跳字模型，skip_gram

1.训练数据

就普通的文本格式

训练数据示例如下：

第一章 陨落的天才
    
    “斗之力，三段！”
    望着测验魔石碑上面闪亮得甚至有些刺眼的五个大字，少年面无表情，唇角有着一抹自嘲，紧握的手掌，因为大力，而导致略微尖锐的指甲深深的刺进了掌心之中，带来一阵阵钻心的疼痛……
    “萧炎，斗之力，三段！级别：低级！”测验魔石碑之旁，一位中年男子，看了一眼碑上所显示出来的信息，语气漠然的将之公布了出来……
    中年男子话刚刚脱口，便是不出意外的在人头汹涌的广场上带起了一阵嘲讽的骚动。
    “三段？嘿嘿，果然不出我所料，这个“天才”这一年又是在原地踏步！”
    “哎，这废物真是把家族的脸都给丢光了。”
    “要不是族长是他的父亲，这种废物，早就被驱赶出家族，任其自生自灭了，哪还有机会待在家族中白吃白喝。”
    “唉，昔年那名闻乌坦城的天才少年，如今怎么落魄成这般模样了啊？”

2.使用示例

skip_gram共实现了三种模型，分别为基础softmax模型(SkipGramBaseModule)、基于负采样的优化模型(SkipGramNegativeSamplingModule)、基于层次softmax的优化模型(SkipGramHierarchicalSoftmaxModule).

三种模型提供的接口一致，如下所示：

导入接口

from lightnlp.we import SkipGramBaseModule, SkipGramNegativeSamplingModule, SkipGramHierarchicalSoftmaxModule  # 分别导入skip_gram不同模型

训练

# skip_gram_model = SkipGramHierarchicalSoftmaxModule()
skip_gram_model = SkipGramNegativeSamplingModule()
# skip_gram_model = SkipGramBaseModule()

train_path = '/home/lightsmile/NLP/corpus/novel/test.txt'
dev_path = '/home/lightsmile/NLP/corpus/novel/test.txt'

skip_gram_model.train(train_path, dev_path=dev_path, save_path='./skip_gram_saves')

测试

skip_gram_model.load('./skip_gram_saves')

skip_gram_model.test(dev_path)

预测

test_target = '族长'
print(skip_gram_model.evaluate(test_target, '他'))
print(skip_gram_model.evaluate(test_target, '提防'))

预测结果：

1.0
0.002815224463120103

保存词向量

skip_gram_model.save_embeddings('./skip_gram_saves/skip_gram_ns.bin')

./skip_gram_saves/skip_gram_ns.bin文件内容：

623 300
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