This page gathers several pre-trained word vectors trained using fastText.
这一篇整合了一些之前用fasttext训练的词向量。
### Download pre-trained word vectors
### 下载经过训练的词向量
Pre-trained word vectors learned on different sources can be downloaded below:
可以在下面下载在不同来源学习过的预先训练过的单词向量：
1.[wiki-news-300d-1M.vec.zip](https://s3-us-west-1.amazonaws.com/fasttext-vectors/wiki-news-300d-1M.vec.zip): 1 million word vectors trained on Wikipedia 2017, UMBC webbase corpus and statmt.org news dataset (16B tokens).
2.[wiki-news-300d-1M-subword.vec.zip](https://s3-us-west-1.amazonaws.com/fasttext-vectors/wiki-news-300d-1M-subword.vec.zip): 1 million word vectors trained with subword infomation on Wikipedia 2017, UMBC webbase corpus and statmt.org news dataset (16B tokens).
3.[crawl-300d-2M.vec.zip](https://s3-us-west-1.amazonaws.com/fasttext-vectors/crawl-300d-2M.vec.zip): 2 million word vectors trained on Common Crawl (600B tokens).
3.[crawl-300d-2M.vec.zip](https://s3-us-west-1.amazonaws.com/fasttext-vectors/crawl-300d-2M.vec.zip) : 两百万的词向量，这些词向量是在 Common Crawl 上训练得到的。(600B)
The first line of the file contains the number of words in the vocabulary and the size of the vectors.
Each line contains a word followed by its vectors, like in the default fastText text format.
Each value is space separated. Words are ordered by descending frequency.
### 格式
### License
文件的第一行包含了词汇表中单词的数量以及向量的大小。
每一行包含了一个单词和它的向量，就像是fasttext文本格式默认的那种样子。
每个值都是由空格隔开。
单词是按照频数降序排列的。
These word vectors are distributed under the [*Creative Commons Attribution-Share-Alike License 3.0*](https://creativecommons.org/licenses/by-sa/3.0/).
If you use these word vectors, please cite the following paper:
### 参考资料
如果你使用了这些词向量，请引用下面的文章：
T. Mikolov, E. Grave, P. Bojanowski, C. Puhrsch, A. Joulin. [*Advances in Pre-Training Distributed Word Representations*](https://arxiv.org/abs/1712.09405)
