Data Structure ?
6459.98M
Data Structure ?
*The above analysis is the result extracted and analyzed by the system, and the specific actual data shall prevail.
README.md
# FastText
https://fasttext.cc/docs/en/english-vectors.html
Common Crawl（6000億トークン）で学習された200万個の単語ベクトル。Facebookによって公開された300次元の事前学習済みFastText英語単語ベクトル。
FastTextは、ユーザーがテキスト表現とテキスト分類器を学習できるオープンソースで無料の軽量ライブラリです。標準的な汎用ハードウェアで動作します。後でモデルのサイズを縮小して、モバイルデバイスにも収まるようにすることができます。
このテキストには、サブワードを含まない2つの埋め込みファイルが含まれています。
- wiki-news-300d-1M.vec.zip：Wikipedia 2017、UMBCウェブベースコーパス、およびstatmt.orgニュースデータセット（160億トークン）で学習された100万個の単語ベクトル。
- crawl-300d-2M.vec.zip：Common Crawl（6000億トークン）で学習された200万個の単語ベクトル。
謝辞
これらの埋め込みは、以下の論文に基づいて作成されました。
T. Mikolov、E. Grave、P. Bojanowski、C. Puhrsch、A. Joulin。事前学習分散型単語表現の進歩
@inproceedings{mikolov2018advances,
title={Advances in Pre-Training Distributed Word Representations},
author={Mikolov, Tomas and Grave, Edouard and Bojanowski, Piotr and Puhrsch, Christian and Joulin, Armand},
booktitle={Proceedings of the International Conference on Language Resources and Evaluation (LREC 2018)},
year={2018}
}
[fastextチーム](https://fasttext.cc/docs/en/english-vectors.html)に感謝します。
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- 3. Basic dataset information comes from the original data source or the information provided by the data provider. If there are discrepancies in the dataset description, please refer to the original data source or service provider's address.
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