删除了过时的教程和测试文件

6 years ago · 63e023b2fa
--- a/test/test_tutorials.py
+++ b/test/test_tutorials.py
@@ -157,284 +157,6 @@ class TestTutorial(unittest.TestCase):
        trainer.train()
        print('Train finished!')

    def test_fastnlp_advanced_tutorial(self):
        import os
        os.chdir("test/tutorials/fastnlp_advanced_tutorial")

        from fastNLP import DataSet
        from fastNLP import Instance
        from fastNLP import Vocabulary
        from fastNLP import Trainer
        from fastNLP import Tester

        # ### Instance
        # Instance表示一个样本，由一个或者多个field（域、属性、特征）组成，每个field具有自己的名字以及值
        # 在初始化Instance的时候可以定义它包含的field，使用"field_name=field_value"的写法

        # In[2]:

        # 组织一个Instance，这个Instance由premise、hypothesis、label三个field组成
        instance = Instance(premise='an premise example .', hypothesis='an hypothesis example.', label=1)
        instance

        # In[3]:

        data_set = DataSet([instance] * 5)
        data_set.append(instance)
        data_set[-2:]

        # In[4]:

        # 如果某一个field的类型与dataset对应的field类型不一样仍可被加入dataset中
        instance2 = Instance(premise='the second premise example .', hypothesis='the second hypothesis example.',
                             label='1')
        try:
            data_set.append(instance2)
        except:
            pass
        data_set[-2:]

        # In[5]:

        # 如果某一个field的名字不对，则该instance不能被append到dataset中
        instance3 = Instance(premises='the third premise example .', hypothesis='the third hypothesis example.',
                             label=1)
        try:
            data_set.append(instance3)
        except:
            print('cannot append instance')
            pass
        data_set[-2:]

        # In[6]:

        # 除了文本以外，还可以将tensor作为其中一个field的value
        import torch
        tensor_ins = Instance(image=torch.randn(5, 5), label=0)
        ds = DataSet()
        ds.append(tensor_ins)
        ds

        from fastNLP import DataSet
        from fastNLP import Instance

        # 从csv读取数据到DataSet
        # 类csv文件，即每一行为一个example的文件，都可以使用这种方法进行数据读取
        dataset = DataSet.read_csv('tutorial_sample_dataset.csv', headers=('raw_sentence', 'label'), sep='\t')
        # 查看DataSet的大小
        len(dataset)

        # In[8]:

        # 使用数字索引[k]，获取第k个样本
        dataset[0]

        # In[9]:

        # 获取的样本是一个Instance
        type(dataset[0])

        # In[10]:

        # 使用数字索引[a: b]，获取第a到第b个样本
        dataset[0: 3]

        # In[11]:

        # 索引也可以是负数
        dataset[-1]

        data_path = ['premise', 'hypothesis', 'label']

        # 读入文件
        with open(data_path[0]) as f:
            premise = f.readlines()

        with open(data_path[1]) as f:
            hypothesis = f.readlines()

        with open(data_path[2]) as f:
            label = f.readlines()

        assert len(premise) == len(hypothesis) and len(hypothesis) == len(label)

        # 组织DataSet
        data_set = DataSet()
        for p, h, l in zip(premise, hypothesis, label):
            p = p.strip()  # 将行末空格去除
            h = h.strip()  # 将行末空格去除
            data_set.append(Instance(premise=p, hypothesis=h, truth=l))

        data_set[0]

        # ### DataSet的其他操作
        # 在构建完毕DataSet后，仍然可以对DataSet的内容进行操作，函数接口为DataSet.apply()

        # In[13]:

        # 将premise域的所有文本转成小写
        data_set.apply(lambda x: x['premise'].lower(), new_field_name='premise')
        data_set[-2:]

        # In[14]:

        # label转int
        data_set.apply(lambda x: int(x['truth']), new_field_name='truth')
        data_set[-2:]

        # In[15]:

        # 使用空格分割句子
        def split_sent(ins):
            return ins['premise'].split()

        data_set.apply(split_sent, new_field_name='premise')
        data_set.apply(lambda x: x['hypothesis'].split(), new_field_name='hypothesis')
        data_set[-2:]

        # In[16]:

        # 筛选数据
        origin_data_set_len = len(data_set)
        data_set.drop(lambda x: len(x['premise']) <= 6, inplace=True)
        origin_data_set_len, len(data_set)

        # In[17]:

        # 增加长度信息
        data_set.apply(lambda x: [1] * len(x['premise']), new_field_name='premise_len')
        data_set.apply(lambda x: [1] * len(x['hypothesis']), new_field_name='hypothesis_len')
        data_set[-1]

        # In[18]:

        # 设定特征域、标签域
        data_set.set_input("premise", "premise_len", "hypothesis", "hypothesis_len")
        data_set.set_target("truth")

        # In[19]:

        # 重命名field
        data_set.rename_field('truth', 'label')
        data_set[-1]

        # In[20]:

        # 切分训练、验证集、测试集
        train_data, vad_data = data_set.split(0.5)
        dev_data, test_data = vad_data.split(0.4)
        len(train_data), len(dev_data), len(test_data)

        # In[21]:

        # 深拷贝一个数据集
        import copy
        train_data_2, dev_data_2 = copy.deepcopy(train_data), copy.deepcopy(dev_data)
        del copy

        # 初始化词表，该词表最大的vocab_size为10000，词表中每个词出现的最低频率为2，'<unk>'表示未知词语，'<pad>'表示padding词语
        # Vocabulary默认初始化参数为max_size=None, min_freq=None, unknown='<unk>', padding='<pad>'
        vocab = Vocabulary(max_size=10000, min_freq=2, unknown='<unk>', padding='<pad>')

        # 构建词表
        train_data.apply(lambda x: [vocab.add(word) for word in x['premise']])
        train_data.apply(lambda x: [vocab.add(word) for word in x['hypothesis']])
        vocab.build_vocab()

        # In[23]:

        # 根据词表index句子
        train_data.apply(lambda x: [vocab.to_index(word) for word in x['premise']], new_field_name='premise')
        train_data.apply(lambda x: [vocab.to_index(word) for word in x['hypothesis']], new_field_name='hypothesis')
        dev_data.apply(lambda x: [vocab.to_index(word) for word in x['premise']], new_field_name='premise')
        dev_data.apply(lambda x: [vocab.to_index(word) for word in x['hypothesis']], new_field_name='hypothesis')
        test_data.apply(lambda x: [vocab.to_index(word) for word in x['premise']], new_field_name='premise')
        test_data.apply(lambda x: [vocab.to_index(word) for word in x['hypothesis']], new_field_name='hypothesis')
        train_data[-1], dev_data[-1], test_data[-1]

        # 读入vocab文件
        with open('vocab.txt', encoding='utf-8') as f:
            lines = f.readlines()
        vocabs = []
        for line in lines:
            vocabs.append(line.strip())

        # 实例化Vocabulary
        vocab_bert = Vocabulary(unknown=None, padding=None)
        # 将vocabs列表加入Vocabulary
        vocab_bert.add_word_lst(vocabs)
        # 构建词表
        vocab_bert.build_vocab()
        # 更新unknown与padding的token文本
        vocab_bert.unknown = '[UNK]'
        vocab_bert.padding = '[PAD]'

        # In[25]:

        # 根据词表index句子
        train_data_2.apply(lambda x: [vocab_bert.to_index(word) for word in x['premise']], new_field_name='premise')
        train_data_2.apply(lambda x: [vocab_bert.to_index(word) for word in x['hypothesis']],
                           new_field_name='hypothesis')
        dev_data_2.apply(lambda x: [vocab_bert.to_index(word) for word in x['premise']], new_field_name='premise')
        dev_data_2.apply(lambda x: [vocab_bert.to_index(word) for word in x['hypothesis']], new_field_name='hypothesis')
        train_data_2[-1], dev_data_2[-1]

        for data in [train_data, dev_data, test_data]:
            data.rename_field('premise', 'words1')
            data.rename_field('hypothesis', 'words2')
            data.rename_field('premise_len', 'seq_len1')
            data.rename_field('hypothesis_len', 'seq_len2')
            data.set_input('words1', 'words2', 'seq_len1', 'seq_len2')


        # step 1：加载模型参数（非必选）
        from fastNLP.io.config_io import ConfigSection, ConfigLoader
        args = ConfigSection()
        ConfigLoader().load_config("./data/config", {"esim_model": args})
        args["vocab_size"] = len(vocab)
        args.data

        # In[27]:

        # step 2：加载ESIM模型
        from fastNLP.models import ESIM
        model = ESIM(**args.data)
        model

        # In[28]:

        # 另一个例子：加载CNN文本分类模型
        from fastNLP.models import CNNText
        cnn_text_model = CNNText((len(vocab), 50), num_classes=5, padding=2, dropout=0.1)

        from fastNLP import CrossEntropyLoss
        from fastNLP import Adam
        from fastNLP import AccuracyMetric
        trainer = Trainer(
            train_data=train_data,
            model=model,
            loss=CrossEntropyLoss(pred='pred', target='label'),
            metrics=AccuracyMetric(target='label'),
            n_epochs=3,
            batch_size=16,
            print_every=-1,
            validate_every=-1,
            dev_data=dev_data,
            optimizer=Adam(lr=1e-3, weight_decay=0),
            check_code_level=-1,
            metric_key='acc',
            use_tqdm=False,
        )
        trainer.train()

        tester = Tester(
            data=test_data,
            model=model,
            metrics=AccuracyMetric(target='label'),
            batch_size=args["batch_size"],
        )
        tester.test()

    def setUp(self):
        import os
        self._init_wd = os.path.abspath(os.curdir)
--- a/tutorials/fastnlp_advanced_tutorial/advance_tutorial.ipynb
+++ b/tutorials/fastnlp_advanced_tutorial/advance_tutorial.ipynb
--- a/tutorials/fastnlp_advanced_tutorial/data/config
+++ b/tutorials/fastnlp_advanced_tutorial/data/config
@@ -1,8 +0,0 @@
 [esim_model]
 embed_dim = 300
 hidden_size = 300
 batch_first = true
 dropout = 0.3
 num_classes = 3
 gpu = true
 batch_size = 32
--- a/tutorials/fastnlp_advanced_tutorial/hypothesis
+++ b/tutorials/fastnlp_advanced_tutorial/hypothesis
@@ -1,100 +0,0 @@
 A person is training his horse for a competition .
 A person is at a diner , ordering an omelette .
 A person is outdoors , on a horse .
 They are smiling at their parents
 There are children present
 The kids are frowning
 The boy skates down the sidewalk .
 The boy does a skateboarding trick .
 The boy is wearing safety equipment .
 An older man drinks his juice as he waits for his daughter to get off work .
 A boy flips a burger .
 An elderly man sits in a small shop .
 Some women are hugging on vacation .
 The women are sleeping .
 There are women showing affection .
 The people are eating omelettes .
 The people are sitting at desks in school .
 The diners are at a restaurant .
 A man is drinking juice .
 Two women are at a restaurant drinking wine .
 A man in a restaurant is waiting for his meal to arrive .
 A blond man getting a drink of water from a fountain in the park .
 A blond man wearing a brown shirt is reading a book on a bench in the park
 A blond man drinking water from a fountain .
 The friends scowl at each other over a full dinner table .
 There are two woman in this picture .
 The friends have just met for the first time in 20 years , and have had a great time catching up .
 The two sisters saw each other across the crowded diner and shared a hug , both clutching their doggie bags .
 Two groups of rival gang members flipped each other off .
 Two women hug each other .
 A team is trying to score the games winning out .
 A team is trying to tag a runner out .
 A team is playing baseball on Saturn .
 A school hosts a basketball game .
 A high school is hosting an event .
 A school is hosting an event .
 The women do not care what clothes they wear .
 Women are waiting by a tram .
 The women enjoy having a good fashion sense .
 A child with mom and dad , on summer vacation at the beach .
 A family of three is at the beach .
 A family of three is at the mall shopping .
 The people waiting on the train are sitting .
 There are people just getting on a train
 There are people waiting on a train .
 A couple are playing with a young child outside .
 A couple are playing frisbee with a young child at the beach .
 A couple watch a little girl play by herself on the beach .
 The family is sitting down for dinner .
 The family is outside .
 The family is on vacation .
 The people are standing still on the curb .
 Near a couple of restaurants , two people walk across the street .
 The couple are walking across the street together .
 The woman is nake .
 The woman is cold .
 The woman is wearing green .
 The man with the sign is caucasian .
 They are protesting outside the capital .
 A woman in white .
 A man is advertising for a restaurant .
 The woman is wearing black .
 A man and a woman walk down a crowded city street .
 The woman is wearing white .
 They are working for John 's Pizza .
 Olympic swimming .
 A man and a soman are eating together at John 's Pizza and Gyro .
 They are walking with a sign .
 The woman is waiting for a friend .
 The man is sitting down while he has a sign for John 's Pizza and Gyro in his arms .
 The woman and man are outdoors .
 A woman ordering pizza .
 The people are related .
 Two adults run across the street to get away from a red shirted person chasing them .
 The adults are both male and female .
 Two people walk home after a tasty steak dinner .
 Two adults swimming in water
 Two adults walk across a street .
 Two people ride bicycles into a tunnel .
 Two people walk away from a restaurant across a street .
 Two adults walking across a road near the convicted prisoner dressed in red
 Two friends cross a street .
 Some people board a train .
 Two adults walk across the street .
 Two adults walking across a road
 There are no women in the picture .
 Two adults walk across the street to get away from a red shirted person who is chasing them .
 A married couple is sleeping .
 A female is next to a man .
 A married couple is walking next to each other .
 Nobody has food .
 A woman eats a banana and walks across a street , and there is a man trailing behind her .
 The woman and man are playing baseball together .
 two coworkers cross pathes on a street
 A woman eats ice cream walking down the sidewalk , and there is another woman in front of her with a purse .
 The mans briefcase is for work .
 A person eating .
 A person that is hungry .
 An actress and her favorite assistant talk a walk in the city .
 a woman eating a banana crosses a street
--- a/tutorials/fastnlp_advanced_tutorial/label
+++ b/tutorials/fastnlp_advanced_tutorial/label
@@ -1,100 +0,0 @@
 1
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 0
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 0
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 0
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 0
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 0
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 0
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 0
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 1
 0
--- a/tutorials/fastnlp_advanced_tutorial/premise
+++ b/tutorials/fastnlp_advanced_tutorial/premise
@@ -1,100 +0,0 @@
 A person on a horse jumps over a broken down airplane .
 A person on a horse jumps over a broken down airplane .
 A person on a horse jumps over a broken down airplane .
 Children smiling and waving at camera
 Children smiling and waving at camera
 Children smiling and waving at camera
 A boy is jumping on skateboard in the middle of a red bridge .
 A boy is jumping on skateboard in the middle of a red bridge .
 A boy is jumping on skateboard in the middle of a red bridge .
 An older man sits with his orange juice at a small table in a coffee shop while employees in bright colored shirts smile in the background .
 An older man sits with his orange juice at a small table in a coffee shop while employees in bright colored shirts smile in the background .
 An older man sits with his orange juice at a small table in a coffee shop while employees in bright colored shirts smile in the background .
 Two blond women are hugging one another .
 Two blond women are hugging one another .
 Two blond women are hugging one another .
 A few people in a restaurant setting , one of them is drinking orange juice .
 A few people in a restaurant setting , one of them is drinking orange juice .
 A few people in a restaurant setting , one of them is drinking orange juice .
 An older man is drinking orange juice at a restaurant .
 An older man is drinking orange juice at a restaurant .
 An older man is drinking orange juice at a restaurant .
 A man with blond-hair , and a brown shirt drinking out of a public water fountain .
 A man with blond-hair , and a brown shirt drinking out of a public water fountain .
 A man with blond-hair , and a brown shirt drinking out of a public water fountain .
 Two women who just had lunch hugging and saying goodbye .
 Two women who just had lunch hugging and saying goodbye .
 Two women who just had lunch hugging and saying goodbye .
 Two women , holding food carryout containers , hug .
 Two women , holding food carryout containers , hug .
 Two women , holding food carryout containers , hug .
 A Little League team tries to catch a runner sliding into a base in an afternoon game .
 A Little League team tries to catch a runner sliding into a base in an afternoon game .
 A Little League team tries to catch a runner sliding into a base in an afternoon game .
 The school is having a special event in order to show the american culture on how other cultures are dealt with in parties .
 The school is having a special event in order to show the american culture on how other cultures are dealt with in parties .
 The school is having a special event in order to show the american culture on how other cultures are dealt with in parties .
 High fashion ladies wait outside a tram beside a crowd of people in the city .
 High fashion ladies wait outside a tram beside a crowd of people in the city .
 High fashion ladies wait outside a tram beside a crowd of people in the city .
 A man , woman , and child enjoying themselves on a beach .
 A man , woman , and child enjoying themselves on a beach .
 A man , woman , and child enjoying themselves on a beach .
 People waiting to get on a train or just getting off .
 People waiting to get on a train or just getting off .
 People waiting to get on a train or just getting off .
 A couple playing with a little boy on the beach .
 A couple playing with a little boy on the beach .
 A couple playing with a little boy on the beach .
 A couple play in the tide with their young son .
 A couple play in the tide with their young son .
 A couple play in the tide with their young son .
 A man and a woman cross the street in front of a pizza and gyro restaurant .
 A man and a woman cross the street in front of a pizza and gyro restaurant .
 A man and a woman cross the street in front of a pizza and gyro restaurant .
 A woman in a green jacket and hood over her head looking towards a valley .
 A woman in a green jacket and hood over her head looking towards a valley .
 A woman in a green jacket and hood over her head looking towards a valley .
 Woman in white in foreground and a man slightly behind walking with a sign for John 's Pizza and Gyro in the background .
 Woman in white in foreground and a man slightly behind walking with a sign for John 's Pizza and Gyro in the background .
 Woman in white in foreground and a man slightly behind walking with a sign for John 's Pizza and Gyro in the background .
 Woman in white in foreground and a man slightly behind walking with a sign for John 's Pizza and Gyro in the background .
 Woman in white in foreground and a man slightly behind walking with a sign for John 's Pizza and Gyro in the background .
 Woman in white in foreground and a man slightly behind walking with a sign for John 's Pizza and Gyro in the background .
 Woman in white in foreground and a man slightly behind walking with a sign for John 's Pizza and Gyro in the background .
 Woman in white in foreground and a man slightly behind walking with a sign for John 's Pizza and Gyro in the background .
 Woman in white in foreground and a man slightly behind walking with a sign for John 's Pizza and Gyro in the background .
 Woman in white in foreground and a man slightly behind walking with a sign for John 's Pizza and Gyro in the background .
 Woman in white in foreground and a man slightly behind walking with a sign for John 's Pizza and Gyro in the background .
 Woman in white in foreground and a man slightly behind walking with a sign for John 's Pizza and Gyro in the background .
 Woman in white in foreground and a man slightly behind walking with a sign for John 's Pizza and Gyro in the background .
 Woman in white in foreground and a man slightly behind walking with a sign for John 's Pizza and Gyro in the background .
 Woman in white in foreground and a man slightly behind walking with a sign for John 's Pizza and Gyro in the background .
 Two adults , one female in white , with shades and one male , gray clothes , walking across a street , away from a eatery with a blurred image of a dark colored red shirted person in the foreground .
 Two adults , one female in white , with shades and one male , gray clothes , walking across a street , away from a eatery with a blurred image of a dark colored red shirted person in the foreground .
 Two adults , one female in white , with shades and one male , gray clothes , walking across a street , away from a eatery with a blurred image of a dark colored red shirted person in the foreground .
 Two adults , one female in white , with shades and one male , gray clothes , walking across a street , away from a eatery with a blurred image of a dark colored red shirted person in the foreground .
 Two adults , one female in white , with shades and one male , gray clothes , walking across a street , away from a eatery with a blurred image of a dark colored red shirted person in the foreground .
 Two adults , one female in white , with shades and one male , gray clothes , walking across a street , away from a eatery with a blurred image of a dark colored red shirted person in the foreground .
 Two adults , one female in white , with shades and one male , gray clothes , walking across a street , away from a eatery with a blurred image of a dark colored red shirted person in the foreground .
 Two adults , one female in white , with shades and one male , gray clothes , walking across a street , away from a eatery with a blurred image of a dark colored red shirted person in the foreground .
 Two adults , one female in white , with shades and one male , gray clothes , walking across a street , away from a eatery with a blurred image of a dark colored red shirted person in the foreground .
 Two adults , one female in white , with shades and one male , gray clothes , walking across a street , away from a eatery with a blurred image of a dark colored red shirted person in the foreground .
 Two adults , one female in white , with shades and one male , gray clothes , walking across a street , away from a eatery with a blurred image of a dark colored red shirted person in the foreground .
 Two adults , one female in white , with shades and one male , gray clothes , walking across a street , away from a eatery with a blurred image of a dark colored red shirted person in the foreground .
 Two adults , one female in white , with shades and one male , gray clothes , walking across a street , away from a eatery with a blurred image of a dark colored red shirted person in the foreground .
 Two adults , one female in white , with shades and one male , gray clothes , walking across a street , away from a eatery with a blurred image of a dark colored red shirted person in the foreground .
 Two adults , one female in white , with shades and one male , gray clothes , walking across a street , away from a eatery with a blurred image of a dark colored red shirted person in the foreground .
 A woman wearing all white and eating , walks next to a man holding a briefcase .
 A woman wearing all white and eating , walks next to a man holding a briefcase .
 A woman wearing all white and eating , walks next to a man holding a briefcase .
 A woman is walking across the street eating a banana , while a man is following with his briefcase .
 A woman is walking across the street eating a banana , while a man is following with his briefcase .
 A woman is walking across the street eating a banana , while a man is following with his briefcase .
 A woman is walking across the street eating a banana , while a man is following with his briefcase .
 A woman is walking across the street eating a banana , while a man is following with his briefcase .
 A woman is walking across the street eating a banana , while a man is following with his briefcase .
 A woman is walking across the street eating a banana , while a man is following with his briefcase .
 A woman is walking across the street eating a banana , while a man is following with his briefcase .
 A woman is walking across the street eating a banana , while a man is following with his briefcase .
 A woman is walking across the street eating a banana , while a man is following with his briefcase .
--- a/tutorials/fastnlp_advanced_tutorial/tutorial_sample_dataset.csv
+++ b/tutorials/fastnlp_advanced_tutorial/tutorial_sample_dataset.csv
@@ -1,77 +0,0 @@
 A series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story .	1
 This quiet , introspective and entertaining independent is worth seeking .	4
 Even fans of Ismail Merchant 's work , I suspect , would have a hard time sitting through this one .	1
 A positively thrilling combination of ethnography and all the intrigue , betrayal , deceit and murder of a Shakespearean tragedy or a juicy soap opera .	3
 Aggressive self-glorification and a manipulative whitewash .	1
 A comedy-drama of nearly epic proportions rooted in a sincere performance by the title character undergoing midlife crisis .	4
 Narratively , Trouble Every Day is a plodding mess .	1
 The Importance of Being Earnest , so thick with wit it plays like a reading from Bartlett 's Familiar Quotations	3
 But it does n't leave you with much .	1
 You could hate it for the same reason .	1
 There 's little to recommend Snow Dogs , unless one considers cliched dialogue and perverse escapism a source of high hilarity .	1
 Kung Pow is Oedekerk 's realization of his childhood dream to be in a martial-arts flick , and proves that sometimes the dreams of youth should remain just that .	1
 The performances are an absolute joy .	4
 Fresnadillo has something serious to say about the ways in which extravagant chance can distort our perspective and throw us off the path of good sense .	3
 I still like Moonlight Mile , better judgment be damned .	3
 A welcome relief from baseball movies that try too hard to be mythic , this one is a sweet and modest and ultimately winning story .	3
 a bilingual charmer , just like the woman who inspired it	3
 Like a less dizzily gorgeous companion to Mr. Wong 's In the Mood for Love -- very much a Hong Kong movie despite its mainland setting .	2
 As inept as big-screen remakes of The Avengers and The Wild Wild West .	1
 It 's everything you 'd expect -- but nothing more .	2
 Best indie of the year , so far .	4
 Hatfield and Hicks make the oddest of couples , and in this sense the movie becomes a study of the gambles of the publishing world , offering a case study that exists apart from all the movie 's political ramifications .	3
 It 's like going to a house party and watching the host defend himself against a frothing ex-girlfriend .	1
 That the Chuck Norris `` grenade gag '' occurs about 7 times during Windtalkers is a good indication of how serious-minded the film is .	2
 The plot is romantic comedy boilerplate from start to finish .	2
 It arrives with an impeccable pedigree , mongrel pep , and almost indecipherable plot complications .	2
 A film that clearly means to preach exclusively to the converted .	2
 While The Importance of Being Earnest offers opportunities for occasional smiles and chuckles , it does n't give us a reason to be in the theater beyond Wilde 's wit and the actors ' performances .	1
 The latest vapid actor 's exercise to appropriate the structure of Arthur Schnitzler 's Reigen .	1
 More vaudeville show than well-constructed narrative , but on those terms it 's inoffensive and actually rather sweet .	2
 Nothing more than a run-of-the-mill action flick .	2
 Hampered -- no , paralyzed -- by a self-indulgent script ... that aims for poetry and ends up sounding like satire .	0
 Ice Age is the first computer-generated feature cartoon to feel like other movies , and that makes for some glacial pacing early on .	2
 There 's very little sense to what 's going on here , but the makers serve up the cliches with considerable dash .	2
 Cattaneo should have followed the runaway success of his first film , The Full Monty , with something different .	2
 They 're the unnamed , easily substitutable forces that serve as whatever terror the heroes of horror movies try to avoid .	1
 It almost feels as if the movie is more interested in entertaining itself than in amusing us .	1
 The movie 's progression into rambling incoherence gives new meaning to the phrase ` fatal script error . '	0
 I still like Moonlight Mile , better judgment be damned .	3
 A welcome relief from baseball movies that try too hard to be mythic , this one is a sweet and modest and ultimately winning story .	3
 a bilingual charmer , just like the woman who inspired it	3
 Like a less dizzily gorgeous companion to Mr. Wong 's In the Mood for Love -- very much a Hong Kong movie despite its mainland setting .	2
 As inept as big-screen remakes of The Avengers and The Wild Wild West .	1
 It 's everything you 'd expect -- but nothing more .	2
 Best indie of the year , so far .	4
 Hatfield and Hicks make the oddest of couples , and in this sense the movie becomes a study of the gambles of the publishing world , offering a case study that exists apart from all the movie 's political ramifications .	3
 It 's like going to a house party and watching the host defend himself against a frothing ex-girlfriend .	1
 That the Chuck Norris `` grenade gag '' occurs about 7 times during Windtalkers is a good indication of how serious-minded the film is .	2
 The plot is romantic comedy boilerplate from start to finish .	2
 It arrives with an impeccable pedigree , mongrel pep , and almost indecipherable plot complications .	2
 A film that clearly means to preach exclusively to the converted .	2
 I still like Moonlight Mile , better judgment be damned .	3
 A welcome relief from baseball movies that try too hard to be mythic , this one is a sweet and modest and ultimately winning story .	3
 a bilingual charmer , just like the woman who inspired it	3
 Like a less dizzily gorgeous companion to Mr. Wong 's In the Mood for Love -- very much a Hong Kong movie despite its mainland setting .	2
 As inept as big-screen remakes of The Avengers and The Wild Wild West .	1
 It 's everything you 'd expect -- but nothing more .	2
 Best indie of the year , so far .	4
 Hatfield and Hicks make the oddest of couples , and in this sense the movie becomes a study of the gambles of the publishing world , offering a case study that exists apart from all the movie 's political ramifications .	3
 It 's like going to a house party and watching the host defend himself against a frothing ex-girlfriend .	1
 That the Chuck Norris `` grenade gag '' occurs about 7 times during Windtalkers is a good indication of how serious-minded the film is .	2
 The plot is romantic comedy boilerplate from start to finish .	2
 It arrives with an impeccable pedigree , mongrel pep , and almost indecipherable plot complications .	2
 A film that clearly means to preach exclusively to the converted .	2
 I still like Moonlight Mile , better judgment be damned .	3
 A welcome relief from baseball movies that try too hard to be mythic , this one is a sweet and modest and ultimately winning story .	3
 a bilingual charmer , just like the woman who inspired it	3
 Like a less dizzily gorgeous companion to Mr. Wong 's In the Mood for Love -- very much a Hong Kong movie despite its mainland setting .	2
 As inept as big-screen remakes of The Avengers and The Wild Wild West .	1
 It 's everything you 'd expect -- but nothing more .	2
 Best indie of the year , so far .	4
 Hatfield and Hicks make the oddest of couples , and in this sense the movie becomes a study of the gambles of the publishing world , offering a case study that exists apart from all the movie 's political ramifications .	3
 It 's like going to a house party and watching the host defend himself against a frothing ex-girlfriend .	1
 That the Chuck Norris `` grenade gag '' occurs about 7 times during Windtalkers is a good indication of how serious-minded the film is .	2
 The plot is romantic comedy boilerplate from start to finish .	2
 It arrives with an impeccable pedigree , mongrel pep , and almost indecipherable plot complications .	2
 A film that clearly means to preach exclusively to the converted .	2
--- a/tutorials/fastnlp_advanced_tutorial/vocab.txt
+++ b/tutorials/fastnlp_advanced_tutorial/vocab.txt