This tool provides an efficient implementation of the continuous bag-of-words and skip-gram architectures for computing vector representations of words. These r
This tool provides an efficient implementation of the continuous bag-of-words and skip-gram architectures for computing vector representations of words. These r
Original from https://code.google.com/p/word2vec/
I've copied it to a github project so I can apply and track community patches for my needs (starting with capability for Mac OS X compilation).
There seems to be a segfault in the compute-accuracy utility.
To get started:
cd scripts && ./demo-word.shOriginal README text follows:
This tool provides an efficient implementation of the continuous bag-of-words and skip-gram architectures for computing vector representations of words. These representations can be subsequently used in many natural language processing applications and for further research.
We provide an implementation of the Continuous Bag-of-Words (CBOW) and the Skip-gram model (SG), as well as several demo scripts.
Given a text corpus, the word2vec tool learns a vector for every word in the vocabulary using the Continuous Bag-of-Words or the Skip-Gram neural network architectures. The user should to specify the following:
Usually, the other hyper-parameters such as the learning rate do not need to be tuned for different training sets.
The script demo-word.sh downloads a small (100MB) text corpus from the web, and trains a small word vector model. After the training is finished, the user can interactively explore the similarity of the words.
More information about the scripts is provided at https://code.google.com/p/word2vec/
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