per in enumerate(data):
+ q = per['instruction']
+ i = per['input']
+ a = per['output']
+ q = q + i
+ if len(q) < 10 or len(a) < 5:
+ continue
+ if len(q) > 256 or len(a) > 256:
+ continue
+ text_id = tokenizer(f'{bos_token}{q},{a}{eos_token}').data['input_ids']
+ if len(text_id) > 5:
+ doc_ids += text_id
+ if idx % 50000 == 0:
+ print(idx, len(data))
+
+ arr = np.array(doc_ids, dtype=np.uint16)
+ with open('./dataset/clean_other.bin', 'wb') as f:
+ f.write(arr.tobytes())
+
+
+# pretrain
+def process_seq_monkey():
+ doc_ids = []
+ with jsonlines.open('./dataset/mobvoi_seq_monkey_general_open_corpus.jsonl') as reader:
+ for idx, obj in enumerate(reader):
+ content = obj.get('text', '')
+ if len(content) > 512:
+ continue
+ text_id = tokenizer(f'{bos_token}{content}{eos_token}').data['input_ids']
+ doc_ids += text_id
+ if idx % 50000 == 0:
+ print(f"seq_monkey: [{idx}]")
+
+ arr = np.array(doc_ids, dtype=np.uint16)
+ with open('./dataset/clean_seq_monkey.bin', 'wb') as f:
+ f.write(arr.tobytes())
+
+
+def pretrain_process():
+ # process_wiki_clean()
+ process_seq_monkey()
+
+ data_path_list = [
+ # './dataset/clean-wikipedia-cn.bin',
+ './dataset/clean_seq_monkey.bin'
+ ]
+ data_lst = []
+ for data_path in data_path_list:
+ with open(data_path, 'rb') as f:
+ data = np.fromfile(f, dtype=np.uint16)
+ data_lst.append(data)
+ arr = np.concatenate(data_lst)
+ print(arr.shape)
+ with open('./dataset/pretrain_data.bin', 'wb') as f:
+ f.write(arr.tobytes())
+
+
+def sft_process(contain_history=False):
+ file_name = 'sft_data.csv'
+ if not contain_history:
+ file_name = 'sft_data_single.csv'
+
+ def chinese_ratio(text):
+ # 匹配所有中文字符
+ chinese_chars = re.findall(r'[\u4e00-\u9fff]', text)
+ # 中文字符数量占比
+ return len(chinese_chars) / len(text) if text else 0
+
+ def process_and_write_data(data):
+ q_lst, a_lst, history_lst = [], [], []
+ for per in data:
+ history, q, a = per['history'], per['q'], per['a']
+
+ if (contain_history and not history) or not q or not a:
+ continue
+ if len(q) < 10 or len(a) < 5:
+ continue
+ if len(q) > 256 or len(a) > 256:
+ continue
+ # 判断q和a中中文字符占比是否超过70%
+ if not (chinese_ratio(q) > 0.9 and chinese_ratio(a) > 0.9):
+ continue
+
+ q_lst.append(q)
+ a_lst.append(a)
+ if contain_history:
+ history_lst.append(history)
+ else:
+ history_lst.append([])
+
+ # 创建DataFrame并追加到CSV文件
+ df = pd.DataFrame({'history': history_lst, 'q': q_lst, 'a': a_lst})
+ df.to_csv(f'./dataset/{file_name}', mode='a', header=False, index=False, lineterminator='\r\n')
+
+ chunk_size = 1000 # 每次处理的记录数
+ data = []
+
+ with open(f'./dataset/{file_name}', 'w', encoding='utf-8') as f:
+ f.write('history,q,a\n')
+
+ sft_datasets = ['./dataset/sft_data_zh_2.jsonl']
+ if not contain_history:
+ sft_datasets = ['./dataset/sft_data_zh_2.jsonl']
+
+ for path in sft_datasets:
+ with jsonlines.open(path) as reader:
+ for idx, obj in enumerate(reader):
+ data.append({
+ 'history': obj.get('history', ''),
+ 'q': obj.get('input', '') + obj.get('q', ''),
+ 'a': obj.get('output', '') + obj.get('a', '')
+ })
+
+ if len(data) >= chunk_size:
+ process_and_write_data(data)
+ data = []
+
+ if data:
+ process_and_write_data(data)
+ data = []
+
+
+def rl_process():
+ ################
+ # Dataset
+ ################
+
+ dataset_path = ['./dataset/dpo/dpo_zh_demo.json',
+ './dataset/dpo/train_1.json',
+ './dataset/dpo/huozi_rlhf_data.json', ]
+
+ train_dataset = load_dataset('json', data_files=dataset_path)
+
+ def process(row):
+ row["chosen"] = tokenizer.apply_chat_template(row["chosen"], tokenize=False)
+ row["reject"] = tokenizer.apply_chat_template(row["rejected"], tokenize=False)
+ return row
+
+ ds = train_dataset.map(
+ process,
+ load_from_cache_file=False,
+ )
+
+ output_dataset_path = './dataset/dpo/train_data.json'
+ ds['train'].to_json(output_dataset_path, force_ascii=False, orient='records', lines=True)
+
+
+if __name__ == "__main__":
+ tokenizer = AutoTokenizer.from_pretrained('./model', use_fast=False)
+ print('tokenizer词表大小:', len(tokenizer))
+
+ ################
+ # 1: pretrain
+ # 2: sft
+ # 3: RL
+ ################
+ process_type = 2
+
+ if process_type == 1:
+ pretrain_process()
+ if process_type == 2:
+ sft_process(contain_history=True)
+ if process_type == 3:
+ rl_process()
diff --git a/eval_ceval.py b/eval_ceval.py
new file mode 100644
index 0000000..21edd0b
--- /dev/null
+++ b/eval_ceval.py
@@ -0,0 +1,183 @@
+import random
+import time
+import os
+
+import pandas as pd
+import torch
+import warnings
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from model.model import Transformer
+from model.LMConfig import LMConfig
+import torch.nn.functional as F
+
+warnings.filterwarnings('ignore')
+
+
+def init_model(lm_config):
+ tokenizer = AutoTokenizer.from_pretrained('./model/minimind_tokenizer',
+ trust_remote_code=True, use_fast=False)
+ model_from = 1 # 1从权重,2用transformers
+
+ if model_from == 1:
+ moe_path = '_moe' if lm_config.use_moe else ''
+ ckp = f'./out/single_chat/full_sft_{lm_config.dim}{moe_path}.pth'
+
+ model = Transformer(lm_config)
+ state_dict = torch.load(ckp, map_location=device)
+
+ # 处理不需要的前缀
+ unwanted_prefix = '_orig_mod.'
+ for k, v in list(state_dict.items()):
+ if k.startswith(unwanted_prefix):
+ state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
+ # 加载到模型中
+ model.load_state_dict(state_dict, strict=False)
+ else:
+ model = AutoModelForCausalLM.from_pretrained('minimind', trust_remote_code=True)
+ model = model.to(device)
+
+ return model, tokenizer
+
+
+if __name__ == "__main__":
+ # -----------------------------------------------------------------------------
+ seed = random.randint(1, 2000)
+ # device = 'cuda:0'
+ device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
+ dtype = 'bfloat16'
+ lm_config = LMConfig()
+ # -----------------------------------------------------------------------------
+
+ model, tokenizer = init_model(lm_config)
+ model = model.eval()
+
+ # 消息模板,具体实现根据你的tokenizer进行调整
+ messages_origin = [{"role": "system", "content": "开始回答问题"}]
+
+ # 定义文件目录
+ File_Dir = "ceval/ceval-exam/val"
+ results_dir = "ceval/ceval_result"
+
+ # 确保结果目录存在
+ if not os.path.exists(results_dir):
+ os.makedirs(results_dir)
+
+ # 用于记录所有文件的总正确数和总题数
+ total_correct = 0
+ total_questions = 0
+
+ # 遍历目录下的所有CSV文件
+ for filename in os.listdir(File_Dir):
+ if filename.endswith('.csv'):
+ file_path = os.path.join(File_Dir, filename)
+ test_df = pd.read_csv(file_path)
+
+ # 存储结果的DataFrame
+ results_df = pd.DataFrame(columns=['question', 'A', 'B', 'C', 'D', 'answer', 'llm_answer', 'is_right'])
+ total_correct_in_file = 0 # 用于记录当前文件的正确数
+
+ for row in test_df.itertuples(index=True, name='Pandas'):
+ id = getattr(row, 'id')
+ question = getattr(row, 'question')
+ A = getattr(row, 'A')
+ B = getattr(row, 'B')
+ C = getattr(row, 'C')
+ D = getattr(row, 'D')
+ right_answer = getattr(row, 'answer')
+
+ prompt = f'{question}。选择 A: {A}, B: {B}, C: {C}, D: {D}'
+
+ messages = messages_origin.copy()
+ messages.append({"role": "user", "content": prompt})
+
+ # print(messages)
+ new_prompt = tokenizer.apply_chat_template(
+ messages,
+ tokenize=False,
+ add_generation_prompt=True
+ )
+ x = tokenizer(new_prompt).data['input_ids']
+ x = (torch.tensor(x, dtype=torch.long, device=device)[None, ...])
+ res_ids = model.eval_answer(x)
+
+ # 假设 res_ids 是模型的 logits 输出,我们使用 softmax 转换为概率分布
+ probabilities = F.softmax(res_ids, dim=-1)
+
+ # 定义每个选项的 token id
+ A_id = tokenizer('A').data['input_ids']
+ B_id = tokenizer('B').data['input_ids']
+ C_id = tokenizer('C').data['input_ids']
+ D_id = tokenizer('D').data['input_ids']
+
+ # 获取每个选项的概率
+ A_prob = probabilities[0, A_id].item()
+ B_prob = probabilities[0, B_id].item()
+ C_prob = probabilities[0, C_id].item()
+ D_prob = probabilities[0, D_id].item()
+
+ # 将每个选项的概率放入字典中便于处理
+ options_prob = {
+ 'A': A_prob,
+ 'B': B_prob,
+ 'C': C_prob,
+ 'D': D_prob
+ }
+
+ # 找到具有最大概率的选项
+ max_option_answer = max(options_prob, key=options_prob.get)
+
+ # 比较答案并记录
+ is_right = 1 if max_option_answer == right_answer else 0
+ results_df = results_df.append({
+ 'question': question,
+ 'A': A,
+ 'B': B,
+ 'C': C,
+ 'D': D,
+ 'answer': right_answer,
+ 'llm_answer': max_option_answer,
+ 'is_right': is_right
+ }, ignore_index=True)
+ # print(f'id: {id} 问题: {question[:10]}... 是否正确: {is_right}')
+
+ if is_right:
+ total_correct_in_file += 1
+
+ total_correct += total_correct_in_file
+ total_questions += len(test_df)
+
+ # 计算当前文件的正确率并添加到结果DataFrame的最后一行
+ accuracy = total_correct_in_file / len(test_df)
+ results_df = results_df.append({
+ 'question': '-',
+ 'A': '-',
+ 'B': '-',
+ 'C': '-',
+ 'D': '-',
+ 'answer': f'文件 {filename} 的正确率: {accuracy:.2%}',
+ 'llm_answer': '-',
+ 'is_right': '-'
+ }, ignore_index=True)
+
+ print(f'{filename.split(".")[0]} ,{total_correct_in_file}/{len(test_df)},正确率: {accuracy:.2%}')
+
+ # 保存结果到CSV
+ results_path = os.path.join(results_dir, f"{filename.split('.')[0]}_result.csv")
+ results_df.to_csv(results_path, index=False)
+
+ # 计算总正确率
+ total_accuracy = total_correct / total_questions if total_questions > 0 else 0
+
+ # 将各个文件的正确率以及总正确率写入到 "ceval/ceval_result/test.log"
+ log_path = os.path.join(results_dir, "test.log")
+ with open(log_path, 'w') as log_file:
+ result = f"总题数: {total_questions}\n总正确数: {total_correct}\n总正确率: {total_accuracy:.2%}"
+ log_file.write(result)
+
+ print(result)
+
+ for filename in os.listdir(File_Dir):
+ if filename.endswith('.csv'):
+ accuracy_file = pd.read_csv(os.path.join(results_dir, f"{filename.split('.')[0]}_result.csv"))
+ last_row = accuracy_file.iloc[-1]['answer']
+ log_file.write(f"{filename}: {last_row}\n")
diff --git a/export_model.py b/export_model.py
new file mode 100644
index 0000000..fd019c1
--- /dev/null
+++ b/export_model.py
@@ -0,0 +1,59 @@
+import torch
+import warnings
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from model.LMConfig import LMConfig
+from model.model import Transformer
+
+warnings.filterwarnings('ignore', category=UserWarning)
+
+def count_parameters(model):
+ return sum(p.numel() for p in model.parameters() if p.requires_grad)
+
+
+def export_transformers_model():
+ LMConfig.register_for_auto_class()
+ Transformer.register_for_auto_class("AutoModelForCausalLM")
+
+ lm_config = LMConfig()
+ lm_model = Transformer(lm_config)
+ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+ moe_path = '_moe' if lm_config.use_moe else ''
+ ckpt_path = f'./out/single_chat/full_sft_{lm_config.dim}{moe_path}.pth'
+
+ state_dict = torch.load(ckpt_path, map_location=device)
+ unwanted_prefix = '_orig_mod.'
+ for k, v in list(state_dict.items()):
+ if k.startswith(unwanted_prefix):
+ state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
+ lm_model.load_state_dict(state_dict, strict=False)
+ print(f'模型参数: {count_parameters(lm_model) / 1e6} 百万 = {count_parameters(lm_model) / 1e9} B (Billion)')
+
+ lm_model.save_pretrained("minimind-small", safe_serialization=False)
+
+
+def export_tokenizer():
+ tokenizer = AutoTokenizer.from_pretrained('./model',
+ trust_remote_code=True, use_fast=False)
+ tokenizer.save_pretrained("minimind-small")
+
+
+def push_to_hf():
+ def init_model():
+ tokenizer = AutoTokenizer.from_pretrained('./model/minimind_tokenizer',
+ trust_remote_code=True, use_fast=False)
+ model = AutoModelForCausalLM.from_pretrained('minimind-small', trust_remote_code=True)
+ return model, tokenizer
+
+ model, tokenizer = init_model()
+ # 推送到huggingface
+ model.push_to_hub("minimind-small")
+ # tokenizer.push_to_hub("minimind-small-T", safe_serialization=False)
+
+
+if __name__ == '__main__':
+ # 1
+ export_transformers_model()
+ # 2
+ export_tokenizer()
+ # # 3
+ # push_to_hf()
diff --git a/images/2-eval.png b/images/2-eval.png
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diff --git a/images/huggingface_logo-noborder.svg b/images/huggingface_logo-noborder.svg
new file mode 100644
index 0000000..43c5d3c
--- /dev/null
+++ b/images/huggingface_logo-noborder.svg
@@ -0,0 +1,37 @@
+
diff --git a/images/logger.png b/images/logger.png
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diff --git a/model/LMConfig.py b/model/LMConfig.py
new file mode 100644
index 0000000..bf0e4b9
--- /dev/null
+++ b/model/LMConfig.py
@@ -0,0 +1,58 @@
+from transformers import PretrainedConfig
+from typing import List
+
+
+class LMConfig(PretrainedConfig):
+ model_type = "minimind"
+
+ def __init__(
+ self,
+ dim: int = 512,
+ n_layers: int = 8,
+ n_heads: int = 16,
+ n_kv_heads: int = 8,
+ vocab_size: int = 6400,
+ hidden_dim: int = None,
+ multiple_of: int = 64,
+ norm_eps: float = 1e-5,
+ max_seq_len: int = 512,
+ dropout: float = 0.0,
+ flash_attn: bool = True,
+ ####################################################
+ # Here are the specific configurations of MOE
+ # When use_moe is false, the following is invalid
+ ####################################################
+ use_moe: bool = False,
+ num_experts_per_tok=2,
+ n_routed_experts=4,
+ n_shared_experts: bool = True,
+ scoring_func='softmax',
+ aux_loss_alpha=0.01,
+ seq_aux=True,
+ norm_topk_prob=True,
+ **kwargs,
+ ):
+ self.dim = dim
+ self.n_layers = n_layers
+ self.n_heads = n_heads
+ self.n_kv_heads = n_kv_heads
+ self.vocab_size = vocab_size
+ self.hidden_dim = hidden_dim
+ self.multiple_of = multiple_of
+ self.norm_eps = norm_eps
+ self.max_seq_len = max_seq_len
+ self.dropout = dropout
+ self.flash_attn = flash_attn
+ ####################################################
+ # Here are the specific configurations of MOE
+ # When use_moe is false, the following is invalid
+ ####################################################
+ self.use_moe = use_moe
+ self.num_experts_per_tok = num_experts_per_tok # 每个token选择的专家数量
+ self.n_routed_experts = n_routed_experts # 总的专家数量
+ self.n_shared_experts = n_shared_experts # 共享专家
+ self.scoring_func = scoring_func # 评分函数,默认为'softmax'
+ self.aux_loss_alpha = aux_loss_alpha # 辅助损失的alpha参数
+ self.seq_aux = seq_aux # 是否在序列级别上计算辅助损失
+ self.norm_topk_prob = norm_topk_prob # 是否标准化top-k概率
+ super().__init__(**kwargs)
diff --git a/model/__pycache__/LMConfig.cpython-310.pyc b/model/__pycache__/LMConfig.cpython-310.pyc
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diff --git a/model/dataset.py b/model/dataset.py
new file mode 100644
index 0000000..47387d5
--- /dev/null
+++ b/model/dataset.py
@@ -0,0 +1,122 @@
+import json
+import random
+import re
+
+import pandas as pd
+import numpy as np
+from torch.utils.data import Dataset, DataLoader
+import torch
+from sklearn.model_selection import train_test_split
+import os
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+
+class PretrainDataset(Dataset):
+ def __init__(self, data_path_lst, max_length=512, memmap=False):
+ super().__init__()
+ #
+ if memmap:
+ with open(data_path_lst[0], 'r') as f:
+ nbytes = f.seek(0, 2)
+ flen = f.tell() // np.dtype('uint16').itemsize
+ self.data = np.memmap(data_path_lst[0], dtype=np.dtype('uint16'), shape=(flen // max_length, max_length))
+ else:
+ data_lst = []
+ for data_path in data_path_lst:
+ with open(data_path, 'rb') as f:
+ data = np.fromfile(f, dtype=np.uint16)
+ data_lst.append(data)
+ data = np.concatenate(data_lst)
+ data = data[:max_length * int(len(data) / max_length)]
+ # np.random.shuffle(data)
+ self.data = data.reshape(-1, max_length)
+ #
+ print("memmap:{} train data.shape:{}".format(memmap, self.data.shape))
+ print("downloading finished.....")
+
+ def __len__(self):
+ return self.data.shape[0]
+
+ def __getitem__(self, index: int):
+ #
+ sample = self.data[index]
+ X = np.array(sample[:-1]).astype(np.int64)
+ Y = np.array(sample[1:]).astype(np.int64)
+
+ return torch.from_numpy(X), torch.from_numpy(Y)
+
+
+class SFTDataset(Dataset):
+ def __init__(self, df, tokenizer, max_length=1024, prompt_max_len=512, answer_max_len=256):
+ super().__init__()
+ self.df = df
+ self.max_length = max_length
+ self.prompt_max_len = prompt_max_len
+ self.answer_max_len = answer_max_len
+ #
+ self.tokenizer = tokenizer
+ self.padding = 0 # self.tokenizer.special_tokens['assistant').data['input_ids']
+
+ def __len__(self):
+ return self.df.shape[0]
+
+ def find_sublist_index(self, main_list, sub_list) -> int:
+ last_index = -1
+ for i in range(len(main_list) - len(sub_list) + 1):
+ if main_list[i:i + len(sub_list)] == sub_list:
+ last_index = i
+ return last_index
+
+ def __getitem__(self, index: int):
+ #
+ sample = self.df.iloc[index]
+ history = eval(sample['history'])
+ q = sample['q']
+ a = sample['a']
+
+ messages = []
+ for history_message in history:
+ if len(history_message) <= 1:
+ continue
+ messages.append(
+ {"role": 'user', "content": history_message[0][:self.max_length // 2]}
+ )
+ messages.append(
+ {"role": 'assistant', "content": history_message[1][:self.max_length // 2]}
+ )
+
+ messages += [
+ {"role": "user", "content": q},
+ {"role": "assistant", "content": a},
+ ]
+ new_prompt = self.tokenizer.apply_chat_template(
+ messages,
+ tokenize=False,
+ add_generation_prompt=True
+ )
+ input_id = self.tokenizer(new_prompt).data['input_ids'][:self.max_length]
+
+ # 实际长度
+ question_length = self.find_sublist_index(input_id, self.bos_id) + len(self.bos_id)
+ # 没满最大长度的剩余部分
+ padding_len = self.max_length - len(input_id)
+ input_id = input_id + [self.padding] * padding_len
+ mask_len = len(input_id) - question_length - padding_len
+ # 0表示不计算损失
+ loss_mask = [0] * question_length + [1] * (mask_len) + [0] * padding_len
+
+ input_id = np.array(input_id)
+ X = np.array(input_id[:-1]).astype(np.int64)
+ Y = np.array(input_id[1:]).astype(np.int64)
+ loss_mask = np.array(loss_mask[1:]).astype(np.int64)
+
+ X_tensor = torch.from_numpy(X)
+ Y_tensor = torch.from_numpy(Y)
+ loss_mask_tensor = torch.from_numpy(loss_mask)
+
+ return X_tensor, Y_tensor, loss_mask_tensor
+
+
+if __name__ == "__main__":
+ pass
diff --git a/model/minimind_tokenizer/merges.txt b/model/minimind_tokenizer/merges.txt
new file mode 100644
index 0000000..767c649
--- /dev/null
+++ b/model/minimind_tokenizer/merges.txt
@@ -0,0 +1,6142 @@
+#version: 0.2
+Ġ t
+Ġ a
+i n
+h e
+r e
+ï ¼
+ä ¸
+o n
+a t
+ç ļ
+çļ Ħ
+ï¼ Į
+Ġ s
+Ġ c
+n d
+ã Ģ
+e r
+Ġt he
+e s
+e n
+o r
+a n
+Ġa nd
+in g
+Ġ p
+i t
+a l
+ãĢ Ĥ
+Ġ o
+Ġ w
+ä »
+Ġt o
+i s
+o u
+Ġ m
+ä º
+Ġ in
+Ġ f
+Ġ b
+e d
+i on
+å ı
+i c
+Ġ d
+Ġo f
+l e
+a r
+r o
+Ġ Ġ
+å ħ
+en t
+æ ľ
+Ġ e
+å Ĵ
+è ¿
+ä ½
+åĴ Į
+æ Ī
+å ®
+å Ī
+v e
+u s
+Ġ re
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+Ġt h
+a s
+c t
+ç Ķ
+o m
+å ľ
+å ¤
+æ ĺ
+å Ĭ
+å IJ
+ä¸ Ģ
+i m
+è ¯
+æ ĸ
+at ion
+l o
+ç »
+Ġb e
+ãĢ ģ
+i d
+Ġc an
+i l
+æĺ ¯
+ä ¹
+è ®
+Ġ A
+Ġth at
+Ġ T
+ä» ¥
+c h
+Ġ y
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+o t
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+Ġ n
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+Ġ g
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+æ Ĺ
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+o w
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+es s
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+ä» ¬
+äº º
+å ĩ
+Ġe x
+ĠĠ ĠĠ
+å Ľ
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+æķ° æį®
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diff --git a/model/minimind_tokenizer/tokenizer.json b/model/minimind_tokenizer/tokenizer.json
new file mode 100644
index 0000000..dcfabc6
--- /dev/null
+++ b/model/minimind_tokenizer/tokenizer.json
@@ -0,0 +1,12603 @@
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+ "padding": null,
+ "added_tokens": [
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+ "Ġas s",
+ "åĨ ³",
+ "é Ľ",
+ "åIJ į",
+ "Ġg o",
+ "Ġtechnolo gy",
+ "æĢ »",
+ "è® ®",
+ "Ġin ter",
+ "Ġin v",
+ "Ġo ur",
+ "æķ Ī",
+ "ust om",
+ "Ġre l",
+ "if e",
+ "åĻ ¨",
+ "ing s",
+ "ä» ·",
+ "Ġp art",
+ "è¢ «",
+ "æī ĭ",
+ "ar y",
+ "Ġres pon",
+ "Ċ ĠĠĠ",
+ "好 çļĦ",
+ "at ive",
+ "帮 åĬ©",
+ "ç» Ł",
+ "æĶ ¾",
+ "ĠH ere",
+ "ç ģ",
+ "Ġb ut",
+ "æģ ¯",
+ "æŃ £",
+ "ar k",
+ "åħ¬ åı¸",
+ "or y",
+ "å¢ ĥ",
+ "le ct",
+ "é Ł",
+ "æĥ ³",
+ "é£ İ",
+ "at ing",
+ "Ġa m",
+ "it s",
+ "æ »",
+ "gor ith",
+ "åĵ į",
+ "ure s",
+ "Ġeff ect",
+ "Ġsh ould",
+ "Ġp er",
+ "è ±",
+ "ç ²",
+ "ic t",
+ "Ġal gorith",
+ "u c",
+ "rou gh",
+ "ä» »",
+ "ä» ¶",
+ "Ġbe t",
+ "i a",
+ "Ġanaly z",
+ "æł¹ æį®",
+ "iz ed",
+ "æµ ģ",
+ "è§ Ĥ",
+ "è £",
+ "æł ĩ",
+ "ir on",
+ "Ġc ustom",
+ "Ġre g",
+ "Ġperson al",
+ "èĥ½ å¤Ł",
+ "ic s",
+ "iv id",
+ "ç Ī",
+ "èµ Ħ",
+ "æŃ ¥",
+ "å® ¹",
+ "åĪ Ľ",
+ "è Ī",
+ "ä¹ IJ",
+ "å¯ ¼",
+ "g an",
+ "èĬ Ĥ",
+ "Ġal l",
+ "en s",
+ "am e",
+ "n ess",
+ "Ġu p",
+ "Ġ U",
+ "èĢ ĥ",
+ "el f",
+ "åĢ ¼",
+ "å° ij",
+ "æľ į",
+ "ar i",
+ "th ical",
+ "v iron",
+ "è ĥ",
+ "or d",
+ "Ġs ign",
+ "éĩ Į",
+ "ou nd",
+ "o ple",
+ "åŁ º",
+ "Ġinform ation",
+ "Ġident ify",
+ "åĽ ŀ",
+ "Ġc re",
+ "éŁ ³",
+ "ib le",
+ "u b",
+ "è¿ IJ",
+ "Ġle ad",
+ "æ¸ ¸",
+ "æ¬ ¡",
+ "åĨ Ļ",
+ "éĤ £",
+ "g et",
+ "è į",
+ "Ġexam ple",
+ "ä¼ ĺ",
+ "å½± åĵį",
+ "is h",
+ "x t",
+ "æ º",
+ "éª Į",
+ "o b",
+ "å® ¢",
+ "å¤ ĩ",
+ "åģ ¥",
+ "è½ ¦",
+ "ç¤ ¾",
+ "ivid ual",
+ "ere d",
+ "l es",
+ "Ġen viron",
+ "Ġpe ople",
+ "æĺ Ł",
+ "ç ĸ",
+ "ç ĭ",
+ "Ġd et",
+ "æĹ ł",
+ "Ġ if",
+ "o se",
+ "it e",
+ "å¢ ŀ",
+ "é Ĵ",
+ "åIJĮ æĹ¶",
+ "è¿ °",
+ "æĸ¹ å¼ı",
+ "åĽ ½",
+ "é »",
+ "å¤ Ħ",
+ "Ġexam ples",
+ "æ ®",
+ "Ġint o",
+ "æĮ ĩ",
+ "Ġhum an",
+ "åIJ ij",
+ "ç¤ º",
+ "æķ° æį®",
+ "Ġ 3",
+ "Ġ J",
+ "è ı",
+ "çݯ å¢ĥ",
+ "al s",
+ "ers t",
+ "Ġe thical",
+ "ç» Ħ",
+ "ä¼ ł",
+ "Ġdiffere nt",
+ "Ġk now",
+ "åº ı",
+ "Ġind ividual",
+ "æıIJ é«ĺ",
+ "rou nd",
+ "å° ±",
+ "åı ĸ",
+ "åŃ ĺ",
+ "ä¸ ¤",
+ "çŁ ¥",
+ "our ces",
+ "c k",
+ "å £",
+ "in es",
+ "è¾ ¾",
+ "Ġman y",
+ "æķ ´",
+ "æł ·",
+ "dit ional",
+ "om m",
+ "çĶ ±",
+ "éĢ ł",
+ "å®ĥ 们",
+ "u es",
+ "Ġm ent",
+ "Ġimport ant",
+ "Ġo pt",
+ "Ġlo c",
+ "p h",
+ "Ġpro cess",
+ "Ġalgorith ms",
+ "设 计",
+ "Ġsoc ial",
+ "ver y",
+ "åĪ Ļ",
+ "ä¾ĭ å¦Ĥ",
+ "è® ¤",
+ "Ġa ut",
+ "Ġs erv",
+ "g g",
+ "产 åĵģ",
+ "è§ Ħ",
+ "çľ ĭ",
+ "ve l",
+ "æĸ¹ æ³ķ",
+ "Ġb en",
+ "åĽł æŃ¤",
+ "c are",
+ "p er",
+ "åĬ Ł",
+ "建 议",
+ "Ġp os",
+ "æ ¤",
+ "w e",
+ "åĮ º",
+ "i qu",
+ "Ġre al",
+ "æĹ ¥",
+ "Ġredu ce",
+ "a f",
+ "ang u",
+ "Ġs k",
+ "Ġ ed",
+ "erst and",
+ "åĨ µ",
+ "m ot",
+ "åħ Ī",
+ "ç ¥",
+ "åºĶ 该",
+ "Ġth rough",
+ "Ġcon c",
+ "åıij å±ķ",
+ "è¯ ķ",
+ "æ¡ Ī",
+ "Ġenviron ment",
+ "åı £",
+ "Ġad v",
+ "åĪ «",
+ "Ġben ef",
+ "æ¸ ħ",
+ "åij ³",
+ "åħ ī",
+ "Ġdevelop ment",
+ "en g",
+ "å¦Ĥ ä½ķ",
+ "ç® ¡",
+ "iv ers",
+ "åIJ Ħ",
+ "Ġr is",
+ "ro w",
+ "er gy",
+ "计 ç®Ĺ",
+ "ä¿¡ æģ¯",
+ "Ġpro duct",
+ "è¾ ĥ",
+ "è® º",
+ "èĩªå·± çļĦ",
+ "æĬ ¤",
+ "åı į",
+ "åħ¶ ä»ĸ",
+ "åĪ Ĺ",
+ "ç» Ĩ",
+ "ç© º",
+ "Ġg reat",
+ "e ar",
+ "æº IJ",
+ "j ect",
+ "çĶŁ æ´»",
+ "ä¸Ń çļĦ",
+ "Ġund erstand",
+ "è ĭ",
+ "h at",
+ "Ġpro gra",
+ "ç Ĭ",
+ "éĩ ij",
+ "Ġinclud ing",
+ "Ġacc ess",
+ "ĠĠĠĠ ĠĠĠ",
+ "è¯ Ĩ",
+ "ç ¦",
+ "o g",
+ "è£ ħ",
+ "Ġar t",
+ "Ġw rit",
+ "Ġinc re",
+ "Ġp h",
+ "æĸ¹ éĿ¢",
+ "Ġp ract",
+ "Ġus ing",
+ "é¡ ¹",
+ "æİ ¥",
+ "Ġway s",
+ "Ġl angu",
+ "æĶ ¯",
+ "Ġch all",
+ "åİ »",
+ "__ __",
+ "im ate",
+ "æĸ Ń",
+ "è ¨",
+ "Ġw ell",
+ "l l",
+ "Ġp ol",
+ "æĢ ģ",
+ "Ġ ra",
+ "C an",
+ "åİ Ł",
+ "b er",
+ "è¨ Ģ",
+ "ç« ĭ",
+ "Ġg en",
+ "éħ į",
+ "æ· ±",
+ "t e",
+ "ä¸ ī",
+ "ç§ ij",
+ "ĠF or",
+ "çº ¿",
+ "ç ħ",
+ "æ ¼",
+ "åķ Ĩ",
+ "æĿ IJ",
+ "Ġsign ific",
+ "Ġg u",
+ "Ġde cis",
+ "Ġtra in",
+ "Ġa g",
+ "Ġc reat",
+ "å® Į",
+ "æĹ¶ éĹ´",
+ "Ġon e",
+ "è Ħ",
+ "Ġn at",
+ "åѦ ä¹ł",
+ "çļĦæ ķ",
+ "c ed",
+ "Ġwhe n",
+ "Ġb i",
+ "è İ",
+ "æĽ´ åĬł",
+ "iv es",
+ "p ort",
+ "å·¥ ä½ľ",
+ "v ing",
+ "Ġbe en",
+ "æĻ º",
+ "Ġl ife",
+ "å¼ ķ",
+ "ar m",
+ "çİ ĩ",
+ "ç͍ æĪ·",
+ "ä¹ ī",
+ "ä» ½",
+ "è¯ Ŀ",
+ "in ess",
+ "c om",
+ "åº ·",
+ "åĩ ı",
+ "ä» Ģ",
+ "è¾ ĵ",
+ "Ġv ari",
+ "c on",
+ "Ġmo d",
+ "ä»Ģ ä¹Ī",
+ "Ġen ergy",
+ "æĬĢ æľ¯",
+ "ert ain",
+ "m m",
+ "ver all",
+ "åĪ Ĵ",
+ "Ġro bots",
+ "Ġor gan",
+ "æİ ¨",
+ "ant s",
+ "åĩ Ĩ",
+ "d s",
+ "æŀ ģ",
+ "ç Ļ",
+ "Ġre qu",
+ "Ġ ess",
+ "ç® Ģ",
+ "ust ain",
+ "æ ¨",
+ "Ġst r",
+ "c ing",
+ "ab ility",
+ "re e",
+ "Ġed uc",
+ "åİ Ĩ",
+ "Ġcre ate",
+ "åģ¥ åº·",
+ "Ġdes ign",
+ "i ps",
+ "åģ ļ",
+ "èĬ ±",
+ "in k",
+ "èı ľ",
+ "æī ¾",
+ "æ® µ",
+ "æµ ĭ",
+ "Ġ V",
+ "ĠB y",
+ "å Ķ",
+ "é¦ ĸ",
+ "è¯ į",
+ "Ġwhe re",
+ "Ġdis c",
+ "äºĨ è§£",
+ "r ic",
+ "ä¸ Ķ",
+ "è¶ ³",
+ "æĺ¯ ä¸Ģ个",
+ "ar ch",
+ "ç§ ¯",
+ "å¸ ¦",
+ "Ġwh ile",
+ "Ġsignific ant",
+ "çł ģ",
+ "æĪ ¿",
+ "Ġbe ing",
+ "Ġlangu age",
+ "it ive",
+ "2 0",
+ "Ġanalyz e",
+ "æĻ ¯",
+ "è Į",
+ "ri b",
+ "æ¨ ¡",
+ "ĠS t",
+ "è´ ¹",
+ "' t",
+ "Ġhealth care",
+ "Ġexperi ence",
+ "Ġ 5",
+ "个 人",
+ "ay s",
+ "è± ¡",
+ "p lo",
+ "Ġw ould",
+ "èĻ ij",
+ "æĶ ¶",
+ "é¢ Ħ",
+ "é¢ Ĩ",
+ "ä¿Ŀ æĮģ",
+ "en ces",
+ "åı ª",
+ "èĩ ´",
+ "æĪ ı",
+ "Ġment al",
+ "Ġfe w",
+ "at es",
+ "è¿ĩ ç¨ĭ",
+ "å®ī åħ¨",
+ "Ġs ustain",
+ "Ġw ere",
+ "å¤ ª",
+ "ç Į",
+ "Ġspec ific",
+ "Ġwor ld",
+ "çŃ Ķ",
+ "`` `",
+ "Ġt ake",
+ "åħ »",
+ "éĢ Ł",
+ "e ver",
+ "S S",
+ "éĶ Ģ",
+ "Ġb o",
+ "he s",
+ "Ġm us",
+ "æľį åĬ¡",
+ "è§ Ĵ",
+ "t en",
+ "æŀ IJ",
+ "p ow",
+ "d ict",
+ "v ent",
+ "1 0",
+ "çļĦæ Ĺ",
+ "ĸ çķ",
+ "Ġpro t",
+ "ç½ ®",
+ "Ġh igh",
+ "Ġb us",
+ "Ġind ust",
+ "åIJ ¦",
+ "c ial",
+ "人 们",
+ "ĠA s",
+ "åij Ĭ",
+ "ad e",
+ "æĶ ¹",
+ "ç Ĺ",
+ "Ġh ad",
+ "Ġhe r",
+ "Ġj ust",
+ "ï¼ Ľ",
+ "è´ Ń",
+ "ç¬ ¬",
+ "é ĵ",
+ "Ġw ater",
+ "Ġf ood",
+ "éĺ Ł",
+ "a us",
+ "Ġchall eng",
+ "åħ į",
+ "æĸĩ åĮĸ",
+ "Ġmo st",
+ "é ¸",
+ "ç½ ij",
+ "çĽ ´",
+ "Ġs m",
+ "Ġact iv",
+ "plo y",
+ "O verall",
+ "å¿ «",
+ "ru ct",
+ "Ġindividual s",
+ "å§ ĭ",
+ "g ies",
+ "æŁ ¥",
+ "çĪ ±",
+ "i ety",
+ "I n",
+ "åĪĨ æŀIJ",
+ "è§ Ĩ",
+ "æ¸ ©",
+ "ç» ´",
+ "ol ut",
+ "åŁ Ł",
+ "omm end",
+ "Ġcom ple",
+ "æķ Ļ",
+ "Ġb u",
+ "Ġeduc ation",
+ "at her",
+ "Ġ 4",
+ "t ing",
+ "Ġf ind",
+ "æ² ¡",
+ "Ġh is",
+ "ä¹ĭ éĹ´",
+ "Ġeffect ive",
+ "Ġat t",
+ "Ġre se",
+ "èĥ½ åĬĽ",
+ "åŁ İ",
+ "Ġal low",
+ "Ġa v",
+ "Ġpro mot",
+ "æĻº èĥ½",
+ "æ» ¡",
+ "åħ ±",
+ "ie w",
+ "c ome",
+ "ç³» 绣",
+ "Ġrespon s",
+ "äº Ĵ",
+ "Ġc ult",
+ "pow ered",
+ "Ġrec ommend",
+ "èIJ ¥",
+ "O SS",
+ "Ġch ange",
+ "è¯ ģ",
+ "v ed",
+ "æİ Ĵ",
+ "è§£ åĨ³",
+ "ic i",
+ "ĠH ow",
+ "Ġfe el",
+ "æľ Ī",
+ "Ġwh at",
+ "以 åıĬ",
+ "Ġse e",
+ "åŃ ©",
+ "b s",
+ "Ġs ur",
+ "æ £",
+ "al ity",
+ "Ġv is",
+ "ç¡® ä¿Ŀ",
+ "p ect",
+ "å®ŀ çݰ",
+ "Ġc are",
+ "å¹ ¿",
+ "ill s",
+ "åº Ń",
+ "as es",
+ "å¤ į",
+ "åºĶ ç͍",
+ "çļĦæ ĥ",
+ "ard s",
+ "Ġadd ress",
+ "Ġcomp an",
+ "Ġinv ol",
+ "Ġcustom er",
+ "åĽł 为",
+ "Ġstud ents",
+ "Ġin s",
+ "注 æĦı",
+ "æŀ Ħ",
+ "æ¬ ¢",
+ "æµ ·",
+ "åı Ĥ",
+ "èĩª çĦ¶",
+ "é ©",
+ "ĠThe se",
+ "w n",
+ "æĺ ĵ",
+ "çĬ ¶",
+ "re n",
+ "Ġt reat",
+ "Ġbenef its",
+ "Ċ ĠĠĠĠĠĠĠ",
+ "对 äºİ",
+ "æĢ Ŀ",
+ "id er",
+ "ĠY es",
+ "Ġ K",
+ "åĸ ľ",
+ "Ġ ke",
+ "Ġen g",
+ "Ġpo p",
+ "o st",
+ "p are",
+ "Ġm on",
+ "æ¬ ¾",
+ "ĠM OSS",
+ "Ġem ot",
+ "Ġa c",
+ "ç¼ ĸ",
+ "f ore",
+ "åı ¥",
+ "Ġv al",
+ "il y",
+ "Ġis s",
+ "èĤ ī",
+ "èĩ ³",
+ "游 æĪı",
+ "we en",
+ "Ġinclud e",
+ "Ġprot ect",
+ "åħ³ ç³»",
+ "éĻ ©",
+ "Ġse ver",
+ "Ġth an",
+ "éľĢ æ±Ĥ",
+ "ç» ĥ",
+ "ĠThe y",
+ "is s",
+ "y s",
+ "Ġj ob",
+ "éĺ ³",
+ "æ IJ",
+ "Ġbet ween",
+ "Ġm ach",
+ "---- ----",
+ "èĢĥ èĻij",
+ "è´¨ éĩı",
+ "Ġbus iness",
+ "w or",
+ "ic k",
+ "e g",
+ "åħ ħ",
+ "ç ¯",
+ "æĿ ¡",
+ "n er",
+ "a pt",
+ "Ġapp ro",
+ "Ġpl ay",
+ "没 æľī",
+ "¤ IJ",
+ "æľ ª",
+ "æĪ ĺ",
+ "å®¶ åºŃ",
+ "ãĢ ĭ",
+ "en cy",
+ "ĠC h",
+ "ãĢ Ĭ",
+ "Ġprovid ing",
+ "Ġres ources",
+ "âĢ Ļ",
+ "Ġass ist",
+ "Ġnat ural",
+ "è¯ Ħ",
+ "ä¾ ¿",
+ "Ġs af",
+ "åħ· æľī",
+ "è° ¢",
+ "çĥ Ń",
+ "s s",
+ "et h",
+ "ol d",
+ "Ġper form",
+ "Ġsever al",
+ "é ¤IJ",
+ "Ġe ach",
+ "è½ ¬",
+ "c i",
+ "Ġt y",
+ "Ġp ub",
+ "æ´» åĬ¨",
+ "oc us",
+ "çī Į",
+ "è¶ Ĭ",
+ "åĽ ¢",
+ "è½ »",
+ "è¯Ń è¨Ģ",
+ "Ġare as",
+ "éĩ ĩ",
+ "f t",
+ "ri end",
+ "å· ²",
+ "å¸Ĥ åľº",
+ "it ion",
+ "i ents",
+ "管 çIJĨ",
+ "è® ¸",
+ "人 类",
+ "身 ä½ĵ",
+ "iqu e",
+ "Ġpart ic",
+ "ç» Ń",
+ "age ment",
+ "v es",
+ "ç¬ ¦",
+ "l ine",
+ "çº ¢",
+ "åIJ ¸",
+ "Ġpat ter",
+ "00 0",
+ "社 ä¼ļ",
+ "åĨħ 容",
+ "Ġorgan iz",
+ "ou gh",
+ "Ġ ve",
+ "åŃ© åŃIJ",
+ "æĸ ½",
+ "æ¤ į",
+ "åĩ ł",
+ "ä½Ĩ æĺ¯",
+ "Ġa ff",
+ "Ġn um",
+ "le ment",
+ "èī º",
+ "è ij",
+ "Ġc ar",
+ "ag es",
+ "ab or",
+ "æĺ¯ä¸Ģ ç§į",
+ "Ġin st",
+ "è Ľ",
+ "ä¹ĭ ä¸Ģ",
+ "è· ¯",
+ "åį ³",
+ "Ġm ain",
+ "éļ ı",
+ "H ow",
+ "å¿ ħ",
+ "ç¨ĭ åºı",
+ "éŁ³ ä¹IJ",
+ "re d",
+ "æ² ¹",
+ "Ġoff er",
+ "et s",
+ "ç ¢",
+ "Ġd uring",
+ "çļĦ 人",
+ "æĽ´ å¤ļ",
+ "Ġd i",
+ "代 çłģ",
+ "èİ ·",
+ "åħ ĭ",
+ "Ġgu id",
+ "主 è¦ģ",
+ "Ġf am",
+ "æİ §",
+ "éĢļ 常",
+ "ĠA d",
+ "å¤Ħ çIJĨ",
+ "ur n",
+ "ow er",
+ "åij ½",
+ "æı ı",
+ "Ġsk ills",
+ "Ġto ol",
+ "w are",
+ "æĸĩ æľ¬",
+ "Ġpatter ns",
+ "缮 æłĩ",
+ "ac y",
+ "æī ĵ",
+ "åŁİ å¸Ĥ",
+ "Ġe very",
+ "r ies",
+ "è¯ »",
+ "éģ ¿",
+ "çĻ ½",
+ "éĢĤ åIJĪ",
+ "Ġpat ient",
+ "çľ Ł",
+ "ot h",
+ "å¥ ¹",
+ "åĶ ®",
+ "ä¸Ģ ç§į",
+ "Ġm ade",
+ "ä½ İ",
+ "is e",
+ "Ġre m",
+ "æ¶ Ī",
+ "åIJ «",
+ "a ir",
+ "Ġgen er",
+ "o y",
+ "ç² ¾",
+ "æĥħ åĨµ",
+ "ight s",
+ "Ġexp l",
+ "è§ ģ",
+ "Ġpre dict",
+ "ç± ³",
+ "æĽ´ 好",
+ "ä¿ ®",
+ "Ġcl imate",
+ "Ġf ocus",
+ "Ġg row",
+ "客 æĪ·",
+ "ä¸į æĸŃ",
+ "it or",
+ "ĠE n",
+ "çº ¦",
+ "æĺ¯ åIJ¦",
+ "ä» ħ",
+ "æĪij们 çļĦ",
+ "æľ Ľ",
+ "o p",
+ "Ġm aking",
+ "y th",
+ "cc ess",
+ "Ġo wn",
+ "gg est",
+ "Ġt as",
+ "ut ure",
+ "Ġmod el",
+ "p ut",
+ "Ġrese arch",
+ "ere st",
+ "éļ ¾",
+ "Ġ [",
+ "i el",
+ "ation al",
+ "Ġcommun ic",
+ "ç¥ ŀ",
+ "ç© ¶",
+ "Ġre st",
+ "æĪIJ 为",
+ "k ing",
+ "p r",
+ "åĮ »",
+ "c ur",
+ "èĤ ²",
+ "Ġ '",
+ "è¿Ļ ç§į",
+ "ç¯ ĩ",
+ "Ġc he",
+ "ow n",
+ "éĻ ħ",
+ "Ġf in",
+ "åζ ä½ľ",
+ "Ġsu ggest",
+ "å¢ŀ åĬł",
+ "Ġmed ia",
+ "rib ut",
+ "çļĦæĥ ħ",
+ "åĬł åħ¥",
+ "Ġc le",
+ "åij ¨",
+ "ç« ł",
+ "Ġth ink",
+ "Ġloc al",
+ "pport un",
+ "ĠY ou",
+ "Ġpl an",
+ "Ġev en",
+ "éĽ Ĩ",
+ "å· §",
+ "a x",
+ "Ġchalleng es",
+ "Ġpro f",
+ "ĠC an",
+ "Ġconc er",
+ "Ġf uture",
+ "åĬ ¿",
+ "Ġre f",
+ "èģ Ķ",
+ "Ġs elf",
+ "æĪĸ èĢħ",
+ "b le",
+ "åĽ ´",
+ "è¿IJ åĬ¨",
+ "Ġin f",
+ "éĩ Ĭ",
+ "Ġsustain able",
+ "Ġte xt",
+ "Ġg ra",
+ "äº Į",
+ "åĵģ çīĮ",
+ "ä¸įåIJĮ çļĦ",
+ "l ed",
+ "çĭ ¬",
+ "Ġo pportun",
+ "Ġcont in",
+ "y m",
+ "Ġg et",
+ "å¯ Ĩ",
+ "éĻ ¤",
+ "æ ħ",
+ "éģ¿ åħį",
+ "Ġ +",
+ "è§ ī",
+ "Ġre t",
+ "å¸ ĥ",
+ "Ġint erest",
+ "Ġsoc iety",
+ "ç»ĵ æŀľ",
+ "åIJ ¬",
+ "é¦ĸ åħĪ",
+ "Ġb re",
+ "Ġ2 0",
+ "ĠHow ever",
+ "è® °",
+ "on s",
+ "è¿ ij",
+ "å¼Ģ å§ĭ",
+ "Ġbu ild",
+ "Ġbe h",
+ "' m",
+ "v ers",
+ "Ġg ood",
+ "çIJĨ è§£",
+ "res ent",
+ "ç¦ »",
+ "åĬŁ èĥ½",
+ "Ġeff ort",
+ "l abor",
+ "é» ij",
+ "Ġbet ter",
+ "Ġre ad",
+ "å¾ ĭ",
+ "èĽ ĭ",
+ "he d",
+ "ä¹ °",
+ "导 èĩ´",
+ "Ġimp lement",
+ "ç ¿",
+ "äº «",
+ "å¤ ´",
+ "en se",
+ "Ġl ong",
+ "ot her",
+ "é¥ ®",
+ "åŃĺ åľ¨",
+ "çļĦæ Ħ",
+ "ä¸Ģ 份",
+ "yth on",
+ "n ing",
+ "åĩı å°ij",
+ "åĢ Ļ",
+ "ä¸ ĵ",
+ "åIJĦ ç§į",
+ "è ħ",
+ "å° ½",
+ "åį ĩ",
+ "æĬ ¥",
+ "Ġpub lic",
+ "Ġl ar",
+ "ä½ł çļĦ",
+ "a ut",
+ "é¢Ĩ åŁŁ",
+ "æ ļ",
+ "ol low",
+ "èģ Į",
+ "Ġch ang",
+ "Ġb est",
+ "h ip",
+ "åĨ į",
+ "ak es",
+ "Ġch at",
+ "it ed",
+ "Ġp ower",
+ "ä¿Ŀ æĬ¤",
+ "ä¹ ¦",
+ "计 åĪĴ",
+ "éĩįè¦ģ çļĦ",
+ "åıĺ åĮĸ",
+ "il ities",
+ "Ġcons ider",
+ "æĪij们 åı¯ä»¥",
+ "éĤ£ ä¹Ī",
+ "Ġ ide",
+ "æ¼ Ķ",
+ "ag ing",
+ "Ġb ased",
+ "å® Ŀ",
+ "Ġr ange",
+ "Ġres ult",
+ "Ġm em",
+ "çħ §",
+ "Ġle vel",
+ "c ou",
+ "Ġb r",
+ "T h",
+ "ä¼ ģ",
+ "建 ç«ĭ",
+ "Ġun ique",
+ "è® Ń",
+ "Ġm ark",
+ "许 å¤ļ",
+ "è¡Į 为",
+ "Ķ ç©¶",
+ "çļĦæ Ĭ",
+ "Ġs et",
+ "éª ¤",
+ "t s",
+ "Ġh ist",
+ "Ġa round",
+ "Ġre v",
+ "åħ¶ ä¸Ń",
+ "ï¼ ģ",
+ "æıı è¿°",
+ "æľĢ åIJİ",
+ "Ġs im",
+ "n ect",
+ "åĽŀ çŃĶ",
+ "éĺ ²",
+ "èī ¯",
+ "åΰ äºĨ",
+ "ä¸ ĸçķ",
+ "æĸ¹ æ¡Ī",
+ "æĿIJ æĸĻ",
+ "ä¸ĸçķ Į",
+ "æĽ´å¥½ åľ°",
+ "两 个",
+ "Ġem ploy",
+ "Ġtr y",
+ "æ ĵ",
+ "Ġb ack",
+ "åĪ ĩ",
+ "Ġsu ccess",
+ "Ġdecis ions",
+ "Ġth ose",
+ "å¯ Į",
+ "Ġf act",
+ "æİ ¢",
+ "è¶ £",
+ "Ġpract ices",
+ "åIJ Ĺ",
+ "æī į",
+ "çİ ©",
+ "pt ion",
+ "æĸĩ 竳",
+ "Ġfe at",
+ "Ġpre vent",
+ "Ġwrit ing",
+ "çļĦæ Ģ",
+ "Ġn o",
+ "ä» ĭ",
+ "éĹ ¨",
+ "Ġd el",
+ "æ Ĵ",
+ "Ġopt im",
+ "in ation",
+ "Ġ Ċ",
+ "us ion",
+ "Ġacc ount",
+ "l ing",
+ "Ġd ivers",
+ ". \"",
+ "at h",
+ "èĭ ±",
+ "ä¼ģ ä¸ļ",
+ "Ġg rou",
+ "åľ° çIJĥ",
+ "å¤ ±",
+ "Ġpersonal ized",
+ "ĠH e",
+ "表 达",
+ "cur ity",
+ "Ġf ollow",
+ "产 çĶŁ",
+ "Ġe ar",
+ "åİ ĭ",
+ "ver n",
+ "Ġiss ues",
+ "åĿ ĩ",
+ "é ²",
+ "Ġd r",
+ "iv ing",
+ "Ġtrain ing",
+ "Ġris k",
+ "åĩ ½",
+ "åı ²",
+ "æ ij",
+ "çļĦæĹ ¶",
+ "og n",
+ "Ġrequ ire",
+ "Ġenvironment al",
+ "b ack",
+ "éĶ ®",
+ "çĸ Ĺ",
+ "Ġinter act",
+ "åĽ¢ éĺŁ",
+ "æ¯ı 个",
+ "çĦ¶ åIJİ",
+ "Ġd ist",
+ "ç͍ äºİ",
+ "认 为",
+ "åĩ½ æķ°",
+ "Ġs ent",
+ "Ċ ĠĠĠĠĠĠĠĠ",
+ "Ġredu cing",
+ "å¹ ²",
+ "Ġre p",
+ "Ġc aus",
+ "Ġmus ic",
+ "ç ª",
+ "Ġmon itor",
+ "Ġfor m",
+ "é¢ ľ",
+ "çĹ ħ",
+ "é¦ Ļ",
+ "Ġof ten",
+ "åı¯èĥ½ ä¼ļ",
+ "åijĺ å·¥",
+ "Ġha nd",
+ "æĬ ķ",
+ "Ġneed s",
+ "æŃ¤ å¤ĸ",
+ "åı ĭ",
+ "iv ity",
+ "Ġactiv ities",
+ "åĸľ 欢",
+ "Ġp ur",
+ "i an",
+ "s elf",
+ "åĬ¨ çī©",
+ "com es",
+ "å ©",
+ "Ġpr iv",
+ "a z",
+ "Ġrel ations",
+ "Ġmach ine",
+ "çļĦæ °",
+ "ä»· æł¼",
+ "ä»· å̼",
+ "ç´ ¢",
+ "Ġfe ed",
+ "ä¸Ģ ä¸ĭ",
+ "Ġte am",
+ "Ġindust ry",
+ "è´ ¢",
+ "ĠP ro",
+ "Ġw ant",
+ "ç§ °",
+ "Ġcl ass",
+ "Ġlo ve",
+ "åħ³ äºİ",
+ "è¾ĵ åħ¥",
+ "Ġtrans port",
+ "Ġcomple x",
+ "Ġy ear",
+ "éĶĢ åĶ®",
+ "å¯ »",
+ "i ence",
+ "ist s",
+ "æĶ¯ æĮģ",
+ "Ġm ind",
+ "Ġf un",
+ "Ġch ar",
+ "æĮ ī",
+ "Ġconcer ns",
+ "con om",
+ "ç®Ģ åįķ",
+ "以ä¸ĭ æĺ¯",
+ "Ġst art",
+ "å¹¶ ä¸Ķ",
+ "av i",
+ "ä¸Ń åĽ½",
+ "åħĥ ç´ł",
+ "Ġcon f",
+ "Ġpos itive",
+ "Ġc ur",
+ "Ġc ount",
+ "er y",
+ "å ¡",
+ "å® ¤",
+ "Ġco st",
+ "Ġe qu",
+ "Ġpol ic",
+ "ast e",
+ "a w",
+ "éħ Ĵ",
+ "cou ra",
+ "iv en",
+ "pl ace",
+ "ch ie",
+ "çļĦæķ °",
+ "åĽł ç´ł",
+ "Ġf l",
+ "is m",
+ "Ġmed ical",
+ "Ġhum ans",
+ "Ġaut om",
+ "ertain ly",
+ "Ġ 0",
+ "Ġoff ers",
+ "Ġdet ect",
+ "Ġ 6",
+ "é£İ æł¼",
+ "Ġsh ow",
+ "çģ «",
+ "Ġan im",
+ "é¢ľ èī²",
+ "le ase",
+ "a ve",
+ "åĵ ª",
+ "ĠThe re",
+ "以 ä¸Ĭ",
+ "æľª æĿ¥",
+ "X X",
+ "çī ĩ",
+ "u ch",
+ "Ġtas ks",
+ "åħ· ä½ĵ",
+ "æ¤į çī©",
+ "Ġm in",
+ "èīº æľ¯",
+ "ic ult",
+ "Ġexperi ences",
+ "æİ§ åζ",
+ "b e",
+ "Ġpat ients",
+ "å ²",
+ "ĠW e",
+ "Ġrec ogn",
+ "çĥ ¤",
+ "Ġsm all",
+ "åĿ Ĺ",
+ "å Ħ",
+ "太 éĺ³",
+ "ct ion",
+ "Ġ ent",
+ "æį ¢",
+ "Ġbe fore",
+ "Ġbe come",
+ "å·² ç»ı",
+ "表 çݰ",
+ "Ġexp lo",
+ "Ġa chie",
+ "ä»» åĬ¡",
+ "大 çļĦ",
+ "Ġd ay",
+ "Ġf ound",
+ "å± ±",
+ "on d",
+ "Ġtreat ment",
+ "pe nd",
+ "he n",
+ "Ġcon dit",
+ "ç¡® å®ļ",
+ "Ġbusiness es",
+ "ĠW h",
+ "æīĢ æľī",
+ "Ġdevelop ed",
+ "ç» Ī",
+ "æŃ¥ 骤",
+ "Ġdiff icult",
+ "åı ·",
+ "ĠR e",
+ "éĶ Ļ",
+ "Ġch o",
+ "Ġqu est",
+ "Ġtrans pare",
+ "Ġpro ject",
+ "Ġcommun ity",
+ "o v",
+ "å¸ Ī",
+ "å¼ ł",
+ "åĪĨ ç±»",
+ "人 çļĦ",
+ "s is",
+ "çĽ Ĭ",
+ "o id",
+ "ĠA n",
+ "w ays",
+ "Ġe as",
+ "Ġaff ect",
+ "Ġother s",
+ "Ġreg ul",
+ "æĢ§ åĴĮ",
+ "åĸ Ħ",
+ "ag n",
+ "ä½ľ 为",
+ "åı¯ä»¥ 帮åĬ©",
+ "åĦ ¿",
+ "Ġorganiz ations",
+ "é¸ ¡",
+ "åħ ´",
+ "Ġf riend",
+ "Ġ $",
+ "Ġdet ail",
+ "Ġtra ditional",
+ "Ġdesign ed",
+ "è´Ń ä¹°",
+ "ä½ĵ éªĮ",
+ "ç» į",
+ "er m",
+ "Ġcon nect",
+ "è¿Ļ æł·",
+ "Ġrecommend ations",
+ "Ġb oth",
+ "Ł éĢļ",
+ "æ¯ į",
+ "Ġs it",
+ "ä½ľ ç͍",
+ "ä»ĭ ç»į",
+ "Ġst e",
+ "ĠS ure",
+ "åı °",
+ "æĤ¨ çļĦ",
+ "Ġs he",
+ "Ġman agement",
+ "j oy",
+ "è´ Ł",
+ "Ġpromot e",
+ "Ġvari ous",
+ "( \"",
+ "p or",
+ "Ġs ens",
+ "Ġess ential",
+ "get her",
+ "ular ly",
+ "äº ī",
+ "ir st",
+ "Ġo p",
+ "Ġspec ies",
+ "çݰ åľ¨",
+ "ch o",
+ "Ġbeh avi",
+ "çŃ ij",
+ "å¥ ³",
+ "Ġqu ality",
+ "Ġex t",
+ "è ¥",
+ "å®Į æĪIJ",
+ "æĢ» ä¹ĭ",
+ "éĥ¨ åĪĨ",
+ "ä»İ èĢĮ",
+ "åĽ ¾",
+ "Ġty p",
+ "Ġstr ate",
+ "è¥ ¿",
+ "Ġhe re",
+ "ar s",
+ "å¸ Į",
+ "çļĦæ Ŀ",
+ "å° Ŀ",
+ "e e",
+ "i er",
+ "Ġe c",
+ "ical ly",
+ "er ing",
+ "å¿ µ",
+ "ĠD e",
+ "Ġne g",
+ "建 çŃij",
+ "Ġserv ices",
+ "Ġab le",
+ "im es",
+ "Ġopt ions",
+ "缸 åħ³",
+ "Ġsu b",
+ "Ġdecis ion",
+ "ĠC ertainly",
+ "Ġ åľ¨",
+ "æ ¢",
+ "Ġserv ice",
+ ") :",
+ "带 æĿ¥",
+ "Ġch ild",
+ "è§£ éĩĬ",
+ "ir t",
+ "ç Ĩ",
+ "ä¸į ä»ħ",
+ "æĿ ¾",
+ "积 æŀģ",
+ "r on",
+ "åı ¤",
+ "çł Ķç©¶",
+ "ç² ī",
+ "h or",
+ "Ġprof ess",
+ "çļĦ éĹ®é¢ĺ",
+ "Ġopportun ities",
+ "åİĨ åı²",
+ "Ġde f",
+ "ĠA m",
+ "Ġg r",
+ "a ur",
+ "å± Ĥ",
+ "çŃ ĸ",
+ "Ġpop ular",
+ "æ´ ģ",
+ "åıij çݰ",
+ "Ġpo em",
+ "èµ Ľ",
+ "Ġo b",
+ "Ġd on",
+ "Ġs ound",
+ "Ġtransport ation",
+ "i ous",
+ "åı ¦",
+ "Ġro le",
+ "Ġf iel",
+ "ç§ij åѦ",
+ "èĢ ģ",
+ "re en",
+ "æľī æķĪ",
+ "Ġc or",
+ "Ġfeed back",
+ "Ġtechnolo gies",
+ "交 éĢļ",
+ "Ġad apt",
+ "' re",
+ "erv ation",
+ "Ġcommun ities",
+ "çݰ 代",
+ "Ġlo ok",
+ "Ġf ac",
+ "ç͵ å½±",
+ "Ġcol lect",
+ "å¾Ĺ åΰ",
+ "h ips",
+ "Ġav ail",
+ "ere n",
+ "ä¸Ģ èµ·",
+ "çī Ľ",
+ "Ġpos s",
+ "Ġwe ather",
+ "Ġeffort s",
+ "¿ Ģ",
+ "æĹ ħ",
+ "o h",
+ "Ġcol labor",
+ "æĭ ¥",
+ "æĪIJ åĬŁ",
+ "èİ· å¾Ĺ",
+ "å± ħ",
+ "Ġt re",
+ "Ġs ources",
+ "Ġstud y",
+ "Ġprogra ms",
+ "éĻ IJ",
+ "Ġt ips",
+ "Ġmark et",
+ "al ly",
+ "å® ³",
+ "w ards",
+ "æ£ Ģ",
+ "ä¸Ģ ç¯ĩ",
+ "ri or",
+ "Ġto p",
+ "Ġe nd",
+ "å ĭ",
+ "Ġlar ge",
+ "ici ency",
+ "Ġde c",
+ "å®ļ çļĦ",
+ "ic ient",
+ "è¿ĩç¨ĭ ä¸Ń",
+ "lic ations",
+ "ç¼ º",
+ "Ġto ur",
+ "Ġto gether",
+ "人 工",
+ "Ġtool s",
+ "æĸ ¯",
+ "æ° ij",
+ "æĬ Ĭ",
+ "ä¹ĭéĹ´ çļĦ",
+ "çī¹ çĤ¹",
+ "Ġbe l",
+ "ditional ly",
+ "åĪ© ç͍",
+ "è¾ ¹",
+ "éĻ į",
+ "ĠI f",
+ "é¢ Ŀ",
+ "åį ı",
+ "å¾ Ģ",
+ "l ish",
+ "è¯ ī",
+ "in s",
+ "å¥ ¶",
+ "Ġe conom",
+ "Ġinv est",
+ "ĠD o",
+ "t ain",
+ "åĩº çݰ",
+ "çļĦ å½±åĵį",
+ "ater ial",
+ "Ġs ure",
+ "Ġp ass",
+ "çĶ »",
+ "è´ £",
+ "ç»ĵ æŀĦ",
+ "æķ ħ",
+ "æĥħ æĦŁ",
+ "æ ¿Ģ",
+ "ell ig",
+ "ä¼ Ĺ",
+ "æ¯Ķ è¾ĥ",
+ "ter n",
+ "Ġout comes",
+ "u p",
+ "Ġbe aut",
+ "re ad",
+ "çĶŁ æĪIJ",
+ "æķ° åŃĹ",
+ "Ġde m",
+ "i res",
+ "åı¯ä»¥ éĢļè¿ĩ",
+ "æĸ° çļĦ",
+ "Ġde ep",
+ "å ¨",
+ "çĭ Ĺ",
+ "åħ³ 注",
+ "çĶŁ åij½",
+ "ä¼ł 绣",
+ "Ġst ay",
+ "æŃ Į",
+ "åħ³ éĶ®",
+ "Ġpl ace",
+ "主 é¢ĺ",
+ "å¾Ī å¤ļ",
+ "èĪ Ĵ",
+ "Ġprofess ional",
+ "y le",
+ "æĽ ²",
+ "1 9",
+ "Ġess ay",
+ "Ġg ive",
+ "ç³ ĸ",
+ "Ġon ly",
+ "æŁ IJ",
+ "Ġph ys",
+ "对 è¯Ŀ",
+ "Ġcont ro",
+ "Ġam ount",
+ "ce pt",
+ "iz ation",
+ "ç¼ĸ åĨĻ",
+ "åıĹ åΰ",
+ "Ġal ways",
+ "æ¯Ķ å¦Ĥ",
+ "Ġpriv acy",
+ "a u",
+ "____ ____",
+ "Ġrespons ible",
+ "( )",
+ "çŃī çŃī",
+ "Ġm aterial",
+ "Ġon line",
+ "é ¼",
+ "æĶ ¿",
+ "åĽ Ľ",
+ "Ġen joy",
+ "åľ Ł",
+ "Ġsaf ety",
+ "Ġt w",
+ "Ġcommunic ation",
+ "ä¸ ½",
+ "æĺ ¾",
+ "olut ion",
+ "er g",
+ "į ä½ľ",
+ "Ġus er",
+ "Ġemot ional",
+ "t ime",
+ "é ¾",
+ "Ġse curity",
+ "Ġs ense",
+ "el ines",
+ "åĬ ±",
+ "çī© è´¨",
+ "u ra",
+ "Ġsh are",
+ "Ġanalyz ing",
+ "it al",
+ "é ±",
+ "irt ual",
+ "Ġvis it",
+ "b ers",
+ "Ġc our",
+ "Ġpro ble",
+ "设 å¤ĩ",
+ "at ch",
+ "l and",
+ "é± ¼",
+ "æĪij们 éľĢè¦ģ",
+ "ç¨ ³",
+ "ib ility",
+ "Ġeff iciency",
+ "å£ °",
+ "è Ĵ",
+ "æľº åύ",
+ "Ġcle ar",
+ "åζ å®ļ",
+ "iz ing",
+ "Ġcondit ions",
+ "l usion",
+ "Ġlo w",
+ "Ġl im",
+ "her s",
+ "Ġris ks",
+ "ç¿ »",
+ "Ġle t",
+ "åĴ ĸ",
+ "å¿ĥ çIJĨ",
+ "è¿ ľ",
+ "pr int",
+ "Ġchang es",
+ "Ġme as",
+ "Ġimpro ving",
+ "Ġc rit",
+ "5 0",
+ "å¸Į æľĽ",
+ "Ġa ud",
+ "åį Ĺ",
+ "æĹł æ³ķ",
+ "Ġneg ative",
+ "项 缮",
+ "u nd",
+ "at s",
+ "Ġcompan ies",
+ "æī¾ åΰ",
+ "Ġcont ribut",
+ "æŃ£ ç¡®",
+ "é» Ħ",
+ "å± ŀ",
+ "Ġunderstand ing",
+ "Ġm ult",
+ "Ġc lo",
+ "å¾ ģ",
+ "Ġp rior",
+ "r im",
+ "人工 æĻºèĥ½",
+ "Ġvari ety",
+ "Ġt aking",
+ "å Ĥ",
+ "as ter",
+ "od y",
+ "Ġ {",
+ "çļĦ éĩįè¦ģ",
+ "Ġf ore",
+ "èµĦ æºIJ",
+ "è¦ģ æ±Ĥ",
+ "Ġfeat ures",
+ "èį ī",
+ "m e",
+ "èĮ ĥ",
+ "Ġo per",
+ "çº §",
+ "é² ľ",
+ "æĬĢ å·§",
+ "ij æĪĺ",
+ "ç±» åŀĭ",
+ "æĿ ¿",
+ "è½ ¯",
+ "e w",
+ "Ġrest aur",
+ "Ġwith out",
+ "ruct ure",
+ "çļĦ æĺ¯",
+ "ç ı",
+ "Ġl ist",
+ "ur ate",
+ "Ġbo ok",
+ "äº ²",
+ "åº Ĺ",
+ "ä¹Ł æĺ¯",
+ "ä»» ä½ķ",
+ "Ġc am",
+ "ĠB e",
+ "Ġgo vern",
+ "Ġbehavi or",
+ "è®Ń ç»ĥ",
+ "Ġfam ily",
+ "æĿ Ĥ",
+ "Ġc ity",
+ "Ġappro ach",
+ "Ġacc urate",
+ "Ġs om",
+ "Ġe l",
+ "èĪ ŀ",
+ "è ŀ",
+ "åŁº æľ¬",
+ "Ġdis e",
+ "Ġen coura",
+ "ĠW hat",
+ "å ĥ",
+ "è¯ ¦",
+ "¦ Ĥ",
+ "å·¥ åħ·",
+ "åķ ¡",
+ "Ġst ill",
+ "cho ol",
+ "æĦŁ åΰ",
+ "çĶŁ çī©",
+ "åĴĸ åķ¡",
+ "åĩĨ å¤ĩ",
+ "Ġw aste",
+ "Ġev ents",
+ "æķĻ èĤ²",
+ "Ġ 8",
+ "Ġm ust",
+ "i ed",
+ "as ing",
+ "å½¢ æĪIJ",
+ "Ġproduct s",
+ "åħ ¸",
+ "è® ²",
+ "f ter",
+ "å· ®",
+ "l ess",
+ "Ġc ro",
+ "Ġfin an",
+ "åıį åºĶ",
+ "åĪĽ éĢł",
+ "Ġguid elines",
+ "åĪ ¤",
+ "ä½ľ åĵģ",
+ "表 示",
+ "å¼ Ĥ",
+ "Ġknow n",
+ "Ġt est",
+ "è¯ ¯",
+ "o pe",
+ "Ġus ers",
+ "A I",
+ "å¾ ·",
+ "ne w",
+ "è¿ ½",
+ "iqu es",
+ "模 åŀĭ",
+ "åĬĽ åĴĮ",
+ "Ġhist ory",
+ "ĠA l",
+ "æĬķ èµĦ",
+ "å°Ŀ è¯ķ",
+ "an k",
+ "Ġh ome",
+ "éĴ Ł",
+ "ä¸ °",
+ "èĪĴ éĢĤ",
+ "Ġincre ase",
+ "Ġh ab",
+ "åĪ »",
+ "è¾ĵ åĩº",
+ "Ġlead ing",
+ "Ġ 7",
+ "é£İ éĻ©",
+ "Ġperform ance",
+ "Ġha pp",
+ "åŃ £",
+ "Ġst and",
+ "t y",
+ "ç¦ ı",
+ "Ġcustom ers",
+ "åį İ",
+ "Ġbel ie",
+ "Ġcompan y",
+ "å½ ķ",
+ "é£Ł çī©",
+ "ĠU n",
+ "Ġsu mm",
+ "re nt",
+ "ĠC on",
+ "éĢĤ éĩı",
+ "an ced",
+ "Ġ i",
+ "Ġl ight",
+ "Ġanaly sis",
+ "å° Ĭ",
+ "ĠU se",
+ "ou se",
+ "t ed",
+ "Ġchar act",
+ "Ġ #",
+ "t o",
+ "ç» ľ",
+ "ä¸į æĺ¯",
+ "Ġdevelop ing",
+ "åŁ ¹",
+ "Ġstrate gies",
+ "Ġm ight",
+ "çŁ Ń",
+ "çļĦæ İ",
+ "Ġf irst",
+ "èĥ Į",
+ "çĮ «",
+ "Ġinclud es",
+ "åĽ Ń",
+ "Ġdi agn",
+ "Ġgrow th",
+ "ä¸ĵ ä¸ļ",
+ "Ġdo es",
+ "1 2",
+ "ç» ¿",
+ "Ġke ep",
+ "详 ç»Ĩ",
+ "åĥ ı",
+ "åıij çĶŁ",
+ "f act",
+ "åı¯ä»¥ åľ¨",
+ "ç« Ļ",
+ "æĭ ī",
+ "æµ İ",
+ "Ġchat bots",
+ "Ġbre ak",
+ "è¡ ¡",
+ "çŁ ³",
+ "æĮģ ç»Ń",
+ "l ife",
+ "Ġ1 0",
+ "æ´ Ĺ",
+ "ĠAd ditionally",
+ "å£ «",
+ "em ber",
+ "Ġgo als",
+ "å¾ ®",
+ "Ġv iew",
+ "Â ·",
+ "o ve",
+ "åŁº ç¡",
+ "Ġoptim ize",
+ "Ġt em",
+ "Ġd own",
+ "åŁºç¡ Ģ",
+ "è¶ ħ",
+ "er cis",
+ "Ġl ess",
+ "e es",
+ "æĿ ĥ",
+ "Ġke y",
+ "Ġwor ks",
+ "è® ¨",
+ "åı¥ åŃIJ",
+ "Ġro bot",
+ "us s",
+ "åħ¨ çIJĥ",
+ "ç»ı æµİ",
+ "æīį èĥ½",
+ "eg r",
+ "ä»ĸ们 çļĦ",
+ "äº Ķ",
+ "èµ· æĿ¥",
+ "ç ĵ",
+ "Ġfact ors",
+ "Ġcult ural",
+ "æľ ¨",
+ "Ġwork ing",
+ "ä¼ ¼",
+ "èIJ ½",
+ "éĢŁ 度",
+ "ä½ ı",
+ "Ġeffect s",
+ "å© ļ",
+ "b r",
+ "åİ ħ",
+ "ra in",
+ "\" )",
+ "åѦ çĶŁ",
+ "\" ,",
+ "Ġp ar",
+ "at form",
+ "Ġens uring",
+ "çͱ äºİ",
+ "Ġm uch",
+ "Ġwor ds",
+ "Ġm ar",
+ "ç»ı éªĮ",
+ "为 äºĨ",
+ "åIJĪ ä½ľ",
+ "v en",
+ "Ġ /",
+ "Ġfinan cial",
+ "wor k",
+ "or ies",
+ "æ² »",
+ "Ġtechn iques",
+ "æĭ¥ æľī",
+ "ra p",
+ "å° Ķ",
+ "Ġ est",
+ "Ġavail able",
+ "Ġl it",
+ "æ ¹",
+ "Ġeff icient",
+ "el s",
+ "o ver",
+ "Ġl and",
+ "Ġare a",
+ "Ġint ellig",
+ "Ġpre f",
+ "at ure",
+ "çŁ¥ è¯Ĩ",
+ "æĵ įä½ľ",
+ "å¾ ħ",
+ "ig ate",
+ "çļĦæ Ķ",
+ "Ġme an",
+ "b o",
+ "Ġcontro l",
+ "éĩĩ ç͍",
+ "ric ult",
+ "Ġprogra mm",
+ "Ġto wards",
+ "th ing",
+ "ä¸į è¦ģ",
+ "Ġth ough",
+ "å½ ©",
+ "Ġc ertain",
+ "Ġw ild",
+ "ä» Ĭ",
+ "Ġcons ervation",
+ "çŁ¥ éģĵ",
+ "Ġreal ly",
+ "çļĦ åľ°",
+ "i o",
+ "é¥ °",
+ "Ġf ul",
+ "çݯ ä¿Ŀ",
+ "Ġexplo re",
+ "çļĦæ ¸",
+ "Ġdivers e",
+ "åĬł 强",
+ "çļ ®",
+ "Ġemot ions",
+ "Ġav oid",
+ "' ll",
+ "çļĦæ ī",
+ "åį ¡",
+ "Ġpl atform",
+ "an ces",
+ "Ġsit u",
+ "ä» ĺ",
+ "ä½į ç½®",
+ "or ing",
+ "çĽ IJ",
+ "ä¸ ĩ",
+ "Ġde v",
+ "n ov",
+ "as h",
+ "Ġtw o",
+ "å® ł",
+ "b on",
+ "èµ °",
+ "åĪĹ è¡¨",
+ "Ġc y",
+ "èį IJ",
+ "ĠS ome",
+ "Ġexpl ain",
+ "Ġa ware",
+ "社 交",
+ "d ay",
+ "åı Į",
+ "æ² ŁéĢļ",
+ "æ° §",
+ "å¼Ģ åıij",
+ "åħ¬åı¸ çļĦ",
+ "Ġa ir",
+ "åĩ »",
+ "ar ing",
+ "éĥ½ æĺ¯",
+ "Ġlevel s",
+ "od s",
+ "Ġste ps",
+ "Ġc ap",
+ "æ´ ŀ",
+ "é© ¬",
+ "Ġret urn",
+ "Ġm et",
+ "çĶŁ æĢģ",
+ "丰 å¯Į",
+ "æŁ ĵ",
+ "æīĢ ä»¥",
+ "é¡ »",
+ "Ġ er",
+ "Ġf ra",
+ "3 0",
+ "è ĵ",
+ "âĢ Ķ",
+ "Ġ å½ĵ",
+ "a h",
+ "ä¿ ĥ",
+ "Ġlike ly",
+ "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ",
+ "åĪ Ŀ",
+ "Ġcreat ing",
+ "Ġf arm",
+ "Ġb al",
+ "Ġl ives",
+ "å®ĥ çļĦ",
+ "Ġab ility",
+ "ä¸Ĭ çļĦ",
+ "Ġsent ence",
+ "åĤ ¨",
+ "Ġr out",
+ "Ġprovid es",
+ "Ġag ain",
+ "å®ł çī©",
+ "éĢ IJ",
+ "Ġyear s",
+ "èŀ į",
+ "Ġphys ical",
+ "P ython",
+ "ĠE x",
+ "it ing",
+ "è°ĥ æķ´",
+ "ç½ij 绾",
+ "æħ ¢",
+ "空 éĹ´",
+ "åĽ °",
+ "è± Ĩ",
+ "æĽ´å¤ļ çļĦ",
+ "ĠA r",
+ "Ġmain tain",
+ "å®ŀ éĻħ",
+ "Ġtra vel",
+ "Ġs at",
+ "p ro",
+ "ç͵ åŃIJ",
+ "æ± ½",
+ "e x",
+ "åģ ĩ",
+ "æIJ Ń",
+ "éļı çĿĢ",
+ "è¿ĺ æľī",
+ "ç¤ ¼",
+ "al e",
+ "Ġcons um",
+ "Ċ Ġ",
+ "n cy",
+ "Ġquest ions",
+ "f ort",
+ "m aking",
+ "Ġdes c",
+ "1 5",
+ "Ġinvol ves",
+ "Ġst ress",
+ "åŃŠ符",
+ "he re",
+ "Ġimpact s",
+ "Ġex ercis",
+ "åĿ ļ",
+ "led ge",
+ "ç§ij æĬĢ",
+ "oc i",
+ "Ġeffective ly",
+ "æ¶Ī è´¹",
+ "Ġconc lusion",
+ "éĺ ħ",
+ "Ġst re",
+ "iss ions",
+ "æ· »",
+ "I t",
+ "éĿ Ļ",
+ "Ġv irtual",
+ "è¡ £",
+ "Ġachie ve",
+ "our ce",
+ "è¿ ŀ",
+ "ac ks",
+ "表 æł¼",
+ "Ġimport ance",
+ "èĩª æĪij",
+ "The se",
+ "n um",
+ "çļĦæ ł",
+ "Ġrelations hips",
+ "Ġwork ers",
+ "g ical",
+ "or por",
+ "ers on",
+ "åij ¢",
+ "nd s",
+ "æİ¨ èįIJ",
+ "oh n",
+ "å¿ħ é¡»",
+ "容 æĺĵ",
+ "ĠG o",
+ "Ġt ell",
+ "ĠR es",
+ "on om",
+ "Ġbe c",
+ "æ³ Ľ",
+ "p os",
+ "Ġmo ve",
+ "Ġst ory",
+ "æŃ ¢",
+ "Ġprior it",
+ "Ġindust ries",
+ "è ľ",
+ "Ġposs ible",
+ "ĠM an",
+ "Ġexp ress",
+ "ab ilities",
+ "Ġint egr",
+ "代 表",
+ "Ġrespon d",
+ "åĪĨ éĴŁ",
+ "æľº ä¼ļ",
+ "Ġth ings",
+ "交 æµģ",
+ "Ġm eth",
+ "ur ther",
+ "Ġw ide",
+ "èij Ĺ",
+ "æĪij çļĦ",
+ "ĸçķ ¥",
+ "id es",
+ "eth ing",
+ "ĠWh ile",
+ "p an",
+ "çŃ ĸçķ¥",
+ "Ġc ent",
+ "Ġp lease",
+ "olo gy",
+ "ura cy",
+ "å¾ ª",
+ "w ard",
+ "n ce",
+ "Ġthe n",
+ "çª ģ",
+ "å¥ ĩ",
+ "Ġb lo",
+ "a i",
+ "æŀ Ĺ",
+ "ç®Ĺ æ³ķ",
+ "ç» ¼",
+ "Ġpr int",
+ "ac es",
+ "l u",
+ "ª æĸ½",
+ "p re",
+ "çļĦæĦ ı",
+ "Ġs ol",
+ "Ġover all",
+ "h old",
+ "Ġ es",
+ "çļĦ ä¸Ģ",
+ "éģ ĩ",
+ "Ġpop ul",
+ "å°ı 说",
+ "æ³ ¢",
+ "åį ģ",
+ "ä¹Ł åı¯ä»¥",
+ "é£Ł åĵģ",
+ "Ġcont ent",
+ "å° Ħ",
+ "Ġrequ ires",
+ "æ£Ģ æŁ¥",
+ "ĊĠĠĠĠĠĠĠĠ ĠĠĠ",
+ "Ġgrou ps",
+ "Ġf air",
+ "Ġb l",
+ "å®ŀ éªĮ",
+ "æĮī çħ§",
+ "os p",
+ "st r",
+ "ä¸į èĥ½",
+ "Ġh arm",
+ "Ġpro du",
+ "çļĦæĬ Ģ",
+ "ç ĩ",
+ "t le",
+ "Ġanim als",
+ "è§Ĵ èī²",
+ "le v",
+ "æ¸ IJ",
+ "å¤į æĿĤ",
+ "Ġde pend",
+ "æĮ ijæĪĺ",
+ "åĮħ åIJ«",
+ "Ġhelp s",
+ "Ġop en",
+ "Ġn et",
+ "ĠĠĠĠ Ġ",
+ "Ġstr ong",
+ "Ġj our",
+ "广 æ³Ľ",
+ "æķ´ 个",
+ "Ġe lect",
+ "Ġrespon se",
+ "åįķ è¯į",
+ "æľ ĭ",
+ "Ġ <",
+ "åĮĸ åѦ",
+ "éĴ Ī",
+ "Ġqu ick",
+ "ual ly",
+ "Ġsom ething",
+ "Ġtra ck",
+ "度 åĴĮ",
+ "eren ces",
+ "æł ij",
+ "Ġacc uracy",
+ "Ġex c",
+ "é£ ŀ",
+ "Ġfiel d",
+ "寻 æī¾",
+ "éħ ¸",
+ "Ġh ope",
+ "ç ij",
+ "Ġin nov",
+ "ç» ª",
+ "al k",
+ "Ġtyp es",
+ "Ġd id",
+ "åĬ ª",
+ "Ġc all",
+ "è¯ Ĺ",
+ "Ġear ly",
+ "ĠO ne",
+ "a pp",
+ "Ġcomm on",
+ "æľĢ ç»Ī",
+ "Ġche ck",
+ "Ġs ym",
+ "çĤ Ĵ",
+ "æĬĢ èĥ½",
+ "Ġen h",
+ "Ġag ricult",
+ "Ġim m",
+ "ç» ĩ",
+ "满 足",
+ "Ġs chool",
+ "b al",
+ "Ġfollow ing",
+ "b ased",
+ "Ġwe bs",
+ "Ġcult ure",
+ "ĠC om",
+ "w ay",
+ "ä¸Ģ å®ļ",
+ "åķĨ åĵģ",
+ "ud e",
+ "çļĦ åıijå±ķ",
+ "çĶŁ 产",
+ "os ystem",
+ "Ġpl ant",
+ "åı ¶",
+ "åIJ ĥ",
+ "ä»ĸ çļĦ",
+ "d er",
+ "è¯ ¢",
+ "å®¶ åħ·",
+ "Ġf ree",
+ "ç§ »",
+ "æİ Į",
+ "Ġb ody",
+ "Ġp resent",
+ "Ġpartic ularly",
+ "Ġchild ren",
+ "Ġstud ent",
+ ") .",
+ "çī¹ å¾ģ",
+ "è Ķ",
+ "éĺħ 读",
+ "æķĪ çİĩ",
+ "Ġprogra m",
+ "éħ ±",
+ "åıĺ å¾Ĺ",
+ "i x",
+ "Ġcom e",
+ "çļĦæ ²",
+ "ĠT e",
+ "ĠT o",
+ "åħ± åIJĮ",
+ "Ġemploy ees",
+ "说 æĺİ",
+ "Ġhe art",
+ "Ġm ot",
+ "æľĭ åıĭ",
+ "er ic",
+ "è¯ ij",
+ "Ġcur rent",
+ "æĪIJ æľ¬",
+ "Ġto o",
+ "çİ© å®¶",
+ "åĪĽ æĸ°",
+ "Ġec osystem",
+ "常 è§ģ",
+ "ä¸Ģ æŃ¥",
+ "Ġp res",
+ "Ġmult i",
+ "åijĬ è¯ī",
+ "ä¸ ¥",
+ "Ġm it",
+ "Ġact ion",
+ "çĨ Ł",
+ "Ġhab it",
+ "åı£ æĦŁ",
+ "ç® ±",
+ "Ġus es",
+ "å¢ŀ 强",
+ "ç»Ļ åĩº",
+ "Ġ 9",
+ "Ġde p",
+ "Ġeconom ic",
+ "æĢ§ çļĦ",
+ "1 8",
+ "åĨ °",
+ "Ġhelp ed",
+ "åIJ¸ å¼ķ",
+ "çİ ĭ",
+ "Ġdiagn os",
+ "å ł",
+ "èģĶ ç³»",
+ "ç¾ ¤",
+ "ç»ĥ ä¹ł",
+ "æĪIJ éķ¿",
+ "Ġpo int",
+ "å®ļ æľŁ",
+ "åij ¼",
+ "èį ¯",
+ "æĿ ¯",
+ "æ¤ Ĵ",
+ "æķĪ æŀľ",
+ "Ġspec ial",
+ "æ· ·",
+ "åĩł 个",
+ "aus e",
+ "é Ĩ",
+ "æ¯Ķ èµĽ",
+ "è· Ŀ",
+ "W hat",
+ "Ġt imes",
+ "ic les",
+ "Ġ *",
+ "ç´ §",
+ "å¦Ĥæŀľ ä½ł",
+ "çĭ¬ çī¹",
+ "çģ µ",
+ "ç¨ İ",
+ "Ġcar bon",
+ "Ġbi as",
+ "åĬ© äºİ",
+ "Ġcon st",
+ "èĩª çͱ",
+ "æĿ¥ 说",
+ "å°± æĺ¯",
+ "åį °",
+ "Ġme et",
+ "è§Ħ åĪĴ",
+ "çļĦç ¾",
+ "èIJ¥ åħ»",
+ "at ors",
+ "稳 å®ļ",
+ "od e",
+ "çħ ®",
+ "Ġass oci",
+ "å¿ Ĺ",
+ "è¡Į æĺŁ",
+ "æĿ İ",
+ "Ġrev iew",
+ "åĩ Ģ",
+ "ĠR o",
+ "Ġknow ledge",
+ "以 便",
+ "æµĭ è¯ķ",
+ "åIJĪ éĢĤ",
+ "s c",
+ "å½¢ å¼ı",
+ "Ġfriend s",
+ "Ġnat ure",
+ "Ġcrit ical",
+ "æ´ ĭ",
+ "Ġa fter",
+ "er ve",
+ "Ġre ce",
+ "çļĦæ Ń",
+ "汽 车",
+ "çķ Į",
+ "Ġlo ss",
+ "Ġapp lications",
+ "å¤ļ ç§į",
+ "éĶ ħ",
+ "ä¸ ²",
+ "Ġins p",
+ "-- -",
+ "ĠS h",
+ "Ġv ol",
+ "l ut",
+ "o ks",
+ "se qu",
+ "Ġb ir",
+ "åIJĪ çIJĨ",
+ "Ġne cess",
+ "æĪij æĥ³",
+ "çŃī æĸ¹éĿ¢",
+ "é¼ ĵ",
+ "Ġso ft",
+ "Ġl ive",
+ "å°ı æĺİ",
+ "ĠI nd",
+ "Ġbr ing",
+ "æĺ¯ æĮĩ",
+ "Ġso il",
+ "il ar",
+ "ä¸ ľ",
+ "æĿ¡ ä»¶",
+ "Ġt ri",
+ "äº ®",
+ "Ġm om",
+ "æı ¡",
+ "ä¼ °",
+ "ŀ äºī",
+ "çĽ ij",
+ "èĤ ¤",
+ "è´¢ åĬ¡",
+ "æ·» åĬł",
+ "饮 é£Ł",
+ "Ġallow ing",
+ "åº ķ",
+ "Ġr ight",
+ "Ġexp ert",
+ "Ġsu pp",
+ "Ġin it",
+ "çļĦæ µ",
+ "ar get",
+ "Ġexp ect",
+ "Ġ1 9",
+ "Ġmeas ures",
+ "olut ions",
+ "j ust",
+ "ar c",
+ "å° ļ",
+ "Ġpract ice",
+ "æľī åĬ©äºİ",
+ "大 éĩı",
+ "' ,",
+ "im ent",
+ "Ġcontin ue",
+ "Ġdisc uss",
+ "1 00",
+ "éļ ľ",
+ "çļĦæĦ Ł",
+ "Ġref lect",
+ "it ation",
+ "åį «",
+ "äºĨ ä¸Ģ",
+ "ne y",
+ "ĠL e",
+ "is ed",
+ "è¶ ĭ",
+ "äºĨ ä¸Ģ个",
+ "Ġincre asing",
+ "çļĦæ Į",
+ "Ġst ru",
+ "æĢ» ç»ĵ",
+ "e ly",
+ "å® ĩ",
+ "Ġaut hor",
+ "表 éĿ¢",
+ "Ġ x",
+ "æķħ äºĭ",
+ "em ic",
+ "Ġrep resent",
+ "g er",
+ "Ġincre ased",
+ "on es",
+ "ain s",
+ "Ġtrain ed",
+ "Ġf ish",
+ "Ġst ate",
+ "åĨ ·",
+ "çĶŁ éķ¿",
+ "Ġre new",
+ "ord ing",
+ "åĮ Ĺ",
+ "æİ ªæĸ½",
+ "å¹³ è¡¡",
+ "Ġsuccess ful",
+ "ä¸ĭ éĿ¢",
+ "Ġactiv ity",
+ "èĮ ¶",
+ "éĢĤ åºĶ",
+ "èĦ ij",
+ "æİ¢ ç´¢",
+ "ff ic",
+ "ç»Ħ æĪIJ",
+ "at ives",
+ "äº ļ",
+ "Ġsc en",
+ "æ² Ļ",
+ "g ress",
+ "使 å¾Ĺ",
+ "æī ¿",
+ "Ġdisc rim",
+ "Ġassist ants",
+ "Ġex ist",
+ "çķ Ļ",
+ "Ġsp ace",
+ "æľĢ è¿ij",
+ "Ġide as",
+ "éĩĩ åıĸ",
+ "l ight",
+ "注 éĩį",
+ "çļĦæĹ¶ éĹ´",
+ "è¿ İ",
+ "Ġcom b",
+ "éĢĤ å½ĵ",
+ "Ġyour self",
+ "rit e",
+ "as on",
+ "åĮ Ģ",
+ "åı¯ä»¥ 使ç͍",
+ "åħħ 满",
+ "Ġval ues",
+ "æ ½",
+ "Ġbi ases",
+ "ä¿ĥ è¿Ľ",
+ "åľº æĻ¯",
+ "ro ss",
+ "åį³ åı¯",
+ "Ġc ru",
+ "Ġnum ber",
+ "Ġty pe",
+ "r ast",
+ "åĩĨ ç¡®",
+ "Th is",
+ "Ġp ast",
+ "çģ ¯",
+ "å®ļ ä¹ī",
+ "Ġs olutions",
+ "Ġt er",
+ "ä¿Ŀ è¯ģ",
+ "èĶ ¬",
+ "å¹ ¸",
+ "åī §",
+ "åħ´ è¶£",
+ "å ª",
+ "ent ion",
+ "av or",
+ "Ġsc ient",
+ "åĬª åĬĽ",
+ "Ġprovid ers",
+ "Ġpolic ies",
+ "al u",
+ "ĠI m",
+ "Ġallow s",
+ "Ġintellig ence",
+ "çļĦ æĸ¹æ³ķ",
+ "è¿Ļ æĺ¯",
+ "Ġ `",
+ "Ġem issions",
+ "Ġ å°Ĩ",
+ "Ġmean ing",
+ "Ġst yle",
+ "åİŁ åĽł",
+ "Ġstru gg",
+ "çļĦç¾ İ",
+ "if ul",
+ "dit ion",
+ "éĥ½ æľī",
+ "空 æ°Ķ",
+ "å®ĥ们 çļĦ",
+ "ä¼ĺ åĮĸ",
+ "Ġinf lu",
+ "åŁº äºİ",
+ "Ġdetail s",
+ "Ġtranspare ncy",
+ "Ġm ess",
+ "ĠC l",
+ "Ġg ame",
+ "p ri",
+ "è¶ĭ åĬ¿",
+ "å½ Ĵ",
+ "ç¿» è¯ij",
+ "æķ £",
+ "B y",
+ "é Ń",
+ "ĠAm eric",
+ "Ġproduct ion",
+ "Ġinc orpor",
+ "æĻ ļ",
+ "Ġinvol ve",
+ "Ġh ot",
+ "æĻ ®",
+ "b y",
+ "Ġf low",
+ "Ġem erg",
+ "åº §",
+ "Ġide a",
+ "åİĭ åĬĽ",
+ "éĿ Ĵ",
+ "om s",
+ "èģĮ ä¸ļ",
+ "Ġre port",
+ "Ġp ap",
+ "Ġthe rap",
+ "Ġs al",
+ "åıĤ ä¸İ",
+ "æĸĩ åѦ",
+ "æIJŃ éħį",
+ "o ot",
+ ") ,",
+ "Ġc r",
+ "Ġprocess es",
+ "g in",
+ "å¹³ åı°",
+ "å¯ Ł",
+ "Ġpromot ing",
+ "æļ ĸ",
+ "ake hold",
+ "ç» §",
+ "iv er",
+ "æ ¦Ĥ",
+ "Ġmodel s",
+ "Ġd ra",
+ "è ĸ",
+ "Ġgrou p",
+ "è¶³ å¤Ł",
+ "Ġg reen",
+ "Ġhealth y",
+ "Ġcom fort",
+ "Ġad ditional",
+ "ä¸Ģ 次",
+ "é¤IJ åİħ",
+ "Ġmaterial s",
+ "Ġman age",
+ "çļĦæ ¯",
+ "ä¼ ¤",
+ "åıĬ æĹ¶",
+ "Ġg lo",
+ "Ġst at",
+ "å¿« éĢŁ",
+ "Ġmonitor ing",
+ "ail y",
+ "ra nd",
+ "o ice",
+ "res h",
+ "ç»Ħ ç»ĩ",
+ "Ġund er",
+ "Ġnecess ary",
+ "Ġhelp ful",
+ "ĠC ol",
+ "é»ij æ´ŀ",
+ "åģļ åĩº",
+ "Ġcour se",
+ "Ġm at",
+ "Ġle g",
+ "Ġf ace",
+ "ä» ¤",
+ "èī¯ å¥½çļĦ",
+ "oc k",
+ "åĮ» çĸĹ",
+ "çĽ ĸ",
+ "id ence",
+ "Ġassoci ated",
+ "Ġpro gress",
+ "åľ Ĩ",
+ "Ġevery one",
+ "ç¼ ĵ",
+ "ĠEn g",
+ "w ord",
+ "èĵ Ŀ",
+ "天 æ°Ķ",
+ "Ġact ions",
+ "em s",
+ "ĠP l",
+ "å® Ļ",
+ "us h",
+ "é¡ ¾",
+ "Ġcost s",
+ "at or",
+ "ç© ¿",
+ "Ġamount s",
+ "èͬ èıľ",
+ ". .",
+ "Ġman ner",
+ "Ġcon sequ",
+ "æ°Ķ åĢĻ",
+ "Ġins ights",
+ "be ing",
+ "at ory",
+ "en er",
+ "le x",
+ "Ġme ans",
+ "Ġcollabor ation",
+ "Ġpers pect",
+ "or m",
+ "pri ate",
+ "å°Ĭ éĩį",
+ "Ġt arget",
+ "è®° å½ķ",
+ "åĢ Ĵ",
+ "Ġrenew able",
+ "æĦ ¿",
+ "èĥ½ æºIJ",
+ "Ġin put",
+ "å®ĩ å®Ļ",
+ "a pe",
+ "Ġad just",
+ "er ies",
+ "Ġd ire",
+ "ä¾ Ŀ",
+ "ust r",
+ "f ect",
+ "Ġbeaut iful",
+ "Ġd ue",
+ "re ci",
+ "çĮ ®",
+ "èĥĮ æĻ¯",
+ "èĤ ¡",
+ "Ġd am",
+ "i k",
+ "Ġadv anced",
+ "缸 对",
+ "åIJį ç§°",
+ "Ġsh ort",
+ "Ġob ject",
+ "è¿Ļ éĩĮ",
+ "éĢł æĪIJ",
+ "èIJ¥ éĶĢ",
+ "çļĦæĥħ æĦŁ",
+ "ç¥ ¨",
+ "Ġcount ries",
+ "in ing",
+ "ist ic",
+ "Ġpl ans",
+ "è´£ ä»»",
+ "Ġst akehold",
+ "t he",
+ "Ġass ess",
+ "æĢĿ èĢĥ",
+ "e ch",
+ "æĪIJ åijĺ",
+ "2 1",
+ "Ġd aily",
+ "Ġcomp ut",
+ "çļĦæĥħ åĨµ",
+ "æıIJ åĩº",
+ "Ġ âĢľ",
+ "åª Ĵ",
+ "ä¸Ń å¿ĥ",
+ "is hed",
+ "ĠS e",
+ "onom ous",
+ "er n",
+ "ç»´ æĬ¤",
+ "am es",
+ "Ġpriorit ize",
+ "çº ¸",
+ "èĤ ¥",
+ "Ġtem per",
+ "æ¸ħ æ´ģ",
+ "us e",
+ "æ± ¡",
+ "Ġmin im",
+ "æĺ¯ åľ¨",
+ "大 å°ı",
+ "åĵª äºĽ",
+ "Ġapp reci",
+ "ren g",
+ "Ġregul ations",
+ "Ġ Z",
+ "éĶĻ è¯¯",
+ "r ans",
+ "èĢĮ ä¸Ķ",
+ "èĪ ¬",
+ "èij ±",
+ "è Ĩ",
+ "æ°´ å¹³",
+ "è´Ń çī©",
+ "åŃĹ符 串",
+ "对 æĸ¹",
+ "Ġh im",
+ "Ġconsequ ences",
+ "å· ´",
+ "é¼ĵ åĬ±",
+ "Ġf il",
+ "人 åijĺ",
+ "è·Ŀ 离",
+ "ĠW hen",
+ "çļĦæ° ´",
+ "çī© çIJĨ",
+ "åIJĮæĹ¶ ä¹Ł",
+ "åľ¨ è¿Ļ个",
+ "åħ¶ 次",
+ ", \"",
+ "æ¶ ²",
+ "çĶ ·",
+ "iv al",
+ "åı¯ä»¥ 让",
+ "æĥ ¯",
+ "Ġadv ance",
+ "Ġve h",
+ "å¦Ĥæŀľ æĤ¨",
+ "Ġest ab",
+ "ri pt",
+ "ç« ¯",
+ "ä¸į ä¼ļ",
+ "Ġtranspare nt",
+ "æķ° éĩı",
+ "çĽ ĺ",
+ "Ġspe ak",
+ "Ġp ark",
+ "Ġstakehold ers",
+ "é º",
+ "Ġev ent",
+ "çļĦæķ° æį®",
+ "èĩª åĬ¨",
+ "ç»Ĩ èĬĤ",
+ "è¯Ħ ä¼°",
+ "æ¶ ¦",
+ "Ġpref erences",
+ "Ġve get",
+ "æį Ł",
+ "e qu",
+ "Ġg l",
+ "Ġp ain",
+ "o gra",
+ "Ġtra ffic",
+ "Ġo ce",
+ "ä¹ ĺ",
+ "e xt",
+ "âĢĿ ï¼Į",
+ "Ġan other",
+ "å¤ļ å°ij",
+ "Ġagain st",
+ "ç»ı åİĨ",
+ "计ç®Ĺ æľº",
+ "èĢ IJ",
+ "软 件",
+ "ĠP re",
+ "Ġpl ants",
+ "缸 äºĴ",
+ "é¢ ij",
+ "\\ _",
+ "Ġs ame",
+ "ru g",
+ "Ġval u",
+ "Ġo cc",
+ "çļĦç ¤",
+ "Ġsustain ability",
+ "ĠS he",
+ "d e",
+ "ot e",
+ "Ġd ig",
+ "N A",
+ "Ġcru cial",
+ "æī §",
+ "å± Ģ",
+ "æĭ Ł",
+ "æĭ Į",
+ "Ġn on",
+ "Ġeng aging",
+ "Ġinter n",
+ "L P",
+ "温 度",
+ "æł ¸",
+ "æĬ¥ åijĬ",
+ "æĿ¥ è¶Ĭ",
+ "h ood",
+ "ä¸ī 个",
+ "å¦Ĥ ä¸ĭ",
+ "çī© ä½ĵ",
+ "for ce",
+ "Ġneed ed",
+ "Ġim ages",
+ "Ġbuild ing",
+ "ici ous",
+ "Ġ æĪij",
+ "è¶Ĭ æĿ¥è¶Ĭ",
+ "æĶ¾ åħ¥",
+ "g o",
+ "éĻį ä½İ",
+ "å½ĵ åľ°",
+ "æ¶Īè´¹ èĢħ",
+ "ç £",
+ "ivers ity",
+ "é¢Ħ ç®Ĺ",
+ "ic le",
+ "æ·· åIJĪ",
+ "Ġpartic ip",
+ "Ġdis hes",
+ "Ġthrough out",
+ "Ġwith in",
+ "åı ³",
+ "é«ĺ çļĦ",
+ "Ġph ot",
+ "Ġtr ust",
+ "æĦı è¯Ĩ",
+ "以 ç¡®ä¿Ŀ",
+ "çĬ¶ æĢģ",
+ "Ġautom ation",
+ "1 1",
+ "Ġpo st",
+ "æīĭ æľº",
+ "wor ks",
+ "éĢ ı",
+ "åº ĵ",
+ "Ġw ind",
+ "Ġ= =",
+ "Ġprocess ing",
+ "èĮĥ åĽ´",
+ "æĦı ä¹ī",
+ "追 æ±Ĥ",
+ "Ã ©",
+ "å¾ Ħ",
+ "éĿ ł",
+ "ä¸ ĸ",
+ "èĻ ½",
+ "ç« ŀäºī",
+ "Ġappro priate",
+ "æĽ´ 好çļĦ",
+ "Ġcharact er",
+ "c l",
+ "ç§ ĺ",
+ "it ude",
+ "Ġte ac",
+ "le ep",
+ "ĠDe velop",
+ "in ce",
+ "å· ¦",
+ "g round",
+ "è¡Į ä¸ļ",
+ "éĴΠ坹",
+ "å¿ħ è¦ģ",
+ "Ġdet erm",
+ "-------- --------",
+ "Ġst reng",
+ "d o",
+ "Ġchalleng ing",
+ "or k",
+ "Ġan x",
+ "èī² çļĦ",
+ "Ġh ard",
+ "æĺİ ç¡®",
+ "åĪĨ 享",
+ "æĶ¹ åıĺ",
+ "ä½ ³",
+ "åıª æľī",
+ "å±ķ 示",
+ "Ġcam p",
+ "çº ³",
+ "a j",
+ "et ic",
+ "u ment",
+ "ä½ł åı¯ä»¥",
+ "Ġpol lut",
+ "Ġh ig",
+ "pp ing",
+ "e ad",
+ "çĦ¶ èĢĮ",
+ "第 äºĮ",
+ "é¸ Ł",
+ "çī© åĵģ",
+ "ä¸ ¾",
+ "Ġencoura ge",
+ "pe cial",
+ "Ġac ross",
+ "el ves",
+ "äºĭ ä»¶",
+ "c le",
+ "æ ©",
+ "åªĴ ä½ĵ",
+ "n ers",
+ "Ġc al",
+ "èϽ çĦ¶",
+ "åĽ º",
+ "ä¹ł æĥ¯",
+ "Ġsaf e",
+ "èĥ½ éĩı",
+ "ist ics",
+ "ä¹ĭ åīį",
+ "Ġiss ue",
+ "å¤ļ 个",
+ "åĨ³ çŃĸ",
+ "è¾¾ åΰ",
+ "æĹ ©",
+ "ä¸į åı¯",
+ "ä¸Ģ 缴",
+ "å· ¨",
+ "æĦŁ è°¢",
+ "ĠN ew",
+ "ä¸Ģ 段",
+ "Ġmach ines",
+ "å°Ĩ åħ¶",
+ "ç»§ ç»Ń",
+ "Ġwor d",
+ "çī¹ åĪ«",
+ "Ġagricult ure",
+ "æĢ İ",
+ "éĢIJ æ¸IJ",
+ "éĵ ¾",
+ "è¯ ¾",
+ "Ġk ind",
+ "å¢ Ļ",
+ "è°¢ è°¢",
+ "Ġalgorith m",
+ "è£ħ 饰",
+ "Ġal ong",
+ "Ġeas y",
+ "äº ij",
+ "è§£åĨ³ æĸ¹æ¡Ī",
+ "Ġaware ness",
+ "' ve",
+ "æĸ¹ åIJij",
+ "Ġne ver",
+ "Ġquick ly",
+ "Ġres pect",
+ "çļĦæ Ļ",
+ "Ġam ong",
+ "Ġaccount ability",
+ "Ġl aw",
+ "en ing",
+ "Ġdef in",
+ "Ġsur round",
+ "éĵ ģ",
+ "Ġpower ful",
+ "A n",
+ "Ġcaus e",
+ "æ ¥",
+ "æİĮ æı¡",
+ "è¿ĺ æĺ¯",
+ "Ġcreat ive",
+ "è¡ Ģ",
+ "Ġloc ated",
+ "un ning",
+ "åľ° åĮº",
+ "éĿ¢ 积",
+ "éĽ ¨",
+ "Ġne ar",
+ "Ġinit i",
+ "ress ion",
+ "ä¸ĭ æĿ¥",
+ "2 5",
+ "é© ¶",
+ "¾ çĹħ",
+ "ab les",
+ "æľī è¶£",
+ "循 çݯ",
+ "çŃĶ æ¡Ī",
+ "çł ´",
+ "ic ation",
+ "éĻ ¢",
+ "æ²» çĸĹ",
+ "Ġad dition",
+ "äºĭ æĥħ",
+ "Ġbec ause",
+ "åı Ī",
+ "èĤ Į",
+ "çº ª",
+ "s ide",
+ "æĭ ħ",
+ "æ¹ ¿",
+ "åį Ĭ",
+ "é¡ º",
+ "ĠA nd",
+ "Ġrestaur ant",
+ "Ġv ide",
+ "Ġproble m",
+ "az ing",
+ "Ġmem bers",
+ "Ġn ut",
+ "Ġc ou",
+ "æµ ª",
+ "Ġ è¿Ļ",
+ "Ġhelp ing",
+ "ĠI s",
+ "æıIJ åįĩ",
+ "ĠĠĠĠ ĠĠ",
+ "Ġsh o",
+ "Ġre lev",
+ "Ġar g",
+ "Ġbal ance",
+ "ill ed",
+ "æĺ¯ ä»Ģä¹Ī",
+ "åĬĽ éĩı",
+ "ire d",
+ "å¤ ľ",
+ "åı¯ æĮģç»Ń",
+ "Ġper fect",
+ "* *",
+ "ific ation",
+ "æ¶ ī",
+ "Ġwild life",
+ "an e",
+ "Ġrel ated",
+ "室 åĨħ",
+ "åº ľ",
+ "享 åıĹ",
+ "our s",
+ "è· ij",
+ "åķĨ ä¸ļ",
+ "ach ing",
+ "Ġsu n",
+ "Ġrecogn ition",
+ "el t",
+ "Ġor der",
+ "å¹³ åĿĩ",
+ "g ing",
+ "ä¸ ´",
+ "çĤ ¼",
+ "Ġgo ing",
+ "åij¼ åIJ¸",
+ "Ġsoft ware",
+ "Ġre mot",
+ "èijĹ åIJį",
+ "幸 ç¦ı",
+ "Ġenh ance",
+ "èĻ ļ",
+ "Ġn ow",
+ "Ġth reat",
+ "Ġd est",
+ "åĿĩ åĮĢ",
+ "Ġac ad",
+ "åºĶ 对",
+ "çľĭ åΰ",
+ "c ast",
+ "è¾ Ĩ",
+ "ific ial",
+ "Ġ very",
+ "o ok",
+ "åĮº åŁŁ",
+ "¹ ģ",
+ "æĪ¿ éĹ´",
+ "æıIJä¾Ľ äºĨ",
+ "Ġmot iv",
+ "Ġaccess ible",
+ "åĨ³ å®ļ",
+ "Ġh y",
+ "å® Ī",
+ "Ġf lo",
+ "u g",
+ "Ġinform ed",
+ "åĵģ è´¨",
+ "çļĦç Ł",
+ "av es",
+ "ar r",
+ "ĠW ith",
+ "le t",
+ "è§Ĥ çĤ¹",
+ "en ge",
+ "è¡Į åĬ¨",
+ "f riend",
+ "ç³ ķ",
+ "Ġf urther",
+ "ĠE ns",
+ "ç§ ģ",
+ "Ġad o",
+ "Ġcle an",
+ "缸 åºĶ",
+ "Ġf re",
+ "pecial ly",
+ "è Ĺ",
+ "Ġc apt",
+ "çļĦç ľ",
+ "Ġsome one",
+ "Ġc ell",
+ "æĶ¾ åľ¨",
+ "欢 è¿İ",
+ "Ġ âĢ",
+ "Ġdev ices",
+ "çļĦ æĸ¹å¼ı",
+ "Ġjob s",
+ "au gh",
+ "n ot",
+ "æľī äºĽ",
+ "åħ¬ åħ±",
+ "g est",
+ "çļĦ çĶŁæ´»",
+ "çľ ¼",
+ "çļĦ ä¿¡æģ¯",
+ "ĠC ons",
+ "æİĴ åºı",
+ "Ġbenef it",
+ "re ct",
+ "å¤ ı",
+ "un te",
+ "符 åIJĪ",
+ "ä¸Ģ ä½į",
+ "åĨħ éĥ¨",
+ "Ġlook ing",
+ "d ing",
+ "æĬ ĺ",
+ "è¾ ij",
+ "è¿Ļ个 éĹ®é¢ĺ",
+ "Ġes pecially",
+ "çľ ł",
+ "âĢĿ ãĢĤ",
+ "å¥ ı",
+ "ra y",
+ "è¿ĺ åı¯ä»¥",
+ "åĪĽ ä½ľ",
+ "com ing",
+ "Ġmulti ple",
+ "éļ IJ",
+ "æ³ ¡",
+ "æłĩ åĩĨ",
+ "Ġm il",
+ "éľĢè¦ģ 注æĦı",
+ "Ġanx iety",
+ "æĶ¹ è¿Ľ",
+ "å± ĭ",
+ "污 æŁĵ",
+ "ç¼ĸ ç¨ĭ",
+ "è´¹ ç͍",
+ "Ġev alu",
+ "imate ly",
+ "Ġlit er",
+ "ogra ph",
+ "Ġse arch",
+ "1 6",
+ "en ced",
+ "Ġmeth ods",
+ "çĥ Ī",
+ "模 å¼ı",
+ "çĬ¶ åĨµ",
+ "æĶ¹ åĸĦ",
+ "å¤ļ æł·",
+ "c er",
+ "å¥ ĸ",
+ "Ġsat is",
+ "Ġwebs ite",
+ "åĬ ŀ",
+ "åģ¥ èº«",
+ "Ġglo bal",
+ "Ġas k",
+ "Ġplatform s",
+ "Ġdise ases",
+ "çݰ 象",
+ "t ics",
+ "æ± ģ",
+ "åΤ æĸŃ",
+ "Ġcon vers",
+ "Ġrelations hip",
+ "设 置",
+ "æ³ķ å¾ĭ",
+ "Ġmind ful",
+ "é¢Ħ æµĭ",
+ "o very",
+ "åģ ľ",
+ "ç͵ è§Ĩ",
+ "è§Ħ åĪĻ",
+ "ak en",
+ "Ġimplement ing",
+ "is ing",
+ "åıĤ åĬł",
+ "æĥħ 绪",
+ "Ġprovid ed",
+ "æ·± åħ¥",
+ "Ġprogramm ed",
+ "Ġrelev ant",
+ "çļĦç ĥ",
+ "çĸ ¾çĹħ",
+ "åĮ» çĶŁ",
+ "åĪĽ 建",
+ "Ġgener ate",
+ "æĶ¶ åħ¥",
+ "ä¼ ij",
+ "iz es",
+ "Ġtrans form",
+ "éģ µ",
+ "ast ic",
+ "åij Ī",
+ "æ¯ı 个人",
+ "è¿ Ķ",
+ "i et",
+ "Ġv oice",
+ "éĢ Ķ",
+ "æĶ¾ æĿ¾",
+ "åį ´",
+ "èĥ ľ",
+ "Ġst ructure",
+ "æĹ¶ å°ļ",
+ "Ġ Q",
+ "Ġel se",
+ "du c",
+ "Ġem p",
+ "èģ ļ",
+ "è´ §",
+ "ac hes",
+ "ç§ Ģ",
+ "an ks",
+ "Ġn ight",
+ "Ġprofessional s",
+ "Ġb as",
+ "è´ µ",
+ "e c",
+ "Ġdivers ity",
+ "it es",
+ "d r",
+ "åĽ° éļ¾",
+ "ĥ åľ",
+ "åŀ ĥåľ",
+ "åŀĥåľ ¾",
+ "Ġd rug",
+ "ç¢ ³",
+ "Ġn ame",
+ "åĮĸ çļĦ",
+ "a id",
+ "æľĢ 大",
+ "æij Ħ",
+ "ç®Ģåįķ çļĦ",
+ "Ġw arm",
+ "Ġd one",
+ "Ġfun ction",
+ "as c",
+ "强 è°ĥ",
+ "Ġdem and",
+ "Ġvis ual",
+ "Ġup d",
+ "æŃ£ åľ¨",
+ "Ġsim ilar",
+ "éĢ Ĵ",
+ "æ¯ Ľ",
+ "éĶ »",
+ "ent ly",
+ "Ġvalu able",
+ "Ġdis aster",
+ "ä¸Ģ èά",
+ "æ´ ²",
+ "ĠR eg",
+ "Ġdiscrim ination",
+ "åĨĻ ä¸Ģç¯ĩ",
+ "Ġgovern ment",
+ "Ġ 好çļĦ",
+ "5 00",
+ "ly ing",
+ "Ġpre v",
+ "Ġpre pare",
+ "Ġproble ms",
+ "è· ³",
+ "Ġpro m",
+ "åĨ ²",
+ "å®ī è£ħ",
+ "éĶ» çĤ¼",
+ "æµ ĵ",
+ "è ¹",
+ "åºĶç͍ ç¨ĭåºı",
+ "n g",
+ "Ġcomp et",
+ "åĪĨ åĪ«",
+ "olo gical",
+ "å® ¡",
+ "Ġtrans l",
+ "Ġdire ct",
+ "åī Ĥ",
+ "Ġsuggest ions",
+ "Ġpap er",
+ "Ġrecogn ize",
+ "t on",
+ "Ġmit igate",
+ "讨 论",
+ "äºĴ åĬ¨",
+ "ĠE ar",
+ "Ġam azing",
+ "c re",
+ "é¦ Ī",
+ "Ġinvol ved",
+ "f ace",
+ "æľī åħ³",
+ ") )",
+ "Ġex ce",
+ "Ġproduct ivity",
+ "è Ń",
+ "é¦ Ĩ",
+ "Ġsound s",
+ "Ġidentify ing",
+ "] ,",
+ "é¾ Ļ",
+ "Ġf it",
+ "Ġcontribut e",
+ "th s",
+ "friend ly",
+ "e le",
+ "if ied",
+ "iven ess",
+ "ite ly",
+ "Ġ X",
+ "Ġl ed",
+ "åĿ ı",
+ "Ġhist or",
+ "Ġd at",
+ "Ġjour ney",
+ "Ġ }",
+ "Ġse lect",
+ "æ¼ «",
+ "Ġcon duct",
+ "è¿Ľ ä¸ĢæŃ¥",
+ "ç»Ļ æĪij",
+ "Ġl if",
+ "è£ħ ä¿®",
+ "为 ä»Ģä¹Ī",
+ "äº ¬",
+ "Ġn av",
+ "Ġwho le",
+ "ç ¹ģ",
+ "åĨ ľ",
+ "æĶ »",
+ "Ġb reat",
+ "Ġm iss",
+ "é¾ Ħ",
+ "t t",
+ "s w",
+ "Ġb ar",
+ "请 éĹ®",
+ "èģĶ ç½ij",
+ "Ġatt ract",
+ "æĤ¨ åı¯ä»¥",
+ "O ne",
+ "åħħ åĪĨ",
+ "r ing",
+ "Ġå½ĵ çĦ¶",
+ "re am",
+ "Ġev ol",
+ "Ġs n",
+ "ĠE m",
+ "m osp",
+ "Ġcho ose",
+ "v iew",
+ "Ġar r",
+ "Ġs leep",
+ "end ed",
+ "æŀ ¶",
+ "Ġveh icles",
+ "Ġf resh",
+ "Ġorganiz ation",
+ "è¿Ļ 段",
+ "æ± ¤",
+ "ĠI nt",
+ "Ġcont ext",
+ "åı¦ å¤ĸ",
+ "Ġoce an",
+ "æĦŁ åıĹ",
+ "Ġpollut ion",
+ "ur b",
+ "æī§ è¡Į",
+ "erson al",
+ "ĠHe alth",
+ "ä¼ĺ çĤ¹",
+ "Ġatt ention",
+ "æľī çĿĢ",
+ "é£Ł æĿIJ",
+ "Ġer r",
+ "çļĦæĿ ¥",
+ "çļĦç Ī",
+ "èŃ ¦",
+ "è· Ł",
+ "æĹħ è¡Į",
+ "èĴ ľ",
+ "çļĦæĢ Ŀ",
+ "Ġchat bot",
+ "çļĦ éľĢæ±Ĥ",
+ "çķ ¥",
+ "Ġfeel ing",
+ "Ġimplement ed",
+ "社 åĮº",
+ "çļĦ 建议",
+ "æIJ ħ",
+ "éĹ »",
+ "åıį é¦Ī",
+ "缴 æİ¥",
+ "æĺ ¥",
+ "it able",
+ "æĪij ä¼ļ",
+ "åį ±",
+ "èī¯ å¥½",
+ "Ġl iving",
+ "åıĺ éĩı",
+ "ĠB ut",
+ "Ġcomple te",
+ "Ġtre nds",
+ "Ġm akes",
+ "ä»Ĭ 天",
+ "Ġdist ribut",
+ "Ġcomm it",
+ "Ġat mosp",
+ "ä¼ ´",
+ "Ġsens ors",
+ "Ġs w",
+ "æĹł 论",
+ "om en",
+ "æĶ¿ åºľ",
+ "Ġchall enge",
+ "Ġt urn",
+ "çIJĨ 论",
+ "p ar",
+ "Ġwrit e",
+ "ç»ı åħ¸",
+ "em ember",
+ "é¥ Ń",
+ "æĸ¹ 便",
+ "Ġc u",
+ "Ġval ue",
+ "Ġf und",
+ "p ose",
+ "è°ĥ æŁ¥",
+ "çĿ ¡",
+ "Ġcommunic ate",
+ "Ġdise ase",
+ "Ġrese arc",
+ "Ġl ack",
+ "arn ing",
+ "ĠP ark",
+ "çĦ ¦",
+ "é«ĺ 度",
+ "Ġr ather",
+ "å® £",
+ "çĪ ¶",
+ "éĺ ¶",
+ "è® ¢",
+ "çĥ §",
+ "Ġhig her",
+ "Ġsumm ary",
+ "ĠA ut",
+ "çļĦæ ³",
+ "Ġe le",
+ "is ms",
+ "Ġrel i",
+ "ä¹Ł ä¼ļ",
+ "f ra",
+ "åijĬè¯ī æĪij",
+ "æĬ ½",
+ "Ġsitu ations",
+ "Ġmar ine",
+ "æĥ³ è¦ģ",
+ "in ci",
+ "in al",
+ "Ġg ain",
+ "Ġdiffere nce",
+ "æľºåύ 人",
+ "æµģ ç¨ĭ",
+ "ĠC hat",
+ "ç½ij ç«Ļ",
+ "æľ «",
+ "Ġcol or",
+ "Ġas pect",
+ "ç½ Ĺ",
+ "ĠE duc",
+ "Ġde ploy",
+ "Ġbeaut y",
+ "æĤ £",
+ "ruct ion",
+ "it ut",
+ "æĿ Ł",
+ "让 æĪij们",
+ "éķ¿ åº¦",
+ "ul es",
+ "æ¶ī åıĬ",
+ "Ġdig ital",
+ "Ġexist ing",
+ "ĠO r",
+ "\\_ \\_",
+ "Ġback ground",
+ "çĹ ĩ",
+ "æ¯ı 天",
+ "p ython",
+ "Ġfarm ers",
+ "Ġcontin u",
+ "\" :",
+ "Ġg iven",
+ "å°ı æĹ¶",
+ "Ġmom ent",
+ "2 00",
+ "J ohn",
+ "éĿ¢ 对",
+ "Ġint ro",
+ "Ġtherap y",
+ "è¿Ķ åĽŀ",
+ "å¹¶ åľ¨",
+ "Ġ z",
+ "Ġaff ord",
+ "ä¸ Ŀ",
+ "å® ½",
+ "Ġ Ã",
+ "ĠN ational",
+ "èĥ ¡",
+ "Ġexercis e",
+ "æIJħ æĭĮ",
+ "æĶ¯ ä»ĺ",
+ "éĺ³ åħī",
+ "è¯ ļ",
+ "Ġs ect",
+ "ĠS u",
+ "å¢ŀ éķ¿",
+ "ç¾İ 丽",
+ "Ġw a",
+ "以ä¸ĭæĺ¯ ä¸ĢäºĽ",
+ "èĽĭ ç³ķ",
+ "Ġ ill",
+ "æ¸ħ æĻ",
+ "et ry",
+ "æ¢ ¦",
+ "ç¾İ åĽ½",
+ "ä» į",
+ "one y",
+ "Ġecosystem s",
+ "æĮĩ 导",
+ "d ef",
+ "9 9",
+ "æŁ Ķ",
+ "pp ed",
+ "Ġlim it",
+ "çİ ī",
+ "Ġacad emic",
+ "Ġrestaur ants",
+ "Ġhe ad",
+ "ä¿¡ ä»»",
+ "ast ers",
+ "å² ģ",
+ "ak ers",
+ "1 4",
+ "A s",
+ "æł ¡",
+ "é«ĺ æķĪ",
+ "ph as",
+ "y n",
+ "ç¨ĭ 度",
+ "è¾ £",
+ "ä¸Ĭ éĿ¢",
+ "å®¶ å±ħ",
+ "ter m",
+ "ç¾İ é£Ł",
+ "Ġo vers",
+ "å® ĺ",
+ "Ġind ic",
+ "ĠY our",
+ "S t",
+ "形 象",
+ "è´ ¡",
+ "åº Ĭ",
+ "ĠS c",
+ "ag ra",
+ "羣 æŃ£",
+ "o int",
+ "id s",
+ "are nt",
+ "éĵ ¶",
+ "èģ Ĭ",
+ "Ġreg ular",
+ "ä¼ĺ ç§Ģ",
+ "Ġcol le",
+ "çĸ ij",
+ "Ġsub ject",
+ "Ġgreat er",
+ "Ġst ore",
+ "åŁ¹ è®Ń",
+ "Ġim ag",
+ "Ġan sw",
+ "ä½ Ļ",
+ "Ġsp ot",
+ "åĪĨ åŃIJ",
+ "Ġaud ience",
+ "p et",
+ "Ġv ers",
+ "Ġtra il",
+ "åĭ ĩ",
+ "er ous",
+ "Ġguid ance",
+ "Ġspe ech",
+ "åĵ ²",
+ "æĺ¯ çͱ",
+ "è´¡ çĮ®",
+ "åIJĪéĢĤ çļĦ",
+ "设 æĸ½",
+ "ä»ĸ 人",
+ "ens ive",
+ "åĢ ¾",
+ "al ing",
+ "Ġproject s",
+ "å ³",
+ "Ġt akes",
+ "ç» ©",
+ "T hat",
+ "Ġb ro",
+ "iv ed",
+ "Ġ &",
+ "åĿ IJ",
+ "place ment",
+ "è¿ŀ æİ¥",
+ "çļĦç¤ ¾",
+ "ĠT ra",
+ "Ġrel ax",
+ "u fact",
+ "éģ į",
+ "Ġsur v",
+ "åı£ åij³",
+ "Ġcreat ivity",
+ "o f",
+ "å¨ ģ",
+ "çļĦç ł",
+ "Ġbreat h",
+ "Ġpl aces",
+ "Ġdesc rib",
+ "èĭ± è¯Ń",
+ "Ġdam age",
+ "or ation",
+ "为 æĤ¨",
+ "if t",
+ "Ġc ase",
+ "å¹´ é¾Ħ",
+ "Ġp ress",
+ "çĶ ľ",
+ "éĩ İ",
+ "æĹħ 游",
+ "Ġt aken",
+ "in ed",
+ "Ġcon cept",
+ "æĴ Ń",
+ "Ġinterest ing",
+ "è· µ",
+ "Ġse a",
+ "6 0",
+ "Ġf oot",
+ "ĠN ame",
+ "Ġresearc hers",
+ "éĢ ģ",
+ "Ġwe e",
+ ") ;",
+ "çļĦ åħ³éĶ®",
+ "ä¼ ½",
+ "ele br",
+ "å¡ ij",
+ "W e",
+ "ç»ı 常",
+ "Ġpopul ations",
+ "åħ¬ å¼ı",
+ "or n",
+ "çĩ ĥ",
+ "人 çĶŁ",
+ "1 7",
+ "æİ¥ åıĹ",
+ "Ġloc ation",
+ "Ġin equ",
+ "Ġinter vent",
+ "Ġinterest ed",
+ "Ġdefin itely",
+ "Ġassist ance",
+ "è¿Ļ ä¸Ģ",
+ "åIJĪ åIJĮ",
+ "ä¼ĺ åĬ¿",
+ "çļĦ å·¥ä½ľ",
+ "Ġ1 2",
+ "Ġmo v",
+ "åģ ı",
+ "åŃĺ åĤ¨",
+ "us ive",
+ "æĹ ı",
+ "ï¼ī ï¼Į",
+ "Ġg as",
+ "Ġinterest s",
+ "æ¸ħæĻ °",
+ "Ġg ard",
+ "çĸ «",
+ "Ġs ay",
+ "å¤ «",
+ "g es",
+ "èIJ ¨",
+ "ä¸ļ åĬ¡",
+ "个 æĢ§",
+ "åIJ ¯",
+ "Ġeng agement",
+ "Ġb ig",
+ "éľĢè¦ģ èĢĥèĻij",
+ "Ġpr inci",
+ "åij¨ åĽ´",
+ "Ġopportun ity",
+ "çģ ¾",
+ "èĹ ı",
+ "re l",
+ "缺 çĤ¹",
+ "Ġhapp y",
+ "åĴĮ åħ¶ä»ĸ",
+ "av a",
+ "Ġestab lish",
+ "鸡 èĽĭ",
+ "i king",
+ "ĠT rans",
+ "rast ructure",
+ "fore st",
+ "èİ· åıĸ",
+ "èĦ ļ",
+ "in ally",
+ "èµ ı",
+ "Ġdel icious",
+ "Ġresult s",
+ "è§Ĥ å¯Ł",
+ "å®ŀ è·µ",
+ "Ġl ast",
+ "Ġpol it",
+ "æĢ§ èĥ½",
+ "F or",
+ "b i",
+ "缸 ä¿¡",
+ "ff ee",
+ "Ġph r",
+ "Ġfore st",
+ "ell ing",
+ "æµģ è¡Į",
+ "at ic",
+ "大 家",
+ "ĠIn st",
+ "æķ° åѦ",
+ "æī ©",
+ "å®Į åħ¨",
+ "å¼ķ èµ·",
+ "es e",
+ "转 æį¢",
+ "Ġaffect ed",
+ "Ġrobot ics",
+ "综 ä¸Ĭ",
+ "Ġpro p",
+ "让 人",
+ "æ² ³",
+ "ä¸Ń æľĢ",
+ "Ġaut onomous",
+ "Ġha ving",
+ "Ġtri p",
+ "ur y",
+ "Ġbi ased",
+ "Ġconsider ations",
+ "Ġpartic ular",
+ "åį ł",
+ "æİ¨ 广",
+ "Ġiniti atives",
+ "ial s",
+ "åij³ éģĵ",
+ "Ġtreat ments",
+ "Ġem phas",
+ "çĭ¬çī¹ çļĦ",
+ "Ġl ay",
+ "æĶ¿ çŃĸ",
+ "æĢİ ä¹Ī",
+ "ron ic",
+ "pl ay",
+ "Ġco ok",
+ "è¿Ľ åħ¥",
+ "è½ ®",
+ "Ġvol unte",
+ "Ġra in",
+ "ĠM on",
+ "Ġconsum ption",
+ "èĽĭ çϽ",
+ "ĠS oc",
+ "å£ ¤",
+ "Ġrout ine",
+ "Ġimpro ved",
+ "T o",
+ "人 çī©",
+ "读 èĢħ",
+ "Ġgo al",
+ "广 åijĬ",
+ "éķ¿ æľŁ",
+ "Ġe y",
+ "H e",
+ "Ġout do",
+ "Ġcu is",
+ "Ġa way",
+ "Ġbo oks",
+ "Ġtop ic",
+ "大 åĪ©",
+ "h ouse",
+ "Ġon es",
+ "ç§ Ł",
+ "' :",
+ "æĪ¿ å±ĭ",
+ "ç§» åĬ¨",
+ "Ġdis asters",
+ "est s",
+ "ill ing",
+ "绿 èī²",
+ "åĵ² åѦ",
+ "æĪIJ åĪĨ",
+ "Ġocc ur",
+ "ľ ä¼½",
+ "åľŁ 壤",
+ "çļĦ 主è¦ģ",
+ "çݰ å®ŀ",
+ "Ġanim al",
+ "é¢Ĩ 导",
+ "Ġview s",
+ "éĤ ®",
+ "æ°§ åĮĸ",
+ "ath y",
+ "éģĵ å¾·",
+ "社交 åªĴä½ĵ",
+ "ĠP ersonal",
+ "Ľ åĽ´",
+ "Ġpur ch",
+ "Ġcount ry",
+ "Ġrem ind",
+ "å¯ ¸",
+ "Ġr ights",
+ "çļĦ çݯå¢ĥ",
+ "ĠP r",
+ "Ġl ine",
+ "ib r",
+ "é© ¾",
+ "Ġm aj",
+ "Ġover come",
+ "Ġne xt",
+ "æīĢ è¿°",
+ "è§Ħ å®ļ",
+ "Ġinteract ions",
+ "Ġconf lic",
+ "Ġwh y",
+ "ç³» åĪĹ",
+ "å° ¼",
+ "ib ly",
+ "çīĽ å¥¶",
+ "Ġrespons es",
+ "s es",
+ "åѦ ä¼ļ",
+ "b ol",
+ "Ġstand ards",
+ "ul ner",
+ "对è¯Ŀ åĨħ容",
+ "l ished",
+ "çļĦæĢ §",
+ "çĶŁæĢģ ç³»ç»Ł",
+ "an n",
+ "æĥħåĨµ ä¸ĭ",
+ "寻 æ±Ĥ",
+ "Ġh old",
+ "d en",
+ "åį ĥ",
+ "Ġment ion",
+ "ĠMan y",
+ "缴 åΰ",
+ "éģ Ĺ",
+ "he l",
+ "Ġbelie ve",
+ "ar ies",
+ "æľī ä¸Ģ个",
+ "1 3",
+ "Ġatmosp here",
+ "Ġm or",
+ "æĹ¥ æľŁ",
+ "ä¹ ħ",
+ "ä½ł 好",
+ "Ġaddress ing",
+ "ĠâĢ ĵ",
+ "çļĦåľ° æĸ¹",
+ "m ing",
+ "Ġcan not",
+ "Ġman ufact",
+ "Ġp ie",
+ "ic ing",
+ "Ġstud ies",
+ "ç¾İ åij³",
+ "ĠAmeric an",
+ "ĠN LP",
+ "Ġacc ording",
+ "ms elves",
+ "èĦ Ĥ",
+ "èĩª ä¿¡",
+ "æīĢ éľĢ",
+ "Ġthe mselves",
+ "Ġremot e",
+ "åŁ¹ åħ»",
+ "å®ī æİĴ",
+ "ä½ł éľĢè¦ģ",
+ "Ġreg ard",
+ "ir ing",
+ "è¯Ĩ åĪ«",
+ "Ġart icle",
+ "æģ Ĵ",
+ "æĢ» çļĦæĿ¥",
+ "Ġal ign",
+ "æ± ł",
+ "ten ance",
+ "fact ion",
+ "åĬ¨ ä½ľ",
+ "çļĦç ©",
+ "ç¼ ©",
+ "æĢ ¥",
+ "Ġ1 00",
+ "Ġtest ing",
+ "åŃĹ æ¯į",
+ "å¹´ è½»",
+ "åζ éĢł",
+ "Ġs we",
+ "å° º",
+ "he ns",
+ "æ°´ æŀľ",
+ "Ġinf rastructure",
+ "èī² å½©",
+ "æĢ»çļĦæĿ¥ 说",
+ "æľī ä»Ģä¹Ī",
+ "te xt",
+ "车 è¾Ĩ",
+ "Ġp ay",
+ "ro p",
+ "Ċ ĠĠ",
+ "Ġcaus ed",
+ "Ġcor rect",
+ "Ġ ì",
+ "èĥ ŀ",
+ "ĠM ed",
+ "ç²¾ ç¥ŀ",
+ "æ°ĶåĢĻ åıĺåĮĸ",
+ "ĠR ed",
+ "äºĴ èģĶç½ij",
+ "Ġeng age",
+ "åĪĨ 为",
+ "ĠD ata",
+ "Ġful l",
+ "en c",
+ "éĩį æĸ°",
+ "æŃ£ç¡® çļĦ",
+ "çļĦæ° Ķ",
+ "åıĮ æĸ¹",
+ "Ġcom es",
+ "åı¤ 代",
+ "æŁIJ äºĽ",
+ "åijĪ çݰ",
+ "Ġto day",
+ "ag ed",
+ "æĪij åı¯ä»¥",
+ "æĹ¥ 常",
+ "æ» ij",
+ "Ġcl in",
+ "Ġ \\",
+ "Ġo bs",
+ "Ġart ificial",
+ "Ġexce ll",
+ "çļĦç ¬",
+ "all s",
+ "Ġprodu ce",
+ "ĠD es",
+ "os s",
+ "è¹ Ī",
+ "Ġdra w",
+ "Ġlet ter",
+ "Ġadv ice",
+ "Ġhigh ly",
+ "çĬ ¯",
+ "综ä¸Ĭ æīĢè¿°",
+ "满 æĦı",
+ "Ġprinci ples",
+ "èĮ Ħ",
+ "Ġfeel ings",
+ "çļĦæ ´",
+ "Ġh om",
+ "Ġf ail",
+ "Ġcro p",
+ "å§ ľ",
+ "Ġquest ion",
+ "Ġdis abilities",
+ "èĪŀ è¹Ī",
+ "Ġimp lications",
+ "r al",
+ "Ġs ing",
+ "4 0",
+ "Ġfam il",
+ "Ġgovern ments",
+ "Ġrec ord",
+ "å½¢ çĬ¶",
+ "Ġbe gin",
+ "is es",
+ "çļĦæĥ ³",
+ "ach ine",
+ "è° ±",
+ "Ġv ulner",
+ "Ġpro per",
+ "Ġovers ight",
+ "è´Ł éĿ¢",
+ "Ġem ail",
+ "Ġnew s",
+ "Ġexpl oring",
+ "Ġf avor",
+ "æ¥ ¼",
+ "å® ľ",
+ "Ġun ivers",
+ "å·® å¼Ĥ",
+ "ï¼ī ãĢĤ",
+ "è§£åĨ³ éĹ®é¢ĺ",
+ "Ġfam ous",
+ "g n",
+ "Ġmess age",
+ "at itude",
+ "Ġc ra",
+ "Ġco ver",
+ "æ·± åĪ»",
+ "åı¯ä»¥ éĢīæĭ©",
+ "çĶŁæ´» ä¸Ń",
+ "ç§į ç±»",
+ "Ġsm art",
+ "on str",
+ "ve y",
+ "çĶ ²",
+ "Ġreg ularly",
+ "ĠS m",
+ "æĦŁ è§ī",
+ "Ġthough t",
+ "Ġex h",
+ "c ure",
+ "ç» ĺ",
+ "认 è¯Ĩ",
+ "Ġo ld",
+ "æĦ ī",
+ "称 为",
+ "Ġfiel ds",
+ "Ġcons ist",
+ "ã ģ",
+ "ç»Ĩ èĥŀ",
+ "Ġh ours",
+ "8 0",
+ "al king",
+ "è§ī å¾Ĺ",
+ "ç» Ŀ",
+ "ä½ł 们",
+ "ĠEng lish",
+ "Ġsignificant ly",
+ "Ġs ource",
+ "Ġan t",
+ "Ġeducation al",
+ "Ġtas k",
+ "Ġhand le",
+ "æIJ ľ",
+ "ĠS p",
+ "Ġcall ed",
+ "Ġter ms",
+ "æ² ī",
+ "Ġw in",
+ "duct ion",
+ "Ġmod ern",
+ "Ġcuis ine",
+ "å¥ Ĺ",
+ "è§ ¦",
+ "olut ely",
+ "ç« ¥",
+ "p ite",
+ "Ġf elt",
+ "Ġcomp re",
+ "Ġw ond",
+ "è¿IJ è¡Į",
+ "Ġres il",
+ "缸 ä¼¼",
+ "éĩij èŀį",
+ "çα æĥħ",
+ "ç¬ Ķ",
+ "èĪ ª",
+ "è° Ī",
+ "åĬĽ çļĦ",
+ "æľī æīĢ",
+ "æ½ ľ",
+ "ul ate",
+ "Ġdetect ion",
+ "宣 ä¼ł",
+ "Ġmat ter",
+ "éĩı åŃIJ",
+ "W rite",
+ "ç»ĵ åIJĪ",
+ "ç»ı è¿ĩ",
+ "Ġdevelop ers",
+ "è ª",
+ "Ġ ---",
+ "人 éĻħ",
+ "çŃ ¾",
+ "ï¼ļ âĢľ",
+ "Ġinnov ative",
+ "ãĢĤ âĢĿ",
+ "å½ ¼",
+ "é¥ ¼",
+ "è¿ĩ 度",
+ "Ġplan et",
+ "åħ °",
+ "å¸ ģ",
+ "æķ ¬",
+ "Ġleg al",
+ "Ġlo t",
+ "æĪIJ为 äºĨ",
+ "i ate",
+ "Ġm is",
+ "åģĩ 设",
+ "çļĦ æĸĩ竳",
+ "ĠCom pan",
+ "Ġd oc",
+ "Ġcare ful",
+ "Ġe ver",
+ "æĪij们 å°Ĩ",
+ "ä¾ĭ åŃIJ",
+ "ä¹ ³",
+ "ä½ľ èĢħ",
+ "åIJ §",
+ "æļ ´",
+ "Ġrem ember",
+ "缮 çļĦ",
+ "Ġp ut",
+ "常è§ģ çļĦ",
+ "Ġf est",
+ "建 设",
+ "å®ŀ ç͍",
+ "Ġact ive",
+ "çª Ĺ",
+ "ou th",
+ "åİŁ çIJĨ",
+ "Ġtry ing",
+ "è¿ ·",
+ "缸 åIJĮ",
+ "éħĴ åºĹ",
+ "An other",
+ "æľĢ ä½³",
+ "Ġanaly tics",
+ "Ġper pet",
+ "ip ment",
+ "Ġ å¦Ĥæŀľ",
+ "è§Ĥ ä¼Ĺ",
+ "Ġc elebr",
+ "Ġhe av",
+ "Ġmed itation",
+ "大 æ°Ķ",
+ "A nd",
+ "ä¸į éĶĻ",
+ "Ġwhe ther",
+ "s et",
+ "Ġdem onstr",
+ "ä¸Ģ 款",
+ "æĶ¶ éĽĨ",
+ "éĻIJ åζ",
+ "Ġ ing",
+ "Ġrev olution",
+ "çľ ģ",
+ "Ġsc ience",
+ "缮 åīį",
+ "Ġthink ing",
+ "± ä¹IJ",
+ "课 ç¨ĭ",
+ "Ġp ack",
+ "Ġim age",
+ "lo c",
+ "Ġst ories",
+ "uc k",
+ "Ġsatis faction",
+ "Ġcollect ion",
+ "h o",
+ "èµ ŀ",
+ "éĿ¢ 临",
+ "Ġl a",
+ "Ġsym bol",
+ "Ġem b",
+ "Ġhabit ats",
+ "Ġlow er",
+ "Ġcontin ues",
+ "éľ ĩ",
+ "åĵ Ī",
+ "ĠT ake",
+ "Ġenviron ments",
+ "Ġth ree",
+ "Ġen c",
+ "ĠA cc",
+ "æĦı åij³",
+ "åİ ¨",
+ "ch an",
+ "ĠH um",
+ "Ġtr ue",
+ "åĪĩ æĪIJ",
+ "s ing",
+ "âĢĶ âĢĶ",
+ "åĩº æĿ¥",
+ "Ġreg ion",
+ "Ġinter pre",
+ "Ġdiagnos is",
+ "é ŀ",
+ "Ġdo ing",
+ "Ġr un",
+ "Ġco ffee",
+ "Ġmaj or",
+ "Ġmindful ness",
+ "Ġafford able",
+ "çĻ ¾",
+ "Ġdetail ed",
+ "éĿŀ常 éĩįè¦ģçļĦ",
+ "çļĦæ² ŁéĢļ",
+ "çļĦæķ ħ",
+ "åĢĴ åħ¥",
+ "Ġthem es",
+ "Ġnet work",
+ "ï¼ī ï¼ļ",
+ "ĠUn ited",
+ "çļĦæĮ ĩ",
+ "ort s",
+ "åį« çĶŁ",
+ "Ġplan ning",
+ "æĥ ł",
+ "åī ª",
+ "ĠPro v",
+ "çļĦ åºĶç͍",
+ "Ġp eri",
+ "Ġaccount able",
+ "çī Ļ",
+ "çļĦç ģ",
+ "Ġcho ice",
+ "ĠC omm",
+ "id ents",
+ "çļĦ å®īåħ¨",
+ "å¹¶ ä¸į",
+ "太éĺ³ ç³»",
+ "Ġrece ive",
+ "Ġclo se",
+ "çļĦæĹ¶ åĢĻ",
+ "Ġchang ing",
+ "ä»·å̼ è§Ĥ",
+ "Ġperpet u",
+ "Ġse ason",
+ "Ġm en",
+ "Ġlearn ed",
+ "Ġsitu ation",
+ "Ġre place",
+ "he ad",
+ "让 æĪij",
+ "åľ¨ ä¸Ģèµ·",
+ "çļĦç© º",
+ "éľ ²",
+ "Ġen ough",
+ "å±ķ çݰ",
+ "Ġlead ers",
+ "an cing",
+ "Ġtemper ature",
+ "åı «",
+ "Ġ3 0",
+ "æĦıåij³ çĿĢ",
+ "æ± ĩ",
+ "ĠGo vern",
+ "Ġfocus ed",
+ "u ro",
+ "Ġsim ple",
+ "Ġh iking",
+ "æ¯ Ĵ",
+ "Ġcompre hens",
+ "äº Ī",
+ "Ġcreat ed",
+ "con d",
+ "é¡ µ",
+ "ĠW or",
+ "è¯ģ æį®",
+ "Ġwork place",
+ "Ġcharact ers",
+ "çļĦ 设计",
+ "Ġme chan",
+ "ĠD is",
+ "ç¥ŀ ç§ĺ",
+ "å· ŀ",
+ "ĠO n",
+ "< /",
+ "ç§į æ¤į",
+ "Ġpat h",
+ "Ġlim ited",
+ "Ġsol ar",
+ "çļĦæ ı",
+ "2 2",
+ "Ġappreci ate",
+ "å¿« ä¹IJ",
+ "æĦŁ åıĹåΰ",
+ "èĢ Ĺ",
+ "m ed",
+ "ic ine",
+ "Ġnot e",
+ "å½ĵ åīį",
+ "æĪij们 åºĶ该",
+ "Ġse en",
+ "ä¸Ģ åIJį",
+ "å°½ åı¯èĥ½",
+ "è¿IJ ç®Ĺ",
+ "è§Ĵ 度",
+ "Ġequ ipment",
+ "Ġsp read",
+ "è ¸",
+ "è® ¿",
+ "åı¥ è¯Ŀ",
+ "æĮ ¥",
+ "Ġpur pose",
+ "请 ä½ł",
+ "Y our",
+ "ari an",
+ "ä» ª",
+ "Ġperspect ives",
+ "åĩº äºĨ",
+ "å©ļ 礼",
+ "Ġexcell ent",
+ "ĠEns uring",
+ "Ġre ach",
+ "éĺ¶ æ®µ",
+ "ä¿Ŀ éļľ",
+ "Ġemp athy",
+ "ĠM y",
+ "çij ľä¼½",
+ "Ġ ver",
+ "ab el",
+ "ĠPre dict",
+ "Ġmain tenance",
+ "è¯Ħ ä»·",
+ "Ġ ult",
+ "åĴ ¨",
+ "o x",
+ "åĴ¨ 询",
+ "Ġshare d",
+ "in a",
+ "l ist",
+ "Ġoutdo or",
+ "Ġthough ts",
+ "in ating",
+ "éĴ ±",
+ "Ġfra me",
+ "éĺ ¿",
+ "åĪ© 润",
+ "çļĦæİ ¨",
+ "åį ļ",
+ "Ġrec ent",
+ "Ġal tern",
+ "are d",
+ "= =",
+ "Ġro ad",
+ "äºĭ 项",
+ "g ed",
+ "y nt",
+ "Ġspe nd",
+ "ç½ ª",
+ "åıĸ å¾Ĺ",
+ "é ¹",
+ "l i",
+ "æĹ¶ æľŁ",
+ "严 éĩį",
+ "å¿ Ĩ",
+ "å© ´",
+ "æİ¥ ä¸ĭæĿ¥",
+ "ĠEar th",
+ "ĠChat bots",
+ "Ġset ting",
+ "ç¥ Ŀ",
+ "éĶĢåĶ® é¢Ŀ",
+ "ä¼ ¦",
+ "Ġread ing",
+ "æİ¢ 讨",
+ "a ign",
+ "éŀ ĭ",
+ "Ġyou ng",
+ "Ġcare er",
+ "Ġteac hers",
+ "çļĦ è´¨éĩı",
+ "å±ŀ äºİ",
+ "Ġeas ier",
+ "Ġscient ific",
+ "ç¾İ åħĥ",
+ "Ġsp ir",
+ "åĬ ³",
+ "çļĦæĶ ¯",
+ "r ist",
+ "èµĦ 产",
+ "çĶŁ åŃĺ",
+ "èĩ³ å°ij",
+ "å§ ¿",
+ "Ġvide o",
+ "Ġa im",
+ "å®Ŀ å®Ŀ",
+ "çζ æ¯į",
+ "________ ________",
+ "al ities",
+ "Ġb ud",
+ "Ġstre et",
+ "Ġ æĺ¯",
+ "æĸ¹ ç¨ĭ",
+ "ä¸ĸ 纪",
+ "c hes",
+ "ear ch",
+ "æĴ °",
+ "Ġeng ine",
+ "Ġdis placement",
+ "ĠRo bots",
+ "erv ised",
+ "é¡ ¶",
+ "ou d",
+ "Ġw alk",
+ "Ġemerg ency",
+ "èģ ĺ",
+ "n al",
+ "Ġdat as",
+ "åĢ º",
+ "åIJİ çļĦ",
+ "å¾Ī 好",
+ "Ġmy self",
+ "çļĦæī ĭ",
+ "Ġus age",
+ "Ġsh own",
+ "æ® Ĭ",
+ "Ġtyp ically",
+ "u ly",
+ "æĸ° éĹ»",
+ "æĽ ¿",
+ "Ġor ig",
+ "è½» æĿ¾",
+ "æĺ¾ 示",
+ "Ġado pt",
+ "èĤ¡ 票",
+ "Ġp arent",
+ "a ps",
+ "æĢĿ æĥ³",
+ "Ġmarket ing",
+ "èĻ «",
+ "éĥ¨ éŨ",
+ "çļĦæķ Ī",
+ "Ġcomfort able",
+ "åŃ¦ä¹ł åĴĮ",
+ "Ġfore cast",
+ "ict ion",
+ "Ġget ting",
+ "Ġtre es",
+ "av ing",
+ "çļĦ åŁºç¡Ģ",
+ "read y",
+ "æĸ° é²ľ",
+ "go ing",
+ "¹ é¥",
+ "Ġev idence",
+ "¹é¥ ª",
+ "ç§ ĭ",
+ "æľī å¾Īå¤ļ",
+ "éĿ¢ è¯ķ",
+ "éģĩ åΰ",
+ "ç»Ļ å®ļ",
+ "ir c",
+ "åı¯ä»¥ æł¹æį®",
+ "驾 驶",
+ "å·§ åħĭ",
+ "Ġst unning",
+ "çļĦæ ¦Ĥ",
+ "æ¡ Į",
+ "ĠJ ohn",
+ "ul ation",
+ "åıĤ èĢĥ",
+ "Ġf lex",
+ "çĦ¦ èĻij",
+ "ym akers",
+ "Ġfor ms",
+ "s h",
+ "v al",
+ "ĠS o",
+ "c o",
+ "æİ¨ åĬ¨",
+ "èħ ¿",
+ "çī¹ æ®Ĭ",
+ "Ġen ab",
+ "å°Ĩ ä¼ļ",
+ "æĶ¯ åĩº",
+ "åĿļ æĮģ",
+ "红 èī²",
+ "Ġopt ion",
+ "Ġstart ed",
+ "r ation",
+ "Ġpo etry",
+ "Ġp ort",
+ "g en",
+ "èª ī",
+ "Ġdel iv",
+ "çĶ ļ",
+ "éĢ »",
+ "éĢī 项",
+ "Ġg round",
+ "å½¼ æŃ¤",
+ "an a",
+ "çļĦæĹ ¥",
+ "åľ¨ 线",
+ "Ġse cure",
+ "Ġ æł¹æį®",
+ "饮 æĸĻ",
+ "Ġgr atitude",
+ "第 ä¸ī",
+ "Ġs ong",
+ "Ġpoint s",
+ "Ġal ready",
+ "çļĦçĪ ±",
+ "ĠTe chn",
+ "Ġreal ity",
+ "çı Ń",
+ "Ġs ince",
+ "Ġpopul ation",
+ "y ond",
+ "b or",
+ "ĠSoc ial",
+ "æıIJ åıĸ",
+ "å·¥ ç¨ĭ",
+ "a ff",
+ "交 æĺĵ",
+ "Ġwor th",
+ "å¡ «",
+ "å¨ ±ä¹IJ",
+ "Ġdo g",
+ "ĠAr t",
+ "ç¡ ¬",
+ "æµ· æ´ĭ",
+ "åĨ Ĵ",
+ "çī Ī",
+ "Ġprogramm ing",
+ "ĠAs s",
+ "ĠM achine",
+ "å̼ å¾Ĺ",
+ "请 è¾ĵåħ¥",
+ "声 éŁ³",
+ "Ġexercis es",
+ "åħī 线",
+ "æ³ķ åĴĮ",
+ "Ġfeat ure",
+ "e ff",
+ "è¿Ľ æŃ¥",
+ "女 æĢ§",
+ "Ġefficient ly",
+ "çļĦæĬĢ æľ¯",
+ "Ġgen etic",
+ "令 人",
+ "è´ ¦",
+ "çļĦ 产åĵģ",
+ "åİ ļ",
+ "åĴĮ æĸĩåĮĸ",
+ "éĻ Ħ",
+ "Ġmo b",
+ "综 åIJĪ",
+ "t ers",
+ "æľī ä¸Ģ",
+ "å¦ Ĩ",
+ "åį Ī",
+ "Ġout side",
+ "Ġprop ert",
+ "éĤ® ä»¶",
+ "主 ä¹ī",
+ "Ġpolic y",
+ "èĩª 身",
+ "Ġnav igate",
+ "Ġst y",
+ "ç͵ èĦij",
+ "Ġab ilities",
+ "Ġfac ed",
+ "çļĦç ¼",
+ "çļĦ å°ı",
+ "è ķ",
+ "Ġt one",
+ "ig ation",
+ "åıĤ æķ°",
+ "èĽĭçϽ è´¨",
+ "ä½ Ľ",
+ "çĶļ èĩ³",
+ "Ġsk in",
+ "èĴ ¸",
+ "æĭ Ľ",
+ "éŃ Ķ",
+ "ash ion",
+ "Ġing red",
+ "æĹ ĭ",
+ "Ġcamp aign",
+ "Ġm ount",
+ "Ġcons id",
+ "Ġmus e",
+ "n ter",
+ "w ater",
+ "ä¼ļ è®®",
+ "Ġprotect ion",
+ "ä¿Ŀ éĻ©",
+ "Ġcro ps",
+ "og le",
+ "éļı æĹ¶",
+ "æļ Ĺ",
+ "i um",
+ "ä¹ ı",
+ "Ġdi et",
+ "l ies",
+ "ç͍ æĿ¥",
+ "ĠEn coura",
+ "æĬ Ĺ",
+ "ap an",
+ "éĺ² æŃ¢",
+ "W ow",
+ "çļĦ åŁºæľ¬",
+ "å¹³ æĸ¹",
+ "Ġst ep",
+ "åı¯ éĿł",
+ "表 æĺİ",
+ "Ġpredict ions",
+ "Ġsym pt",
+ "Ġdiagnos es",
+ "åħ¬ åĽŃ",
+ "Ġsupp ly",
+ "Ġprev ious",
+ "ç»Ħ åIJĪ",
+ ". ,",
+ "çļĦ è¿ĩç¨ĭ",
+ "æķ ı",
+ "s u",
+ "ar is",
+ "çķ ħ",
+ "oc ol",
+ "æIJľ ç´¢",
+ "it le",
+ "éĨ Ĵ",
+ "顾 客",
+ "éĢ» è¾ij",
+ "éĿŀ常 éĩįè¦ģ",
+ "ĠB i",
+ "å·¦ åı³",
+ "am m",
+ "Ġevery thing",
+ "æĺ ł",
+ "Ġincre d",
+ "Ġpe ace",
+ "èľ ľ",
+ "Ġmuse um",
+ "çĭ¬ ç«ĭ",
+ "Ġcomprehens ive",
+ "Ġr ates",
+ "/ /",
+ "Ġra d",
+ "åĦ¿ ç«¥",
+ "çī¹ èī²",
+ "ĠPredict ive",
+ "å¼ķ åĬĽ",
+ "l er",
+ "å° ¤",
+ "ic ro",
+ "è¡ ¥",
+ "Ġdeterm ine",
+ "çļĦ åĨħ容",
+ "Ġcom pl",
+ "Ġgreen house",
+ "èħ IJ",
+ "Ġhigh light",
+ "Ġpart ners",
+ "Ġdo ct",
+ "çļĦ 使ç͍",
+ "æŃĮ æĽ²",
+ "æĮĩ åįĹ",
+ "ĠA f",
+ "æľº æŀĦ",
+ "éĢ Ģ",
+ "Ġpoem s",
+ "å¿ĥ åĴĮ",
+ "Ġatt end",
+ "çļĦæ¸ ¸",
+ "Ġs ide",
+ "al es",
+ "Ġmention ed",
+ "ĠA bs",
+ "Ġhistor ical",
+ "Ġle ft",
+ "以ä¸ĭ åĩłä¸ª",
+ "åıĹ æ¬¢è¿İ",
+ "èıľ åĵģ",
+ "Ġrem ain",
+ "æ ĩ",
+ "Ġtour s",
+ "ł éģĵ",
+ "Ġerr ors",
+ "æľº åζ",
+ "æ ¦",
+ "æĤ£ èĢħ",
+ "m ore",
+ "Ġexpert s",
+ "çļĦçł Ķç©¶",
+ "ç»ĵ æĿŁ",
+ "Ġwrit ten",
+ "çł Ķ",
+ "Ġe t",
+ "in put",
+ "æ°Ķ ä½ĵ",
+ "è ļ",
+ "æĥ Ĭ",
+ "Ġa ge",
+ "éĩį å¤į",
+ "å¼ ¹",
+ "åŃ ¤",
+ "Ġsympt oms",
+ "Ġbelie f",
+ "' d",
+ "i ol",
+ "Ġ1 8",
+ "åħħ è¶³",
+ "çı į",
+ "force ment",
+ "æĸ Ĺ",
+ "ª èĮĦ",
+ "Ġ1 5",
+ "ä¸Ģ个 人",
+ "Ġapp lic",
+ "è´ ¥",
+ "ä½į äºİ",
+ "éϤ äºĨ",
+ "= \"",
+ "ä¸ī è§Ĵ",
+ "æĢĿ ç»´",
+ "åį ·",
+ "Ġf ru",
+ "ĠCol labor",
+ "Ġpr im",
+ "Ġrequire d",
+ "Ġw atch",
+ "è°ĥ åij³",
+ "ç»ĵ 论",
+ "on y",
+ "Ġgu ide",
+ "Ġm ax",
+ "ĠC ould",
+ "Ġadv ent",
+ "ĠO verall",
+ "çļĦæĬ ķ",
+ "Ġexp er",
+ "å ĺ",
+ "ic ial",
+ "ost er",
+ "çļĦ é¢ľèī²",
+ "Ġoper ations",
+ "éĥ ģ",
+ "Ġm oney",
+ "le y",
+ "c ling",
+ "Ġo il",
+ "çļ® èĤ¤",
+ "Ġg e",
+ "Ġb at",
+ "ĠP h",
+ "Ġsc he",
+ "Ġelect ric",
+ "v est",
+ "Ġch ain",
+ "Ġcap abilities",
+ "ir d",
+ "è¯ģ æĺİ",
+ "æľĢ 好",
+ "iv il",
+ "Ġdepend ing",
+ "Ġs ave",
+ "Ġpract ical",
+ "Ġcult ures",
+ "缸åºĶ çļĦ",
+ "s y",
+ "çļĦç ²",
+ "Ġbeh ind",
+ "æĹ¶éĹ´ åĴĮ",
+ "å¹ ħ",
+ "ĠA g",
+ "Ġeffect iveness",
+ "A d",
+ "ĠO f",
+ "Ġany thing",
+ "å·§åħĭ åĬĽ",
+ "Ġm ist",
+ "Ġlangu ages",
+ "ĠM ake",
+ "å «",
+ "æ£ ®",
+ "ĠCon t",
+ "ĠAbs olutely",
+ "Ġinvest ment",
+ "m at",
+ "çļĦæķħ äºĭ",
+ "æ¬ §",
+ "Ġspe ed",
+ "çļĦæ¸ ©",
+ "Ġc ities",
+ "åĨĻ ä½ľ",
+ "Th anks",
+ "Ġd ed",
+ "åĪĨ éħį",
+ "Ġd ark",
+ "Ġsupport ing",
+ "å¹ ķ",
+ "ĠK e",
+ "éĽ ¶",
+ "Ġsh aring",
+ "Ġh ouse",
+ "认 çŁ¥",
+ "Ġsurround ing",
+ "Ġredu ced",
+ "Ġf u",
+ "Ġst or",
+ "Ġab s",
+ "T om",
+ "c ent",
+ "ĠEduc ation",
+ "Ġth r",
+ "ot t",
+ "ĠTh at",
+ "Ġhe ar",
+ "un g",
+ "Ġbe yond",
+ "ĠC o",
+ "ro om",
+ "è¯Ĺ æŃĮ",
+ "re me",
+ "Ġlit tle",
+ "Ġg ames",
+ "ä¹ĭ åIJİ",
+ "éĥ½ ä¼ļ",
+ "è¯Ń éŁ³",
+ "ç¬ ij",
+ "çī¹ å®ļ",
+ "第 ä¸Ģ",
+ "Ġdep ression",
+ "Ġinnov ation",
+ "ĠF r",
+ "Ġcomput er",
+ "c an",
+ "å³ °",
+ "ç¼ĸåĨĻ ä¸Ģ个",
+ "Ġintern ational",
+ "Ġcan cer",
+ "åѦ èĢħ",
+ "Ġdisc over",
+ "he t",
+ "Ġcomp os",
+ "Ġrec y",
+ "Ġ2 00",
+ "åIJ« æľī",
+ "çĹ Ľ",
+ "ç¼ĵ è§£",
+ "Ġfre qu",
+ "çĶ ³",
+ "ĠM ar",
+ "çļĦ éĢīæĭ©",
+ "Ġun t",
+ "Ġreg ions",
+ "Ġop in",
+ "ĠGovern ments",
+ "æ¶ Ĥ",
+ "åĨħ å¿ĥ",
+ "ä¸Ĭ æľĢ",
+ "ä»į çĦ¶",
+ "l ier",
+ "æ³ ³",
+ "äºĴ 缸",
+ "ĠSt ud",
+ "az on",
+ "Ġar ch",
+ "Ġche m",
+ "çļĦ èĥ½åĬĽ",
+ "çļĦ ä¸Ģ个",
+ "Ġa p",
+ "Ġre d",
+ "Ġw omen",
+ "Ġpro te",
+ "Ġfind ing",
+ "å§ »",
+ "éĢĤå½ĵ çļĦ",
+ "Ġfor ward",
+ "对 象",
+ "Ġwa it",
+ "Ġconsid ered",
+ "du le",
+ "b acks",
+ "Ġclin ical",
+ "åħ· å¤ĩ",
+ "éº ¦",
+ "Ġon going",
+ "åĨ Ľ",
+ "Ġf ar",
+ "åĴĮ è°",
+ "XX X",
+ "Ġpolit ical",
+ "Ġcam er",
+ "çļĦ è¡Į为",
+ "æĦı 大åĪ©",
+ "Ġapp s",
+ "åĩı è½»",
+ "Ġread ers",
+ "å©ļ å§»",
+ "æ° ¸",
+ "o res",
+ "åħ¨ éĿ¢",
+ "ĠAf ric",
+ "Ġfavor ite",
+ "Ġm ill",
+ "Ġd ang",
+ "ĠSt ates",
+ "åĢ Ł",
+ "å¯ ¿",
+ "Ġl at",
+ "è¿ĩ åİ»",
+ "Ġtr uly",
+ "åĽŀçŃĶ éĹ®é¢ĺ",
+ "Ġco gn",
+ "ä» °",
+ "ĠJ apan",
+ "iz z",
+ "çļĦæĿ IJ",
+ "x x",
+ "é¢ĺ 缮",
+ "ri ption",
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+ "讲 述",
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+ "Ġno vel",
+ "å±ħ æ°ij",
+ "çļĦ åıĺåĮĸ",
+ "å°½ éĩı",
+ "Ġspir it",
+ "å®Į ç¾İ",
+ "è´ ·",
+ "å¿ħè¦ģ çļĦ",
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+ "示 ä¾ĭ",
+ "Ġd iv",
+ "æķ´ æķ°",
+ "Ġeconom y",
+ "Ġethical ly",
+ "éĻ Ī",
+ "Ġschool s",
+ "Ġnet works"
+ ]
+ }
+}
\ No newline at end of file
diff --git a/model/minimind_tokenizer/tokenizer_config.json b/model/minimind_tokenizer/tokenizer_config.json
new file mode 100644
index 0000000..5f3fa2b
--- /dev/null
+++ b/model/minimind_tokenizer/tokenizer_config.json
@@ -0,0 +1,44 @@
+{
+ "add_bos_token": false,
+ "add_eos_token": false,
+ "add_prefix_space": true,
+ "added_tokens_decoder": {
+ "0": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "2": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ }
+ },
+ "additional_special_tokens": [],
+ "bos_token": "",
+ "clean_up_tokenization_spaces": false,
+ "eos_token": "",
+ "legacy": true,
+ "model_max_length": 1000000000000000019884624838656,
+ "pad_token": null,
+ "sp_model_kwargs": {},
+ "spaces_between_special_tokens": false,
+ "tokenizer_class": "PreTrainedTokenizerFast",
+ "unk_token": "user\\n' + content + '\\nassistant\\n' }}{% elif message['role'] == 'assistant' %}{{ content + '' + '\\n' }}{% endif %}{% endfor %}"
+}
\ No newline at end of file
diff --git a/model/minimind_tokenizer/vocab.json b/model/minimind_tokenizer/vocab.json
new file mode 100644
index 0000000..e1318cc
--- /dev/null
+++ b/model/minimind_tokenizer/vocab.json
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\ No newline at end of file
diff --git a/model/model.py b/model/model.py
new file mode 100644
index 0000000..1608d91
--- /dev/null
+++ b/model/model.py
@@ -0,0 +1,530 @@
+import math
+import struct
+import inspect
+from .LMConfig import LMConfig
+from typing import Any, Optional, Tuple
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import nn
+from transformers import PreTrainedModel
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+
+class RMSNorm(torch.nn.Module):
+ def __init__(self, dim: int, eps: float):
+ super().__init__()
+ self.eps = eps
+ self.weight = nn.Parameter(torch.ones(dim))
+
+ def _norm(self, x):
+ return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+ def forward(self, x):
+ output = self._norm(x.float()).type_as(x)
+ return output * self.weight
+
+
+def precompute_pos_cis(dim: int, end: int, theta: float = 10000.0):
+ freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
+ t = torch.arange(end, device=freqs.device) # type: ignore
+ freqs = torch.outer(t, freqs).float() # type: ignore
+ pos_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64
+ return pos_cis
+
+
+def apply_rotary_emb(xq, xk, pos_cis):
+ def unite_shape(pos_cis, x):
+ ndim = x.ndim
+ assert 0 <= 1 < ndim
+ assert pos_cis.shape == (x.shape[1], x.shape[-1])
+ shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+ return pos_cis.view(*shape)
+
+ xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
+ xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
+ pos_cis = unite_shape(pos_cis, xq_)
+ xq_out = torch.view_as_real(xq_ * pos_cis).flatten(3)
+ xk_out = torch.view_as_real(xk_ * pos_cis).flatten(3)
+ return xq_out.type_as(xq), xk_out.type_as(xk)
+
+
+def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
+ """torch.repeat_interleave(x, dim=2, repeats=n_rep)"""
+ bs, slen, n_kv_heads, head_dim = x.shape
+ if n_rep == 1:
+ return x
+ return (
+ x[:, :, :, None, :]
+ .expand(bs, slen, n_kv_heads, n_rep, head_dim)
+ .reshape(bs, slen, n_kv_heads * n_rep, head_dim)
+ )
+
+
+class Attention(nn.Module):
+ def __init__(self, args: LMConfig):
+ super().__init__()
+ self.n_kv_heads = args.n_heads if args.n_kv_heads is None else args.n_kv_heads
+ assert args.n_heads % self.n_kv_heads == 0
+ model_parallel_size = 1
+ self.n_local_heads = args.n_heads // model_parallel_size
+ self.n_local_kv_heads = self.n_kv_heads // model_parallel_size
+ self.n_rep = self.n_local_heads // self.n_local_kv_heads
+ self.head_dim = args.dim // args.n_heads
+ self.wq = nn.Linear(args.dim, args.n_heads * self.head_dim, bias=False)
+ self.wk = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
+ self.wv = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
+ self.wo = nn.Linear(args.n_heads * self.head_dim, args.dim, bias=False)
+ self.attn_dropout = nn.Dropout(args.dropout)
+ self.resid_dropout = nn.Dropout(args.dropout)
+ self.dropout = args.dropout
+
+ # use flash attention or a manual implementation?
+ self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention') and args.flash_attn
+
+ if not self.flash:
+ # print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0")
+ mask = torch.full((1, 1, args.max_seq_len, args.max_seq_len), float("-inf"))
+ mask = torch.triu(mask, diagonal=1)
+ self.register_buffer("mask", mask)
+
+ def forward(
+ self,
+ x: torch.Tensor,
+ pos_cis: torch.Tensor,
+ use_kv_cache: bool = False,
+ past_kv: Tuple[torch.Tensor] = None
+ ):
+ bsz, seqlen, _ = x.shape
+ # QKV
+ # inference
+ if use_kv_cache:
+ # 只计算最后一个token的Q
+ current_token = x[:, -1:, :]
+
+ if not past_kv:
+ xq = self.wq(x)
+ xk, xv = self.wk(x), self.wv(x)
+ else:
+ past_key, past_value = past_kv
+ xq = torch.cat((torch.zeros_like(x[:, :-1, :]), self.wq(current_token)), dim=1)
+ xk = torch.cat((past_key, self.wk(current_token)), dim=1)
+ xv = torch.cat((past_value, self.wv(current_token)), dim=1)
+
+ past_kv = (xk, xv)
+ else:
+ xq = self.wq(x)
+ xk, xv = self.wk(x), self.wv(x)
+
+ xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim)
+ xk = xk.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
+ xv = xv.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
+
+ # RoPE relative positional embeddings
+ xq, xk = apply_rotary_emb(xq, xk, pos_cis)
+
+ # grouped multiquery attention: expand out keys and values
+ xk = repeat_kv(xk, self.n_rep) # (bs, seqlen, n_local_heads, head_dim)
+ xv = repeat_kv(xv, self.n_rep) # (bs, seqlen, n_local_heads, head_dim)
+
+ # make heads into a batch dimension
+ xq = xq.transpose(1, 2) # (bs, n_local_heads, seqlen, head_dim)
+ xk = xk.transpose(1, 2)
+ xv = xv.transpose(1, 2)
+
+ # flash implementation
+ if self.flash:
+ output = torch.nn.functional.scaled_dot_product_attention(xq, xk, xv, attn_mask=None,
+ dropout_p=self.dropout if self.training else 0.0,
+ is_causal=True)
+ else:
+ # manual implementation
+ scores = torch.matmul(xq, xk.transpose(2, 3)) / math.sqrt(self.head_dim)
+ assert hasattr(self, 'mask')
+ scores = scores + self.mask[:, :, :seqlen, :seqlen] # (bs, n_local_heads, seqlen, cache_len + seqlen)
+ scores = F.softmax(scores.float(), dim=-1).type_as(xq)
+ scores = self.attn_dropout(scores)
+ output = torch.matmul(scores, xv) # (bs, n_local_heads, seqlen, head_dim)
+
+ # restore time as batch dimension and concat heads
+ output = output.transpose(1, 2).contiguous().view(bsz, seqlen, -1)
+
+ # final projection into the residual stream
+ output = self.wo(output)
+ output = self.resid_dropout(output)
+ return output, past_kv
+
+
+class FeedForward(nn.Module):
+ def __init__(self, dim: int, hidden_dim: int, multiple_of: int, dropout: float):
+ super().__init__()
+ if hidden_dim is None:
+ hidden_dim = 4 * dim
+ hidden_dim = int(2 * hidden_dim / 3)
+ hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
+ self.w1 = nn.Linear(dim, hidden_dim, bias=False)
+ self.w2 = nn.Linear(hidden_dim, dim, bias=False)
+ self.w3 = nn.Linear(dim, hidden_dim, bias=False)
+ self.dropout = nn.Dropout(dropout)
+
+ def forward(self, x):
+ return self.dropout(self.w2(F.silu(self.w1(x)) * self.w3(x)))
+
+
+class MoEGate(nn.Module):
+ def __init__(self, config: LMConfig):
+ super().__init__()
+ self.config = config
+ self.top_k = config.num_experts_per_tok
+ self.n_routed_experts = config.n_routed_experts
+
+ self.scoring_func = config.scoring_func
+ self.alpha = config.aux_loss_alpha
+ self.seq_aux = config.seq_aux
+
+ # topk selection algorithm
+ self.norm_topk_prob = config.norm_topk_prob
+ self.gating_dim = config.dim
+ self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim)))
+ self.reset_parameters()
+
+ def reset_parameters(self) -> None:
+ import torch.nn.init as init
+ init.kaiming_uniform_(self.weight, a=math.sqrt(5))
+
+ def forward(self, hidden_states):
+ bsz, seq_len, h = hidden_states.shape
+ ### compute gating score
+ hidden_states = hidden_states.view(-1, h)
+ logits = F.linear(hidden_states, self.weight, None)
+ if self.scoring_func == 'softmax':
+ scores = logits.softmax(dim=-1)
+ else:
+ raise NotImplementedError(f'insupportable scoring function for MoE gating: {self.scoring_func}')
+
+ ### select top-k experts
+ topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=False)
+
+ ### norm gate to sum 1
+ if self.top_k > 1 and self.norm_topk_prob:
+ denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20
+ topk_weight = topk_weight / denominator
+
+ ### expert-level computation auxiliary loss
+ if self.training and self.alpha > 0.0:
+ scores_for_aux = scores
+ aux_topk = self.top_k
+ # always compute aux loss based on the naive greedy topk method
+ topk_idx_for_aux_loss = topk_idx.view(bsz, -1)
+ if self.seq_aux:
+ scores_for_seq_aux = scores_for_aux.view(bsz, seq_len, -1)
+ ce = torch.zeros(bsz, self.n_routed_experts, device=hidden_states.device)
+ ce.scatter_add_(1, topk_idx_for_aux_loss,
+ torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device)).div_(
+ seq_len * aux_topk / self.n_routed_experts)
+ aux_loss = (ce * scores_for_seq_aux.mean(dim=1)).sum(dim=1).mean() * self.alpha
+ else:
+ mask_ce = F.one_hot(topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts)
+ ce = mask_ce.float().mean(0)
+ Pi = scores_for_aux.mean(0)
+ fi = ce * self.n_routed_experts
+ aux_loss = (Pi * fi).sum() * self.alpha
+ else:
+ aux_loss = None
+ return topk_idx, topk_weight, aux_loss
+
+
+class MOEFeedForward(nn.Module):
+ def __init__(self, config: LMConfig):
+ super().__init__()
+ self.config = config
+ self.experts = nn.ModuleList([
+ FeedForward(
+ dim=config.dim,
+ hidden_dim=config.hidden_dim,
+ multiple_of=config.multiple_of,
+ dropout=config.dropout,
+ )
+ for _ in range(config.n_routed_experts)
+ ])
+
+ self.gate = MoEGate(config)
+ if config.n_shared_experts is not None:
+ self.shared_experts = FeedForward(
+ dim=config.dim,
+ hidden_dim=config.hidden_dim,
+ multiple_of=config.multiple_of,
+ dropout=config.dropout,
+ )
+
+ def forward(self, x):
+ identity = x
+ orig_shape = x.shape
+ bsz, seq_len, _ = x.shape
+
+ # 使用门控机制选择专家
+ topk_idx, topk_weight, aux_loss = self.gate(x)
+
+ x = x.view(-1, x.shape[-1])
+ flat_topk_idx = topk_idx.view(-1)
+
+ if self.training:
+ # 训练模式下,重复输入数据
+ x = x.repeat_interleave(self.config.num_experts_per_tok, dim=0)
+ y = torch.empty_like(x, dtype=torch.float16)
+ for i, expert in enumerate(self.experts):
+ y[flat_topk_idx == i] = expert(x[flat_topk_idx == i])
+ y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
+ y = y.view(*orig_shape)
+ else:
+ # 推理模式下,只选择最优专家
+ y = self.moe_infer(x, flat_topk_idx, topk_weight.view(-1, 1)).view(*orig_shape)
+
+ if self.config.n_shared_experts is not None:
+ y = y + self.shared_experts(identity)
+
+ return y
+
+ @torch.no_grad()
+ def moe_infer(self, x, flat_expert_indices, flat_expert_weights):
+ expert_cache = torch.zeros_like(x)
+ idxs = flat_expert_indices.argsort()
+ tokens_per_expert = flat_expert_indices.bincount().cpu().numpy().cumsum(0)
+ token_idxs = idxs // self.config.num_experts_per_tok
+ # 例如当tokens_per_expert=[6, 15, 20, 26, 33, 38, 46, 52]
+ # 当token_idxs=[3, 7, 19, 21, 24, 25, 4, 5, 6, 10, 11, 12...]
+ # 意味着当token_idxs[:6] -> [3, 7, 19, 21, 24, 25, 4]位置的token都由专家0处理,token_idxs[6:15]位置的token都由专家1处理......
+ for i, end_idx in enumerate(tokens_per_expert):
+ start_idx = 0 if i == 0 else tokens_per_expert[i - 1]
+ if start_idx == end_idx:
+ continue
+ expert = self.experts[i]
+ exp_token_idx = token_idxs[start_idx:end_idx]
+ expert_tokens = x[exp_token_idx]
+ expert_out = expert(expert_tokens)
+ expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]])
+ # 使用 scatter_add_ 进行 sum 操作
+ expert_cache.scatter_add_(0, exp_token_idx.view(-1, 1).repeat(1, x.shape[-1]), expert_out)
+
+ return expert_cache
+
+
+class TransformerBlock(nn.Module):
+ def __init__(self, layer_id: int, args: LMConfig):
+ super().__init__()
+ self.n_heads = args.n_heads
+ self.dim = args.dim
+ self.head_dim = args.dim // args.n_heads
+ self.attention = Attention(args)
+
+ self.layer_id = layer_id
+ self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps)
+ self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps)
+
+ if args.use_moe:
+ self.feed_forward = MOEFeedForward(args)
+ else:
+ self.feed_forward = FeedForward(
+ dim=args.dim,
+ hidden_dim=args.hidden_dim,
+ multiple_of=args.multiple_of,
+ dropout=args.dropout,
+ )
+
+ def forward(self, x, pos_cis, use_kv_cache=False, past_kv: Tuple[torch.Tensor] = None):
+ attn_res, past_kv = self.attention(self.attention_norm(x), pos_cis, use_kv_cache, past_kv)
+ h = x + attn_res
+ out = h + self.feed_forward(self.ffn_norm(h))
+ return out, past_kv
+
+
+class Transformer(PreTrainedModel):
+ config_class = LMConfig
+ last_loss: Optional[torch.Tensor]
+
+ def __init__(self, params: LMConfig = None):
+ super().__init__(params)
+ if not params:
+ params = LMConfig()
+ self.params = params
+ self.vocab_size = params.vocab_size
+ self.n_layers = params.n_layers
+
+ self.tok_embeddings = nn.Embedding(params.vocab_size, params.dim)
+ self.dropout = nn.Dropout(params.dropout)
+ self.layers = torch.nn.ModuleList()
+ for layer_id in range(self.n_layers):
+ self.layers.append(TransformerBlock(layer_id, params))
+ self.norm = RMSNorm(params.dim, eps=params.norm_eps)
+ self.output = nn.Linear(params.dim, params.vocab_size, bias=False)
+
+ # share the unembedding parameters with the embedding parameters
+ self.tok_embeddings.weight = self.output.weight # https://paperswithcode.com/method/weight-tying
+
+ # some useful precompute for the RoPE relative positional embeddings
+ pos_cis = precompute_pos_cis(self.params.dim // self.params.n_heads, self.params.max_seq_len)
+ self.register_buffer("pos_cis", pos_cis, persistent=False)
+
+ # init all weights
+ self.apply(self._init_weights)
+ # apply special scaled init to the residual projections, per GPT-2 paper
+ for pn, p in self.named_parameters():
+ if pn.endswith('w3.weight') or pn.endswith('wo.weight'):
+ torch.nn.init.normal_(p, mean=0.0, std=0.02 / math.sqrt(2 * params.n_layers))
+
+ # Initialize attribute for the loss of the last forward call. This will be set if the forward is called with a targets tensor.
+ self.last_loss = None
+ self.OUT = CausalLMOutputWithPast()
+
+ def _init_weights(self, module):
+ if isinstance(module, nn.Linear):
+ torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+ if module.bias is not None:
+ torch.nn.init.zeros_(module.bias)
+ elif isinstance(module, nn.Embedding):
+ torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+
+ def forward(self, tokens: Optional[torch.Tensor] = None,
+ targets: Optional[torch.Tensor] = None,
+ use_kv_cache=False, past_kvs=None, **keyargs):
+ if past_kvs is None:
+ past_kvs = [None for _ in range(self.n_layers)]
+ if 'input_ids' in keyargs:
+ tokens = keyargs['input_ids']
+ if 'attention_mask' in keyargs:
+ targets = keyargs['attention_mask']
+
+ _bsz, seqlen = tokens.shape
+ h = self.tok_embeddings(tokens)
+ h = self.dropout(h)
+ pos_cis = self.pos_cis[:seqlen]
+ for idx, layer in enumerate(self.layers):
+ h, past_kvs[idx] = layer(h, pos_cis, use_kv_cache, past_kvs[idx])
+
+ h = self.norm(h)
+
+ if targets is not None:
+ # if we are given some desired targets also calculate the loss
+ logits = self.output(h)
+ self.last_loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
+ else:
+ # inference-time mini-optimization: only forward the output on the very last position
+ logits = self.output(h[:, [-1], :]) # note: using list [-1] to preserve the time dim
+ self.last_loss = None
+
+ self.OUT.__setitem__('logits', logits)
+ self.OUT.__setitem__('last_loss', self.last_loss)
+
+ if use_kv_cache:
+ return self.OUT, past_kvs
+ return self.OUT
+
+
+ @torch.inference_mode()
+ def generate(self, idx, eos, max_new_tokens, temperature=0.7, top_k=None, stream=True, repetition_penalty=1.):
+ index = idx.shape[1]
+ use_kv_cache = True
+ past_kvs = [None for _ in range(self.n_layers)]
+ while idx.shape[1] < max_new_tokens - 1:
+ # if the sequence context is growing too long we must crop it at block_size
+ idx_cond = idx # if idx.size(1) <= self.params.max_seq_len else idx[:, -self.params.max_seq_len:]
+ # forward the model to get the logits for the index in the sequence
+ inference_res = self(idx_cond, use_kv_cache=use_kv_cache, past_kvs=past_kvs)
+ if use_kv_cache:
+ logits, past_kvs = inference_res[0].logits, inference_res[1]
+ else:
+ logits = inference_res.logits
+
+ logits = logits[:, -1, :] # crop to just the final time step
+
+ # Apply repetition penalty
+ for token in set(idx.tolist()[0]):
+ logits[:, token] /= repetition_penalty
+
+ if temperature == 0.0:
+ # "sample" the single most likely index
+ __, idx_next = torch.topk(logits, k=1, dim=-1)
+ else:
+ # pluck the logits at the final step and scale by desired temperature
+ logits = logits / temperature
+ # optionally crop the logits to only the top k options
+ if top_k is not None:
+ v, __ = torch.topk(logits, min(top_k, logits.size(-1)))
+ logits[logits < v[:, [-1]]] = -float('Inf')
+
+ # apply softmax to convert logits to (normalized) probabilities
+ probs = F.softmax(logits, dim=-1)
+ idx_next = torch.multinomial(probs, num_samples=1, generator=None)
+ # append sampled index to the running sequence and continue
+ if idx_next == eos:
+ break
+
+ idx = torch.cat((idx, idx_next), dim=1)
+ if stream:
+ yield idx[:, index:]
+
+ if not stream:
+ yield idx[:, index:]
+
+ @torch.inference_mode()
+ def eval_answer(self, idx):
+ # if the sequence context is growing too long we must crop it at block_size
+ idx_cond = idx if idx.size(1) <= self.params.max_seq_len else idx[:, -self.params.max_seq_len:]
+ # forward the model to get the logits for the index in the sequence
+ past_kvs = [None for _ in range(self.n_layers)]
+ inference_res = self(idx_cond, use_kv_cache=False, past_kvs=past_kvs)
+ logits = inference_res.logits
+ logits = logits[:, -1, :]
+ return logits
+
+ def export(self, filepath='model.bin'):
+ """export the model weights in fp32 into .bin file to be read from C"""
+ f = open(filepath, 'wb')
+
+ def serialize(t):
+ d = t.detach().cpu().view(-1).numpy().astype(np.float32)
+ b = struct.pack(f'{len(d)}f', *d)
+ f.write(b)
+
+ # first write out the header
+ hidden_dim = self.layers[0].feed_forward.w1.weight.shape[0]
+ p = self.params
+ n_kv_heads = p.n_heads if p.n_kv_heads is None else p.n_kv_heads
+ header = struct.pack('iiiiiii', p.dim, hidden_dim, p.n_layers, p.n_heads,
+ n_kv_heads, p.vocab_size, p.max_seq_len)
+ f.write(header)
+
+ # next write out the embedding weights
+ serialize(self.tok_embeddings.weight)
+
+ # now all the layers
+ # attention weights
+ for layer in self.layers:
+ serialize(layer.attention_norm.weight)
+ for layer in self.layers:
+ serialize(layer.attention.wq.weight)
+ for layer in self.layers:
+ serialize(layer.attention.wk.weight)
+ for layer in self.layers:
+ serialize(layer.attention.wv.weight)
+ for layer in self.layers:
+ serialize(layer.attention.wo.weight)
+ # ffn weights
+ for layer in self.layers:
+ serialize(layer.ffn_norm.weight)
+ for layer in self.layers:
+ serialize(layer.feed_forward.w1.weight)
+ for layer in self.layers:
+ serialize(layer.feed_forward.w2.weight)
+ for layer in self.layers:
+ serialize(layer.feed_forward.w3.weight)
+ # final rmsnorm
+ serialize(self.norm.weight)
+ # note: no need to write final classifier weights due to weight sharing
+ # pos_cis
+ serialize(self.freqs_cos[:p.max_seq_len])
+ serialize(self.freqs_sin[:p.max_seq_len])
+
+ # write to binary file
+ f.close()
+ print(f"wrote {filepath}")
diff --git a/model/tokenizer.model b/model/tokenizer.model
new file mode 100644
index 0000000..85c0803
Binary files /dev/null and b/model/tokenizer.model differ
diff --git a/model/tokenizer_config.json b/model/tokenizer_config.json
new file mode 100644
index 0000000..eb538f9
--- /dev/null
+++ b/model/tokenizer_config.json
@@ -0,0 +1,44 @@
+{
+ "add_bos_token": false,
+ "add_eos_token": false,
+ "add_prefix_space": true,
+ "added_tokens_decoder": {
+ "0": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "2": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ }
+ },
+ "additional_special_tokens": [],
+ "bos_token": "",
+ "clean_up_tokenization_spaces": false,
+ "eos_token": "",
+ "legacy": true,
+ "model_max_length": 1000000000000000019884624838656,
+ "pad_token": null,
+ "sp_model_kwargs": {},
+ "spaces_between_special_tokens": false,
+ "tokenizer_class": "LlamaTokenizer",
+ "unk_token": "user\\n' + content + '\\nassistant\\n' }}{% elif message['role'] == 'assistant' %}{{ content + '' + '\\n' }}{% endif %}{% endfor %}"
+}
diff --git a/my_openai_api.py b/my_openai_api.py
new file mode 100644
index 0000000..958816d
--- /dev/null
+++ b/my_openai_api.py
@@ -0,0 +1,407 @@
+# encoding: utf-8
+import json
+import re
+import time
+import uuid
+import warnings
+
+import tiktoken
+import torch
+import numpy as np
+from typing import List
+from flask import Flask, current_app, request, Blueprint, stream_with_context
+from flask_cors import CORS
+from sentence_transformers import SentenceTransformer
+from sklearn.preprocessing import PolynomialFeatures
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from marshmallow import validate, Schema, fields
+from pydantic import BaseModel
+
+warnings.filterwarnings('ignore', category=UserWarning)
+
+# ------------------------------------------------------------------------------------------------------------------
+DEVICE_NAME = "cuda:0" if torch.cuda.is_available() else "cpu"
+DEVICE = torch.device(DEVICE_NAME)
+MODEL_PATH = "./minimind-small-T"
+TOKENIZE_PATH = MODEL_PATH
+max_new_tokens = 2048
+temperature = 0.7
+top_k = 8
+
+
+# ------------------------------------------------------------------------------------------------------------------
+
+class Transformers():
+ def __init__(self, app=None, tokenizer=None, model=None):
+ # self.chat = None
+ if app is not None:
+ self.init_app(app, tokenizer, model)
+
+ def init_app(self, app, tokenizer=None, model=None, chat=None):
+ self.tokenizer = tokenizer
+ self.model = model
+ # if chat is None:
+ # # self.chat = model.chat
+ # self.chat = self.chat
+
+ # gpt2's
+ def build_chat_input(self, tokenizer, messages: List[dict]):
+ new_prompt = tokenizer.apply_chat_template(
+ messages,
+ tokenize=False,
+ add_generation_prompt=True
+ )[-(max_new_tokens - 1):]
+ inputs_ids = tokenizer(new_prompt).data['input_ids']
+ inputs_ids = (torch.tensor(inputs_ids, dtype=torch.long, device=DEVICE)[None, ...])
+ return inputs_ids, tokenizer.eos_token_id, new_prompt
+
+ def chat_stream(self, tokenizer, messages: List[dict], stream=True):
+ input_ids, eos_token_id, new_prompt = self.build_chat_input(tokenizer, messages)
+ if stream:
+ res_y = self.model.generate(input_ids, tokenizer.eos_token_id, max_new_tokens=max_new_tokens,
+ temperature=temperature, top_k=top_k, stream=True)
+
+ y = next(res_y)
+
+ history_idx = 0
+ while y != None:
+ answer = tokenizer.decode(y[0].tolist())
+ if answer and answer[-1] == '�':
+ try:
+ y = next(res_y)
+ except:
+ break
+ continue
+ # print(answer)
+ if not len(answer):
+ try:
+ y = next(res_y)
+ except:
+ break
+ continue
+
+ yield answer[history_idx:]
+ try:
+ y = next(res_y)
+ except:
+ break
+ history_idx = len(answer)
+ if not stream:
+ break
+
+ def chat_no_stream(self, tokenizer, messages: List[dict]):
+ input_ids, eos_token_id, new_prompt = self.build_chat_input(tokenizer, messages)
+ res_y = self.model.generate(input_ids, tokenizer.eos_token_id, max_new_tokens=max_new_tokens,
+ temperature=temperature, top_k=top_k, stream=False)
+ y = next(res_y)
+ answer = tokenizer.decode(y[0].tolist())
+ return answer
+
+
+tfs = Transformers()
+base_tfs = Transformers()
+
+models_bp = Blueprint('Models', __name__, url_prefix='/v1/models')
+chat_bp = Blueprint('Chat', __name__, url_prefix='/v1/chat')
+completions_bp = Blueprint('Completions', __name__, url_prefix='/v1/completions')
+embedding_bp = Blueprint('Embeddings', __name__, url_prefix='/v1')
+
+
+def sse(line, field="data"):
+ return "{}: {}\n\n".format(
+ field, json.dumps(line, ensure_ascii=False) if isinstance(line, dict) else line)
+
+
+def empty_cache():
+ if torch.backends.mps.is_available():
+ torch.mps.empty_cache()
+
+
+def create_app():
+ app = Flask(__name__)
+ CORS(app)
+ app.register_blueprint(models_bp)
+ app.register_blueprint(chat_bp)
+ app.register_blueprint(completions_bp)
+ app.register_blueprint(embedding_bp)
+
+ @app.after_request
+ def after_request(resp):
+ empty_cache()
+ return resp
+
+ tokenizer = AutoTokenizer.from_pretrained(
+ TOKENIZE_PATH, trust_remote_code=True, use_fast=False)
+
+ model = AutoModelForCausalLM.from_pretrained(
+ MODEL_PATH, trust_remote_code=True).to(DEVICE)
+ # model.generation_config = GenerationConfig.from_pretrained(model_name)
+
+ tfs.init_app(app, tokenizer, model)
+ base_tfs.init_app(app, tokenizer, model)
+
+ return app
+
+
+class ModelSchema(Schema):
+ id = fields.Str()
+ object = fields.Str(dump_default="model", metadata={"example": "model"})
+ created = fields.Int(dump_default=lambda: int(time.time()), metadata={"example": 1695402567})
+ owned_by = fields.Str(dump_default="owner", metadata={"example": "owner"})
+
+
+class ModelListSchema(Schema):
+ object = fields.Str(dump_default="list", metadata={"example": "list"})
+ data = fields.List(fields.Nested(ModelSchema), dump_default=[])
+
+
+class ChatMessageSchema(Schema):
+ role = fields.Str(required=True, metadata={"example": "system"})
+ content = fields.Str(required=True, metadata={"example": "You are a helpful assistant."})
+
+
+class CreateChatCompletionSchema(Schema):
+ model = fields.Str(required=True, metadata={"example": "minimind"})
+ messages = fields.List(
+ fields.Nested(ChatMessageSchema), required=True,
+ metadata={"example": [
+ ChatMessageSchema().dump({"role": "system", "content": "You are a helpful assistant."}),
+ ChatMessageSchema().dump({"role": "user", "content": "Hello!"})
+ ]}
+ )
+ temperature = fields.Float(load_default=1.0, metadata={"example": 1.0})
+ top_p = fields.Float(load_default=1.0, metadata={"example": 1.0})
+ n = fields.Int(load_default=1, metadata={"example": 1})
+ max_tokens = fields.Int(load_default=None, metadata={"example": None})
+ stream = fields.Bool(load_default=False, example=False)
+ presence_penalty = fields.Float(load_default=0.0, example=0.0)
+ frequency_penalty = fields.Float(load_default=0.0, example=0.0)
+
+
+class ChatCompletionChoiceSchema(Schema):
+ index = fields.Int(metadata={"example": 0})
+ message = fields.Nested(ChatMessageSchema, metadata={
+ "example": ChatMessageSchema().dump(
+ {"role": "assistant", "content": "\n\nHello there, how may I assist you today?"}
+ )})
+ finish_reason = fields.Str(
+ validate=validate.OneOf(["stop", "length", "content_filter", "function_call"]),
+ metadata={"example": "stop"})
+
+
+class ChatCompletionSchema(Schema):
+ id = fields.Str(
+ dump_default=lambda: uuid.uuid4().hex,
+ metadata={"example": "cmpl-uqkvlQyYK7bGYrRHQ0eXlWi7"})
+ object = fields.Constant("chat.completion")
+ created = fields.Int(dump_default=lambda: int(time.time()), metadata={"example": 1695402567})
+ model = fields.Str(metadata={"example": "minimind"})
+ choices = fields.List(fields.Nested(ChatCompletionChoiceSchema))
+
+
+class ChatDeltaSchema(Schema):
+ role = fields.Str(metadata={"example": "assistant"})
+ content = fields.Str(required=True, metadata={"example": "Hello"})
+
+
+class ChatCompletionChunkChoiceSchema(Schema):
+ index = fields.Int(metadata={"example": 0})
+ delta = fields.Nested(ChatDeltaSchema, metadata={"example": ChatDeltaSchema().dump(
+ {"role": "assistant", "example": "Hello"})})
+ finish_reason = fields.Str(
+ validate=validate.OneOf(["stop", "length", "content_filter", "function_call"]),
+ metadata={"example": "stop"})
+
+
+class ChatCompletionChunkShema(Schema):
+ id = fields.Str(
+ dump_default=lambda: uuid.uuid4().hex,
+ metadata={"example": "cmpl-uqkvlQyYK7bGYrRHQ0eXlWi7"})
+ object = fields.Constant("chat.completion.chunk")
+ created = fields.Int(dump_default=lambda: int(time.time()), metadata={"example": 1695402567})
+ model = fields.Str(metadata={"example": "minimind"})
+ choices = fields.List(fields.Nested(ChatCompletionChunkChoiceSchema))
+
+
+class CreateCompletionSchema(Schema):
+ model = fields.Str(required=True, metadata={"example": "minimind"})
+ prompt = fields.Raw(metadata={"example": "Say this is a test"})
+ max_tokens = fields.Int(load_default=16, metadata={"example": 256})
+ temperature = fields.Float(load_default=1.0, metadata={"example": 1.0})
+ top_p = fields.Float(load_default=1.0, metadata={"example": 1.0})
+ n = fields.Int(load_default=1, metadata={"example": 1})
+ stream = fields.Bool(load_default=False, example=False)
+ logit_bias = fields.Dict(load_default=None, example={})
+ presence_penalty = fields.Float(load_default=0.0, example=0.0)
+ frequency_penalty = fields.Float(load_default=0.0, example=0.0)
+
+
+class CompletionChoiceSchema(Schema):
+ index = fields.Int(load_default=0, metadata={"example": 0})
+ text = fields.Str(required=True, metadata={"example": "登鹳雀楼->王之涣\n夜雨寄北->"})
+ logprobs = fields.Dict(load_default=None, metadata={"example": {}})
+ finish_reason = fields.Str(
+ validate=validate.OneOf(["stop", "length", "content_filter", "function_call"]),
+ metadata={"example": "stop"})
+
+
+class CompletionUsageSchema(Schema):
+ prompt_tokens = fields.Int(metadata={"example": 5})
+ completion_tokens = fields.Int(metadata={"example": 7})
+ total_tokens = fields.Int(metadata={"example": 12})
+
+
+class CompletionSchema(Schema):
+ id = fields.Str(
+ dump_default=lambda: uuid.uuid4().hex,
+ metadata={"example": "cmpl-uqkvlQyYK7bGYrRHQ0eXlWi7"})
+ object = fields.Constant("text_completion")
+ created = fields.Int(dump_default=lambda: int(time.time()), metadata={"example": 1695402567})
+ model = fields.Str(metadata={"example": "minimind"})
+ choices = fields.List(fields.Nested(CompletionChoiceSchema))
+ usage = fields.Nested(CompletionUsageSchema)
+
+
+@stream_with_context
+def stream_chat_generate(messages):
+ delta = ChatDeltaSchema().dump(
+ {"role": "assistant"})
+ choice = ChatCompletionChunkChoiceSchema().dump(
+ {"index": 0, "delta": delta, "finish_reason": None})
+
+ yield sse(
+ ChatCompletionChunkShema().dump({
+ "model": "minimind",
+ "choices": [choice]})
+ )
+
+ # 调用 chat 方法并遍历其返回的生成器
+ for response in tfs.chat_stream(tfs.tokenizer, messages):
+ delta = ChatDeltaSchema().dump(
+ {"content": response})
+ choice = ChatCompletionChunkChoiceSchema().dump(
+ {"index": 0, "delta": delta, "finish_reason": None})
+
+ yield sse(
+ ChatCompletionChunkShema().dump({
+ "model": "minimind",
+ "choices": [choice]})
+ )
+
+ yield sse('[DONE]')
+
+
+@chat_bp.route("/completions", methods=['POST'])
+def create_chat_completion():
+ create_chat_completion = CreateChatCompletionSchema().load(request.json)
+
+ if create_chat_completion["stream"]:
+ return current_app.response_class(
+ stream_chat_generate(create_chat_completion["messages"]),
+ mimetype="text/event-stream"
+ )
+ else:
+ response = tfs.chat_no_stream(tfs.tokenizer, create_chat_completion["messages"])
+
+ message = ChatMessageSchema().dump(
+ {"role": "assistant", "content": response})
+ choice = ChatCompletionChoiceSchema().dump(
+ {"index": 0, "message": message, "finish_reason": "stop"})
+
+ return ChatCompletionSchema().dump({
+ "model": "minimind",
+ "choices": [choice]})
+
+
+class EmbeddingRequest(BaseModel):
+ input: List[str]
+ model: str
+
+
+@embedding_bp.route("/embeddings", methods=['POST'])
+def get_embeddings():
+ request_data = request.get_json() # 获取 POST 请求体中的 JSON 数据
+ request_params = EmbeddingRequest(**request_data) # 将 JSON 数据转换为 EmbeddingRequest 对象
+
+ def expand_features(embedding, target_length):
+ poly = PolynomialFeatures(degree=2)
+ expanded_embedding = poly.fit_transform(embedding.reshape(1, -1))
+ expanded_embedding = expanded_embedding.flatten()
+ if len(expanded_embedding) > target_length:
+ # 如果扩展后的特征超过目标长度,可以通过截断或其他方法来减少维度
+ expanded_embedding = expanded_embedding[:target_length]
+ elif len(expanded_embedding) < target_length:
+ # 如果扩展后的特征少于目标长度,可以通过填充或其他方法来增加维度
+ expanded_embedding = np.pad(
+ expanded_embedding, (0, target_length - len(expanded_embedding))
+ )
+ return expanded_embedding
+
+ def num_tokens_from_string(string: str) -> int:
+ """Returns the number of tokens in a text string."""
+ encoding = tiktoken.get_encoding('cl100k_base')
+ num_tokens = len(encoding.encode(string))
+ return num_tokens
+
+ def has_chinese_char(s):
+ pattern = re.compile(r'[\u4e00-\u9fa5]')
+ # if bool(pattern.search(s)):
+ # print('m3e编码')
+ # else:
+ # print('bge编码')
+
+ return bool(pattern.search(s))
+
+ # 计算嵌入向量和tokens数量
+ embeddings = [embeddings_model_m3e.encode(text)
+ if has_chinese_char(text)
+ else embeddings_model_bge.encode(text)
+ for text in request_params.input]
+
+ # 如果嵌入向量的维度不为1536,则使用插值法扩展至1536维度
+ embeddings = [
+ expand_features(embedding, 768) if len(embedding) < 768 else embedding
+ for embedding in embeddings
+ ]
+
+ # Min-Max normalization 归一化
+ embeddings = [embedding / np.linalg.norm(embedding) for embedding in embeddings]
+
+ # 将numpy数组转换为列表
+ embeddings = [embedding.tolist() for embedding in embeddings]
+ prompt_tokens = sum(len(text.split()) for text in request_params.input)
+ total_tokens = sum(num_tokens_from_string(text) for text in request_params.input)
+
+ response = {
+ "data": [
+ {"embedding": embedding, "index": index, "object": "embedding"}
+ for index, embedding in enumerate(embeddings)
+ ],
+ "model": request_params.model,
+ "object": "list",
+ "usage": {
+ "prompt_tokens": prompt_tokens,
+ "total_tokens": total_tokens,
+ },
+ }
+ # print(response)
+ return response
+
+
+app = create_app()
+
+if __name__ == '__main__':
+ use_emb = False
+ try:
+ import ngrok
+ import logging
+
+ logging.basicConfig(level=logging.INFO)
+ listener = ngrok.werkzeug_develop()
+ except Exception:
+ pass
+
+ embeddings_model_m3e = SentenceTransformer('.\\m3e-base', device='cpu') if use_emb else None
+ embeddings_model_bge = SentenceTransformer('.\\bge-base-en-v1.5', device='cpu') if use_emb else None
+
+ app.run(debug=False, host="0.0.0.0", port=8000)
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..3971f4a
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,28 @@
+datasets==2.16.1
+datasketch==1.6.4
+flash_attn==2.5.0
+Flask==3.0.3
+Flask_Cors==4.0.0
+jieba==0.42.1
+jsonlines==4.0.0
+marshmallow==3.22.0
+matplotlib==3.5.1
+ngrok==1.4.0
+nltk==3.8
+numpy==2.1.0
+openai==1.42.0
+pandas==1.5.3
+peft==0.7.1
+psutil==5.9.7
+psutil==5.9.8
+pydantic==2.8.2
+rich==13.7.1
+scikit_learn==1.5.1
+sentence_transformers==2.3.1
+simhash==2.1.2
+tiktoken==0.5.1
+torch==2.1.2+cu121
+transformers==4.44.0
+jinja2==3.0.2
+trl==0.8.6
+ujson==5.1.0
diff --git a/train_tokenizer.py b/train_tokenizer.py
new file mode 100644
index 0000000..cee2ce3
--- /dev/null
+++ b/train_tokenizer.py
@@ -0,0 +1,162 @@
+import random
+from tqdm import tqdm
+from transformers import AutoTokenizer
+import json
+from datasets import load_dataset
+from tokenizers import (
+ decoders,
+ models,
+ normalizers,
+ pre_tokenizers,
+ processors,
+ trainers,
+ Tokenizer,
+)
+import os
+
+random.seed(42)
+
+def train_tokenizer():
+ # 读取JSONL文件并提取文本数据
+ def read_texts_from_jsonl(file_path):
+ with open(file_path, 'r', encoding='utf-8') as f:
+ for line in f:
+ data = json.loads(line)
+ yield data['text']
+
+ data_path = './dataset/tokenizer_train.jsonl'
+
+ # 初始化tokenizer
+ tokenizer = Tokenizer(models.BPE())
+ tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False)
+
+ # 定义特殊token
+ special_tokens = ["", ""]
+
+ # 设置训练器并添加特殊token
+ trainer = trainers.BpeTrainer(
+ vocab_size=6400,
+ special_tokens=special_tokens, # 确保这三个token被包含
+ show_progress=True,
+ initial_alphabet=pre_tokenizers.ByteLevel.alphabet()
+ )
+
+ # 读取文本数据
+ texts = read_texts_from_jsonl(data_path)
+
+ # 训练tokenizer
+ tokenizer.train_from_iterator(texts, trainer=trainer)
+
+ # 设置解码器
+ tokenizer.decoder = decoders.ByteLevel()
+
+ # 检查特殊token的索引
+ assert tokenizer.token_to_id("") == 1
+ assert tokenizer.token_to_id("") == 2
+
+ # 保存tokenizer
+ tokenizer_dir = "./minimind_tokenizer"
+ os.makedirs(tokenizer_dir, exist_ok=True)
+ tokenizer.save(os.path.join(tokenizer_dir, "tokenizer.json"))
+ tokenizer.model.save("./minimind_tokenizer")
+
+ # 手动创建配置文件
+ config = {
+ "add_bos_token": False,
+ "add_eos_token": False,
+ "add_prefix_space": True,
+ "added_tokens_decoder": {
+ "0": {
+ "content": "",
+ "lstrip": False,
+ "normalized": False,
+ "rstrip": False,
+ "single_word": False,
+ "special": True
+ },
+ "2": {
+ "content": "",
+ "lstrip": False,
+ "normalized": False,
+ "rstrip": False,
+ "single_word": False,
+ "special": True
+ }
+ },
+ "additional_special_tokens": [],
+ "bos_token": "",
+ "clean_up_tokenization_spaces": False,
+ "eos_token": "",
+ "legacy": True,
+ "model_max_length": 1000000000000000019884624838656,
+ "pad_token": None,
+ "sp_model_kwargs": {},
+ "spaces_between_special_tokens": False,
+ "tokenizer_class": "PreTrainedTokenizerFast",
+ "unk_token": "user\\n' + content + '\\nassistant\\n' }}{% elif message['role'] == 'assistant' %}{{ content + '' + '\\n' }}{% endif %}{% endfor %}"
+ }
+
+ # 保存配置文件
+ with open(os.path.join(tokenizer_dir, "tokenizer_config.json"), "w", encoding="utf-8") as config_file:
+ json.dump(config, config_file, ensure_ascii=False, indent=4)
+
+ print("Tokenizer training completed and saved.")
+
+
+def eval_tokenizer():
+ from transformers import AutoTokenizer
+
+ # 加载预训练的tokenizer
+ tokenizer = AutoTokenizer.from_pretrained("./model/minimind_tokenizer")
+
+ messages = [
+ {"role": "system", "content": "你是一个优秀的聊天机器人,总是给我正确的回应!"},
+ {"role": "user", "content": '是椭圆形的'},
+ {"role": "assistant", "content": '456'},
+ {"role": "user", "content": '456'},
+ {"role": "assistant", "content": '789'}
+ ]
+ new_prompt = tokenizer.apply_chat_template(
+ messages,
+ tokenize=False
+ )
+
+ print(new_prompt)
+ # 获取词汇表大小(不包括特殊符号)
+ print('tokenizer词表大小:', tokenizer.vocab_size)
+
+ # 获取实际词汇表长度(包括特殊符号)
+ actual_vocab_size = len(tokenizer)
+ print('qwen实际词表长度:', actual_vocab_size)
+
+ new_prompt = 'wenjie,椭圆和⚪的关系是什么呢?因为明天下午要带家人去下医院,所以申请上午在家办公,因为明天下午要带家人去下医院,所以申请上午在家办公,因为明天下午要带家人去下医院,所以申请上午在家办公,下午请半天假~@LWJWe '
+ print(new_prompt)
+ model_inputs = tokenizer(new_prompt)
+
+ print(model_inputs)
+ print('长度:', len(model_inputs['input_ids']))
+
+ input_ids_ = model_inputs['input_ids']
+
+ response = tokenizer.decode(input_ids_)
+ print(response, end='')
+
+
+def main():
+ # train_tokenizer()
+ eval_tokenizer()
+
+
+if __name__ == '__main__':
+ main()