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FastText模型

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训练模型

试用gensim自带的Lee语料库来训练模型

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from pprint import pprint as print
from gensim.models.fasttext import FastText as FT_gensim
from gensim.test.utils import datapath

#设置文件名
corpus_file = datapath('lee_background.cor')

model = FT_gensim(size = 100)

#建立词汇表
model.build_vocab(corpus_file = corpus_file)

#训练模型
model.train(
    corpus_file = corpus_file,epochs = model.epochs,
    total_examples = model.corpus_count,total_words = model.corpus_total_words
)

print(model)
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<gensim.models.fasttext.FastText object at 0x000001B960B8D400>

训练超参数

用于训练模型的超参数遵循与Word2Vec相同的模式。FastText支持来自原始word2vec的以下参数:

  • model: Training architecture. Allowed values: cbow, skipgram (Default cbow)
  • size: Size of embeddings to be learnt (Default 100)
  • alpha: Initial learning rate (Default 0.025)
  • window: Context window size (Default 5)
  • min_count: Ignore words with number of occurrences below this (Default 5)
  • loss: Training objective. Allowed values: ns, hs, softmax (Default ns)
  • sample: Threshold for downsampling higher-frequency words (Default 0.001)
  • negative: Number of negative words to sample, for ns (Default 5)
  • iter: Number of epochs (Default 5)
  • sorted_vocab: Sort vocab by descending frequency (Default 1)
  • threads: Number of threads to use (Default 12)

此外，FastText还有三个附加参数：

  • min_n: min length of char ngrams (Default 3)
  • max_n: max length of char ngrams (Default 6)
  • bucket: number of buckets used for hashing ngrams (Default 2000000)

保存、加载模型

可以通过load和save方法保存和加载模型

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#通过load和save方法保存和加载模型
import tempfile
import os
with tempfile.NamedTemporaryFile(prefix = 'saved_model_gensim-',delete = False) as tmp:
    model.save(tmp.name,separately = [])
    
loaded_model = FT_gensim.load(tmp.name)
print(loaded_model)

os.unlink(tmp.name)
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<gensim.models.fasttext.FastText object at 0x000001B993579198>

词向量查找

FastText模型通过总结属于单词的字符ngrams来支持词汇表外单词的向量查找

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print('night' in model.wv.vocab)
print('nights' in model.wv.vocab)
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True
False
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print(model['night'])
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array([ 0.0953326 ,  0.00227786, -0.63099104,  0.48497984,  0.61077046,
       -0.20671842, -0.2864202 , -0.00851544,  0.36318386,  0.3499794 ,
       -0.67137724, -0.05059881, -0.75054985,  0.39179742,  0.23554917,
       -0.00135564, -0.16808018,  0.27810544,  0.28773504, -0.42447615,
       -0.21546099,  0.39643624, -0.35647205,  0.11427329, -0.8091079 ,
        0.68502676,  0.21528108,  0.13505526,  0.44624037,  0.09882572,
       -0.6161567 ,  0.27545458, -0.00874986, -0.47377393,  0.404326  ,
        0.04802122, -0.18317538, -0.09473048,  0.45228255,  0.22224163,
       -0.00220438, -0.05910338,  0.3433522 ,  0.01136153,  0.12559271,
        0.15975659, -0.13288054,  0.24637105,  0.03247422, -0.38466278,
       -0.5199409 , -0.56950164,  0.04317402,  0.01742636,  0.37354326,
       -0.857144  , -0.13501711, -0.2867473 , -0.0336968 , -0.05738431,
        0.2521108 , -0.03732477, -0.53491974, -0.07037822, -0.564876  ,
        0.21968082,  0.10176989,  0.09708861, -0.01507892,  0.44874096,
       -0.603698  , -0.5272684 ,  0.04438765, -0.11443205, -0.37223595,
        0.13290597,  0.30462274,  0.1901602 , -0.07255627,  0.08489503,
        0.48211902, -0.0182712 , -0.1890541 ,  0.4491388 , -0.29322693,
       -0.33450714,  0.11029933,  0.24498533,  0.377997  ,  0.33057505,
       -0.23820789,  0.10019816,  0.00383096, -0.17659418, -0.25245324,
        0.5648699 ,  0.29140565,  0.5146172 ,  0.5881857 ,  0.38764346],
      dtype=float32)
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print(model['nights'])
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array([ 0.08310547,  0.00255469, -0.5438613 ,  0.41711542,  0.52543885,
       -0.17962681, -0.24739201, -0.00658811,  0.3127566 ,  0.30214837,
       -0.58069485, -0.04493077, -0.64692456,  0.33895043,  0.19910343,
       -0.00081991, -0.14691554,  0.23774558,  0.24622767, -0.366245  ,
       -0.18553475,  0.3425331 , -0.30774567,  0.09903311, -0.69780546,
        0.59015656,  0.18548216,  0.1167931 ,  0.38456377,  0.084683  ,
       -0.5286803 ,  0.23848796, -0.00879034, -0.41040844,  0.34827596,
        0.04112032, -0.15626806, -0.08284618,  0.38985735,  0.19218934,
       -0.0009631 , -0.05129346,  0.29532096,  0.01136862,  0.10713599,
        0.1378806 , -0.11797399,  0.21411985,  0.02733744, -0.32998133,
       -0.45055205, -0.4899713 ,  0.03825624,  0.01596748,  0.32250437,
       -0.7385598 , -0.11651613, -0.24604279, -0.03137114, -0.04910181,
        0.21684855, -0.0329251 , -0.4606339 , -0.0611559 , -0.487312  ,
        0.18886864,  0.08784177,  0.08357754, -0.01117994,  0.38745624,
       -0.52011096, -0.453825  ,  0.03824352, -0.09949207, -0.32059684,
        0.11355332,  0.2626802 ,  0.16410257, -0.06057531,  0.07242386,
        0.4162787 , -0.01578102, -0.16374995,  0.3860768 , -0.25174987,
       -0.28764334,  0.09705427,  0.21252516,  0.3250878 ,  0.28572875,
       -0.2055463 ,  0.08591434,  0.00200712, -0.15187213, -0.21731909,
        0.4869826 ,  0.25010216,  0.44192523,  0.5064299 ,  0.33416855],
      dtype=float32)

测试单词是否出现在词汇表中

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print("word" in model.wv.vocab)
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False

测试单词是否出现在词汇表中

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print("word" in model)
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True

相似操作

相似性操作的工作方式与word2vec相同。也可以使用词汇表外的单词，前提是他们在训练数据中至少有一个字符ngram

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print("nights" in model.wv.vocab)
print("night" in model.wv.vocab)
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False
True
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print(model.similarity("night","nights"))
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0.99999285

其他相似操作

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print(model.most_similar("nights"))
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[('night', 0.999992847442627),
 ('rights', 0.9999881982803345),
 ('flights', 0.9999872446060181),
 ('fighting', 0.9999870657920837),
 ('starting', 0.9999867677688599),
 ('hearing', 0.9999866485595703),
 ('expressed', 0.9999860525131226),
 ('overnight', 0.9999858736991882),
 ('negotiating', 0.9999858140945435),
 ('stand', 0.9999856948852539)]
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print(model.n_similarity(['sushi', 'shop'], ['japanese', 'restaurant']))
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0.9999506
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print(model.doesnt_match("breakfast cereal dinner lunch".split()))
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'lunch'
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print(model.most_similar(positive = ['baghdad','england'],negative = ['london']))
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[('temporary', 0.9996868371963501),
 ('Island', 0.9996848106384277),
 ('Anthony', 0.9996838569641113),
 ('only', 0.9996838569641113),
 ('last', 0.9996830821037292),
 ('Hamas', 0.9996793270111084),
 ('everything', 0.9996790885925293),
 ('met', 0.9996784329414368),
 ('Pentagon', 0.9996781349182129),
 ('campaign', 0.9996780157089233)]
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print(model.accuracy(questions = datapath('questions-words.txt')))
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Jupyter Notebook
FastText
(自动保存)
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代码
from pprint import pprint as print
from gensim.models.fasttext import FastText as FT_gensim
from gensim.test.utils import datapath

#设置文件名
corpus_file = datapath('lee_background.cor')

model = FT_gensim(size = 100)

#建立词汇表
model.build_vocab(corpus_file = corpus_file)

#训练模型
model.train(
    corpus_file = corpus_file,epochs = model.epochs,
    total_examples = model.corpus_count,total_words = model.corpus_total_words
)

print(model)
from pprint import pprint as print
from gensim.models.fasttext import FastText as FT_gensim
from gensim.test.utils import datapath
​
#设置文件名
corpus_file = datapath('lee_background.cor')
​
model = FT_gensim(size = 100)
​
#建立词汇表
model.build_vocab(corpus_file = corpus_file)
​
#训练模型
model.train(
    corpus_file = corpus_file,epochs = model.epochs,
    total_examples = model.corpus_count,total_words = model.corpus_total_words
)
​
print(model)
<gensim.models.fasttext.FastText object at 0x000001B960B8D400>
#通过load和save方法保存和加载模型
import tempfile
import os
with tempfile.NamedTemporaryFile(prefix = 'saved_model_gensim-',delete = False) as tmp:
    model.save(tmp.name,separately = [])
    
loaded_model = FT_gensim.load(tmp.name)
print(loaded_model)

os.unlink(tmp.name)
#通过load和save方法保存和加载模型
import tempfile
import os
with tempfile.NamedTemporaryFile(prefix = 'saved_model_gensim-',delete = False) as tmp:
    model.save(tmp.name,separately = [])
    
loaded_model = FT_gensim.load(tmp.name)
print(loaded_model)
​
os.unlink(tmp.name)
<gensim.models.fasttext.FastText object at 0x000001B993579198>
print('night' in model.wv.vocab)
True
print('nights' in model.wv.vocab)
False
print(model['night'])
array([ 0.0953326 ,  0.00227786, -0.63099104,  0.48497984,  0.61077046,
       -0.20671842, -0.2864202 , -0.00851544,  0.36318386,  0.3499794 ,
       -0.67137724, -0.05059881, -0.75054985,  0.39179742,  0.23554917,
       -0.00135564, -0.16808018,  0.27810544,  0.28773504, -0.42447615,
       -0.21546099,  0.39643624, -0.35647205,  0.11427329, -0.8091079 ,
        0.68502676,  0.21528108,  0.13505526,  0.44624037,  0.09882572,
       -0.6161567 ,  0.27545458, -0.00874986, -0.47377393,  0.404326  ,
        0.04802122, -0.18317538, -0.09473048,  0.45228255,  0.22224163,
       -0.00220438, -0.05910338,  0.3433522 ,  0.01136153,  0.12559271,
        0.15975659, -0.13288054,  0.24637105,  0.03247422, -0.38466278,
       -0.5199409 , -0.56950164,  0.04317402,  0.01742636,  0.37354326,
       -0.857144  , -0.13501711, -0.2867473 , -0.0336968 , -0.05738431,
        0.2521108 , -0.03732477, -0.53491974, -0.07037822, -0.564876  ,
        0.21968082,  0.10176989,  0.09708861, -0.01507892,  0.44874096,
       -0.603698  , -0.5272684 ,  0.04438765, -0.11443205, -0.37223595,
        0.13290597,  0.30462274,  0.1901602 , -0.07255627,  0.08489503,
        0.48211902, -0.0182712 , -0.1890541 ,  0.4491388 , -0.29322693,
       -0.33450714,  0.11029933,  0.24498533,  0.377997  ,  0.33057505,
       -0.23820789,  0.10019816,  0.00383096, -0.17659418, -0.25245324,
        0.5648699 ,  0.29140565,  0.5146172 ,  0.5881857 ,  0.38764346],
      dtype=float32)
D:\anaconda\anaconda3\lib\site-packages\ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).
  """Entry point for launching an IPython kernel.
print(model['nights'])
array([ 0.08310547,  0.00255469, -0.5438613 ,  0.41711542,  0.52543885,
       -0.17962681, -0.24739201, -0.00658811,  0.3127566 ,  0.30214837,
       -0.58069485, -0.04493077, -0.64692456,  0.33895043,  0.19910343,
       -0.00081991, -0.14691554,  0.23774558,  0.24622767, -0.366245  ,
       -0.18553475,  0.3425331 , -0.30774567,  0.09903311, -0.69780546,
        0.59015656,  0.18548216,  0.1167931 ,  0.38456377,  0.084683  ,
       -0.5286803 ,  0.23848796, -0.00879034, -0.41040844,  0.34827596,
        0.04112032, -0.15626806, -0.08284618,  0.38985735,  0.19218934,
       -0.0009631 , -0.05129346,  0.29532096,  0.01136862,  0.10713599,
        0.1378806 , -0.11797399,  0.21411985,  0.02733744, -0.32998133,
       -0.45055205, -0.4899713 ,  0.03825624,  0.01596748,  0.32250437,
       -0.7385598 , -0.11651613, -0.24604279, -0.03137114, -0.04910181,
        0.21684855, -0.0329251 , -0.4606339 , -0.0611559 , -0.487312  ,
        0.18886864,  0.08784177,  0.08357754, -0.01117994,  0.38745624,
       -0.52011096, -0.453825  ,  0.03824352, -0.09949207, -0.32059684,
        0.11355332,  0.2626802 ,  0.16410257, -0.06057531,  0.07242386,
        0.4162787 , -0.01578102, -0.16374995,  0.3860768 , -0.25174987,
       -0.28764334,  0.09705427,  0.21252516,  0.3250878 ,  0.28572875,
       -0.2055463 ,  0.08591434,  0.00200712, -0.15187213, -0.21731909,
        0.4869826 ,  0.25010216,  0.44192523,  0.5064299 ,  0.33416855],
      dtype=float32)
D:\anaconda\anaconda3\lib\site-packages\ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).
  """Entry point for launching an IPython kernel.
print("word" in model.wv.vocab)
False
n model
print("word" in model)
True
D:\anaconda\anaconda3\lib\site-packages\ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `__contains__` (Method will be removed in 4.0.0, use self.wv.__contains__() instead).
  """Entry point for launching an IPython kernel.
print("nights" in model.wv.vocab)
False
print("night" in model.wv.vocab)
True
print(model.similarity("night","nights"))
0.99999285
D:\anaconda\anaconda3\lib\site-packages\ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `similarity` (Method will be removed in 4.0.0, use self.wv.similarity() instead).
  """Entry point for launching an IPython kernel.
print(model.most_similar("nights"))
print(model.most_similar("nights"))
D:\anaconda\anaconda3\lib\site-packages\ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `most_similar` (Method will be removed in 4.0.0, use self.wv.most_similar() instead).
  """Entry point for launching an IPython kernel.
[('night', 0.999992847442627),
 ('rights', 0.9999881982803345),
 ('flights', 0.9999872446060181),
 ('fighting', 0.9999870657920837),
 ('starting', 0.9999867677688599),
 ('hearing', 0.9999866485595703),
 ('expressed', 0.9999860525131226),
 ('overnight', 0.9999858736991882),
 ('negotiating', 0.9999858140945435),
 ('stand', 0.9999856948852539)]
print(model.n_similarity(['sushi','shop'],['japanese','restaurant']))
0.9999506
D:\anaconda\anaconda3\lib\site-packages\ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `n_similarity` (Method will be removed in 4.0.0, use self.wv.n_similarity() instead).
  """Entry point for launching an IPython kernel.
print(model.doesnt_match("breakfast cereal dinner lunch".split()))
print(model.doesnt_match("breakfast cereal dinner lunch".split()))
D:\anaconda\anaconda3\lib\site-packages\ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `doesnt_match` (Method will be removed in 4.0.0, use self.wv.doesnt_match() instead).
  """Entry point for launching an IPython kernel.
'lunch'
D:\anaconda\anaconda3\lib\site-packages\gensim\models\keyedvectors.py:877: FutureWarning: arrays to stack must be passed as a "sequence" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.
  vectors = vstack(self.word_vec(word, use_norm=True) for word in used_words).astype(REAL)
print(model.most_similar(positive = ['baghdad','england'],negative = ['london']))
print(model.most_similar(positive = ['baghdad','england'],negative = ['london']))
[('temporary', 0.9996868371963501),
 ('Island', 0.9996848106384277),
 ('Anthony', 0.9996838569641113),
 ('only', 0.9996838569641113),
 ('last', 0.9996830821037292),
 ('Hamas', 0.9996793270111084),
 ('everything', 0.9996790885925293),
 ('met', 0.9996784329414368),
 ('Pentagon', 0.9996781349182129),
 ('campaign', 0.9996780157089233)]
D:\anaconda\anaconda3\lib\site-packages\ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `most_similar` (Method will be removed in 4.0.0, use self.wv.most_similar() instead).
  """Entry point for launching an IPython kernel.
print(model.accuracy(questions = datapath('questions-words.txt')))
print(model.accuracy(questions = datapath('questions-words.txt')))
D:\anaconda\anaconda3\lib\site-packages\ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `accuracy` (Method will be removed in 4.0.0, use self.wv.accuracy() instead).
  """Entry point for launching an IPython kernel.
[{'correct': [], 'incorrect': [], 'section': 'capital-common-countries'},
 {'correct': [], 'incorrect': [], 'section': 'capital-world'},
 {'correct': [], 'incorrect': [], 'section': 'currency'},
 {'correct': [], 'incorrect': [], 'section': 'city-in-state'},
 {'correct': [],
  'incorrect': [('HE', 'SHE', 'HIS', 'HER'), ('HIS', 'HER', 'HE', 'SHE')],
  'section': 'family'},
 {'correct': [], 'incorrect': [], 'section': 'gram1-adjective-to-adverb'},
 {'correct': [], 'incorrect': [], 'section': 'gram2-opposite'},
 {'correct': [('GOOD', 'BETTER', 'GREAT', 'GREATER'),
              ('GREAT', 'GREATER', 'LOW', 'LOWER'),
              ('LONG', 'LONGER', 'GREAT', 'GREATER')],
  'incorrect': [('GOOD', 'BETTER', 'LONG', 'LONGER'),
                ('GOOD', 'BETTER', 'LOW', 'LOWER'),
                ('GREAT', 'GREATER', 'LONG', 'LONGER'),
                ('GREAT', 'GREATER', 'GOOD', 'BETTER'),
                ('LONG', 'LONGER', 'LOW', 'LOWER'),
                ('LONG', 'LONGER', 'GOOD', 'BETTER'),
                ('LOW', 'LOWER', 'GOOD', 'BETTER'),
                ('LOW', 'LOWER', 'GREAT', 'GREATER'),
                ('LOW', 'LOWER', 'LONG', 'LONGER')],
  'section': 'gram3-comparative'},
 {'correct': [('GREAT', 'GREATEST', 'LARGE', 'LARGEST')],
  'incorrect': [('BIG', 'BIGGEST', 'GOOD', 'BEST'),
                ('BIG', 'BIGGEST', 'GREAT', 'GREATEST'),
                ('BIG', 'BIGGEST', 'LARGE', 'LARGEST'),
                ('GOOD', 'BEST', 'GREAT', 'GREATEST'),
                ('GOOD', 'BEST', 'LARGE', 'LARGEST'),
                ('GOOD', 'BEST', 'BIG', 'BIGGEST'),
                ('GREAT', 'GREATEST', 'BIG', 'BIGGEST'),
                ('GREAT', 'GREATEST', 'GOOD', 'BEST'),
                ('LARGE', 'LARGEST', 'BIG', 'BIGGEST'),
                ('LARGE', 'LARGEST', 'GOOD', 'BEST'),
                ('LARGE', 'LARGEST', 'GREAT', 'GREATEST')],
  'section': 'gram4-superlative'},
 {'correct': [('LOOK', 'LOOKING', 'SAY', 'SAYING'),
              ('PLAY', 'PLAYING', 'SAY', 'SAYING'),
              ('PLAY', 'PLAYING', 'LOOK', 'LOOKING'),
              ('SAY', 'SAYING', 'LOOK', 'LOOKING'),
              ('SAY', 'SAYING', 'PLAY', 'PLAYING')],
  'incorrect': [('GO', 'GOING', 'LOOK', 'LOOKING'),
                ('GO', 'GOING', 'PLAY', 'PLAYING'),
                ('GO', 'GOING', 'RUN', 'RUNNING'),
                ('GO', 'GOING', 'SAY', 'SAYING'),
                ('LOOK', 'LOOKING', 'PLAY', 'PLAYING'),
                ('LOOK', 'LOOKING', 'RUN', 'RUNNING'),
                ('LOOK', 'LOOKING', 'GO', 'GOING'),
                ('PLAY', 'PLAYING', 'RUN', 'RUNNING'),
                ('PLAY', 'PLAYING', 'GO', 'GOING'),
                ('RUN', 'RUNNING', 'SAY', 'SAYING'),
                ('RUN', 'RUNNING', 'GO', 'GOING'),
                ('RUN', 'RUNNING', 'LOOK', 'LOOKING'),
                ('RUN', 'RUNNING', 'PLAY', 'PLAYING'),
                ('SAY', 'SAYING', 'GO', 'GOING'),
                ('SAY', 'SAYING', 'RUN', 'RUNNING')],
  'section': 'gram5-present-participle'},
 {'correct': [('AUSTRALIA', 'AUSTRALIAN', 'INDIA', 'INDIAN'),
              ('AUSTRALIA', 'AUSTRALIAN', 'ISRAEL', 'ISRAELI'),
              ('FRANCE', 'FRENCH', 'INDIA', 'INDIAN'),
              ('FRANCE', 'FRENCH', 'ISRAEL', 'ISRAELI'),
              ('INDIA', 'INDIAN', 'ISRAEL', 'ISRAELI'),
              ('INDIA', 'INDIAN', 'AUSTRALIA', 'AUSTRALIAN'),
              ('ISRAEL', 'ISRAELI', 'AUSTRALIA', 'AUSTRALIAN'),
              ('ISRAEL', 'ISRAELI', 'INDIA', 'INDIAN')],
  'incorrect': [('AUSTRALIA', 'AUSTRALIAN', 'FRANCE', 'FRENCH'),
                ('AUSTRALIA', 'AUSTRALIAN', 'SWITZERLAND', 'SWISS'),
                ('FRANCE', 'FRENCH', 'SWITZERLAND', 'SWISS'),
                ('FRANCE', 'FRENCH', 'AUSTRALIA', 'AUSTRALIAN'),
                ('INDIA', 'INDIAN', 'SWITZERLAND', 'SWISS'),
                ('INDIA', 'INDIAN', 'FRANCE', 'FRENCH'),
                ('ISRAEL', 'ISRAELI', 'SWITZERLAND', 'SWISS'),
                ('ISRAEL', 'ISRAELI', 'FRANCE', 'FRENCH'),
                ('SWITZERLAND', 'SWISS', 'AUSTRALIA', 'AUSTRALIAN'),
                ('SWITZERLAND', 'SWISS', 'FRANCE', 'FRENCH'),
                ('SWITZERLAND', 'SWISS', 'INDIA', 'INDIAN'),
                ('SWITZERLAND', 'SWISS', 'ISRAEL', 'ISRAELI')],
  'section': 'gram6-nationality-adjective'},
 {'correct': [('PAYING', 'PAID', 'SAYING', 'SAID')],
  'incorrect': [('GOING', 'WENT', 'PAYING', 'PAID'),
                ('GOING', 'WENT', 'PLAYING', 'PLAYED'),
                ('GOING', 'WENT', 'SAYING', 'SAID'),
                ('GOING', 'WENT', 'TAKING', 'TOOK'),
                ('PAYING', 'PAID', 'PLAYING', 'PLAYED'),
                ('PAYING', 'PAID', 'TAKING', 'TOOK'),
                ('PAYING', 'PAID', 'GOING', 'WENT'),
                ('PLAYING', 'PLAYED', 'SAYING', 'SAID'),
                ('PLAYING', 'PLAYED', 'TAKING', 'TOOK'),
                ('PLAYING', 'PLAYED', 'GOING', 'WENT'),
                ('PLAYING', 'PLAYED', 'PAYING', 'PAID'),
                ('SAYING', 'SAID', 'TAKING', 'TOOK'),
                ('SAYING', 'SAID', 'GOING', 'WENT'),
                ('SAYING', 'SAID', 'PAYING', 'PAID'),
                ('SAYING', 'SAID', 'PLAYING', 'PLAYED'),
                ('TAKING', 'TOOK', 'GOING', 'WENT'),
                ('TAKING', 'TOOK', 'PAYING', 'PAID'),
                ('TAKING', 'TOOK', 'PLAYING', 'PLAYED'),
                ('TAKING', 'TOOK', 'SAYING', 'SAID')],
  'section': 'gram7-past-tense'},
 {'correct': [('MAN', 'MEN', 'CHILD', 'CHILDREN')],
  'incorrect': [('BUILDING', 'BUILDINGS', 'CAR', 'CARS'),
                ('BUILDING', 'BUILDINGS', 'CHILD', 'CHILDREN'),
                ('BUILDING', 'BUILDINGS', 'MAN', 'MEN'),
                ('CAR', 'CARS', 'CHILD', 'CHILDREN'),
                ('CAR', 'CARS', 'MAN', 'MEN'),
                ('CAR', 'CARS', 'BUILDING', 'BUILDINGS'),
                ('CHILD', 'CHILDREN', 'MAN', 'MEN'),
                ('CHILD', 'CHILDREN', 'BUILDING', 'BUILDINGS'),
                ('CHILD', 'CHILDREN', 'CAR', 'CARS'),
                ('MAN', 'MEN', 'BUILDING', 'BUILDINGS'),
                ('MAN', 'MEN', 'CAR', 'CARS')],
  'section': 'gram8-plural'},
 {'correct': [], 'incorrect': [], 'section': 'gram9-plural-verbs'},
 {'correct': [('GOOD', 'BETTER', 'GREAT', 'GREATER'),
              ('GREAT', 'GREATER', 'LOW', 'LOWER'),
              ('LONG', 'LONGER', 'GREAT', 'GREATER'),
              ('GREAT', 'GREATEST', 'LARGE', 'LARGEST'),
              ('LOOK', 'LOOKING', 'SAY', 'SAYING'),
              ('PLAY', 'PLAYING', 'SAY', 'SAYING'),
              ('PLAY', 'PLAYING', 'LOOK', 'LOOKING'),
              ('SAY', 'SAYING', 'LOOK', 'LOOKING'),
              ('SAY', 'SAYING', 'PLAY', 'PLAYING'),
              ('AUSTRALIA', 'AUSTRALIAN', 'INDIA', 'INDIAN'),
              ('AUSTRALIA', 'AUSTRALIAN', 'ISRAEL', 'ISRAELI'),
              ('FRANCE', 'FRENCH', 'INDIA', 'INDIAN'),
              ('FRANCE', 'FRENCH', 'ISRAEL', 'ISRAELI'),
              ('INDIA', 'INDIAN', 'ISRAEL', 'ISRAELI'),
              ('INDIA', 'INDIAN', 'AUSTRALIA', 'AUSTRALIAN'),
              ('ISRAEL', 'ISRAELI', 'AUSTRALIA', 'AUSTRALIAN'),
              ('ISRAEL', 'ISRAELI', 'INDIA', 'INDIAN'),
              ('PAYING', 'PAID', 'SAYING', 'SAID'),
              ('MAN', 'MEN', 'CHILD', 'CHILDREN')],
  'incorrect': [('HE', 'SHE', 'HIS', 'HER'),
                ('HIS', 'HER', 'HE', 'SHE'),
                ('GOOD', 'BETTER', 'LONG', 'LONGER'),
                ('GOOD', 'BETTER', 'LOW', 'LOWER'),
                ('GREAT', 'GREATER', 'LONG', 'LONGER'),
                ('GREAT', 'GREATER', 'GOOD', 'BETTER'),
                ('LONG', 'LONGER', 'LOW', 'LOWER'),
                ('LONG', 'LONGER', 'GOOD', 'BETTER'),
                ('LOW', 'LOWER', 'GOOD', 'BETTER'),
                ('LOW', 'LOWER', 'GREAT', 'GREATER'),
                ('LOW', 'LOWER', 'LONG', 'LONGER'),
                ('BIG', 'BIGGEST', 'GOOD', 'BEST'),
                ('BIG', 'BIGGEST', 'GREAT', 'GREATEST'),
                ('BIG', 'BIGGEST', 'LARGE', 'LARGEST'),
                ('GOOD', 'BEST', 'GREAT', 'GREATEST'),
                ('GOOD', 'BEST', 'LARGE', 'LARGEST'),
                ('GOOD', 'BEST', 'BIG', 'BIGGEST'),
                ('GREAT', 'GREATEST', 'BIG', 'BIGGEST'),
                ('GREAT', 'GREATEST', 'GOOD', 'BEST'),
                ('LARGE', 'LARGEST', 'BIG', 'BIGGEST'),
                ('LARGE', 'LARGEST', 'GOOD', 'BEST'),
                ('LARGE', 'LARGEST', 'GREAT', 'GREATEST'),
                ('GO', 'GOING', 'LOOK', 'LOOKING'),
                ('GO', 'GOING', 'PLAY', 'PLAYING'),
                ('GO', 'GOING', 'RUN', 'RUNNING'),
                ('GO', 'GOING', 'SAY', 'SAYING'),
                ('LOOK', 'LOOKING', 'PLAY', 'PLAYING'),
                ('LOOK', 'LOOKING', 'RUN', 'RUNNING'),
                ('LOOK', 'LOOKING', 'GO', 'GOING'),
                ('PLAY', 'PLAYING', 'RUN', 'RUNNING'),
                ('PLAY', 'PLAYING', 'GO', 'GOING'),
                ('RUN', 'RUNNING', 'SAY', 'SAYING'),
                ('RUN', 'RUNNING', 'GO', 'GOING'),
                ('RUN', 'RUNNING', 'LOOK', 'LOOKING'),
                ('RUN', 'RUNNING', 'PLAY', 'PLAYING'),
                ('SAY', 'SAYING', 'GO', 'GOING'),
                ('SAY', 'SAYING', 'RUN', 'RUNNING'),
                ('AUSTRALIA', 'AUSTRALIAN', 'FRANCE', 'FRENCH'),
                ('AUSTRALIA', 'AUSTRALIAN', 'SWITZERLAND', 'SWISS'),
                ('FRANCE', 'FRENCH', 'SWITZERLAND', 'SWISS'),
                ('FRANCE', 'FRENCH', 'AUSTRALIA', 'AUSTRALIAN'),
                ('INDIA', 'INDIAN', 'SWITZERLAND', 'SWISS'),
                ('INDIA', 'INDIAN', 'FRANCE', 'FRENCH'),
                ('ISRAEL', 'ISRAELI', 'SWITZERLAND', 'SWISS'),
                ('ISRAEL', 'ISRAELI', 'FRANCE', 'FRENCH'),
                ('SWITZERLAND', 'SWISS', 'AUSTRALIA', 'AUSTRALIAN'),
                ('SWITZERLAND', 'SWISS', 'FRANCE', 'FRENCH'),
                ('SWITZERLAND', 'SWISS', 'INDIA', 'INDIAN'),
                ('SWITZERLAND', 'SWISS', 'ISRAEL', 'ISRAELI'),
                ('GOING', 'WENT', 'PAYING', 'PAID'),
                ('GOING', 'WENT', 'PLAYING', 'PLAYED'),
                ('GOING', 'WENT', 'SAYING', 'SAID'),
                ('GOING', 'WENT', 'TAKING', 'TOOK'),
                ('PAYING', 'PAID', 'PLAYING', 'PLAYED'),
                ('PAYING', 'PAID', 'TAKING', 'TOOK'),
                ('PAYING', 'PAID', 'GOING', 'WENT'),
                ('PLAYING', 'PLAYED', 'SAYING', 'SAID'),
                ('PLAYING', 'PLAYED', 'TAKING', 'TOOK'),
                ('PLAYING', 'PLAYED', 'GOING', 'WENT'),
                ('PLAYING', 'PLAYED', 'PAYING', 'PAID'),
                ('SAYING', 'SAID', 'TAKING', 'TOOK'),
                ('SAYING', 'SAID', 'GOING', 'WENT'),
                ('SAYING', 'SAID', 'PAYING', 'PAID'),
                ('SAYING', 'SAID', 'PLAYING', 'PLAYED'),
                ('TAKING', 'TOOK', 'GOING', 'WENT'),
                ('TAKING', 'TOOK', 'PAYING', 'PAID'),
                ('TAKING', 'TOOK', 'PLAYING', 'PLAYED'),
                ('TAKING', 'TOOK', 'SAYING', 'SAID'),
                ('BUILDING', 'BUILDINGS', 'CAR', 'CARS'),
                ('BUILDING', 'BUILDINGS', 'CHILD', 'CHILDREN'),
                ('BUILDING', 'BUILDINGS', 'MAN', 'MEN'),
                ('CAR', 'CARS', 'CHILD', 'CHILDREN'),
                ('CAR', 'CARS', 'MAN', 'MEN'),
                ('CAR', 'CARS', 'BUILDING', 'BUILDINGS'),
                ('CHILD', 'CHILDREN', 'MAN', 'MEN'),
                ('CHILD', 'CHILDREN', 'BUILDING', 'BUILDINGS'),
                ('CHILD', 'CHILDREN', 'CAR', 'CARS'),
                ('MAN', 'MEN', 'BUILDING', 'BUILDINGS'),
                ('MAN', 'MEN', 'CAR', 'CARS')],
  'section': 'total'}]