This porject contains data structure of word2vec, and io functions to read/write word2vec from/to either text files or bin files.
This project doesn't contain the algorithm for computing vector representations of words.
Word2Vec is basically a map from word to vectors. To read the word2vec model form a text file, you can
Word2Vec model = Word2VecUtils.readFromTxtFile(file);
This project assumes that the bin file created by the C word2vec tool uses little-endian to write floats into the bin file. Each float occupies 4 bytes (32 bits) specified by the IEEE 754 standard. To read the word2vec model from a bin file, you can
Word2Vec model = Word2VecUtils.readFromBinFile(file);
Alternatively, the project also allows you to specifiy the bye order of the bin file.
Word2Vec model = Word2VecUtils.readFromBinFile(file, byteOrder);
In many cases, the pre-trained word vectors is very large. For example, the model trained on part of Google New data set contains 300-dimensional vectors for 3 million words and phrases. The size of the model is about 1.5G in bin.gz format. This makes loading and using the word2vec model difficult and inefficient. On the other hand, in some projects we may not need to load the entire model. Suppose we have already known the vocabulary of the text. For computing the similarity of words in the text, We only need vectors whose words are in the vocabulary. For this purpose, the project provides a function that loads a subset of word2vec specifiec by the vocabulary.
Word2Vec model = Word2VecUtils.readFromBinFile(file, vocab);
You may download the source code from this website. Alternatively You can pull it from the centeral Maven repositories:
<dependency> <groupId>com.pengyifan.word2vec</groupId> <artifactId>pengyifan-word2vec</artifactId> <version>0.0.1</version> </dependency>
<repositories> <repository> <id>oss-sonatype</id> <name>oss-sonatype</name> <url>https://oss.sonatype.org/content/repositories/snapshots/</url> <snapshots> <enabled>true</enabled> </snapshots> </repository> </repositories> ... <dependency> <groupId>com.pengyifan.word2vec</groupId> <artifactId>pengyifan-word2vec</artifactId> <version>0.0.1-SNAPSHOT</version> </dependency>
- Yifan Peng (email@example.com)
The official word2vec webpage is available with all up-to-date instructions and code.