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删除了过时的教程和测试文件

tags/v0.4.10
ChenXin 6 years ago
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8 changed files with 0 additions and 32302 deletions
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    -278
      test/test_tutorials.py
  2. +0
    -1117
      tutorials/fastnlp_advanced_tutorial/advance_tutorial.ipynb
  3. +0
    -8
      tutorials/fastnlp_advanced_tutorial/data/config
  4. +0
    -100
      tutorials/fastnlp_advanced_tutorial/hypothesis
  5. +0
    -100
      tutorials/fastnlp_advanced_tutorial/label
  6. +0
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      tutorials/fastnlp_advanced_tutorial/premise
  7. +0
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      tutorials/fastnlp_advanced_tutorial/tutorial_sample_dataset.csv
  8. +0
    -30522
      tutorials/fastnlp_advanced_tutorial/vocab.txt

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test/test_tutorials.py View File

@@ -157,284 +157,6 @@ class TestTutorial(unittest.TestCase):
trainer.train() trainer.train()
print('Train finished!') 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): def setUp(self):
import os import os
self._init_wd = os.path.abspath(os.curdir) self._init_wd = os.path.abspath(os.curdir)


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tutorials/fastnlp_advanced_tutorial/advance_tutorial.ipynb
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tutorials/fastnlp_advanced_tutorial/data/config View File

@@ -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

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tutorials/fastnlp_advanced_tutorial/hypothesis View File

@@ -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

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tutorials/fastnlp_advanced_tutorial/label View File

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tutorials/fastnlp_advanced_tutorial/premise View File

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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 .

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tutorials/fastnlp_advanced_tutorial/tutorial_sample_dataset.csv View File

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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

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