This repository contains the Tomas Mikolov's source code for the word2vec tool for computing continuous distributed representations of words.
Initially, the repository was hosted at svn google's server, the first commit of this repo is a mirror of that repo Google 2013.
Before building the repository, ensure you have the following prerequisites installed on your system:
- C compiler (e.g., gcc)
- Make build system
- Python (if you plan to use the provided Python scripts)
To build the repository, follow these steps:
- Clone the repository to your local machine:
$ git clone https://github.com/AlexisTercero55/word2vec_TMB/tree/main- Navigate to the repository directory and run make tool
$ cd .\word2vec_TMB
$ makeWe 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:
- desired vector dimensionality
- the size of the context window for either the Skip-Gram or the Continuous Bag-of-Words model
- training algorithm: hierarchical softmax and / or negative sampling
- threshold for downsampling the frequent words
- number of threads to use
- the format of the output word vector file (text or binary)
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/