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zqhZY avatar zqhZY commented on August 17, 2024 2

gensim: models.keyedvectors – Store and query word vectors, Common code independent of the way the vectors are trained(Word2Vec, FastText, WordRank, VarEmbed etc)

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crownpku avatar crownpku commented on August 17, 2024

我觉得应该是加一个参数, embedding_type="gensim"/"fasttext",因为用户有可能有不同的两种预训练词向量。
当前版本我们先实现word2vec的吧!

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crownpku avatar crownpku commented on August 17, 2024

@zqhZY 所以对于已有词向量的存储和使用,只需要gensim一个包就够了对吧。用户要自己训练词向量的话,还是要用word2vec或者fasttext这些工具?

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zqhZY avatar zqhZY commented on August 17, 2024

只需要gensim一个包就够, 可以适应大多数的工具生成的word2vec,已经提了一个request,辛苦review一下

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befeng avatar befeng commented on August 17, 2024

这个地方我还有一个问题,如果我们用的包太杂,其结果我们需要维护很多依赖库,这个怎么处理。

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crownpku avatar crownpku commented on August 17, 2024

第一个是要做lazy import,即pipeline里某个模块某个函数用到了再去import相应的包。
另外对于常用包要及时写到requirements.txt里面
最后docker环境搭建的时候也会把依赖包一并打包进去的。

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