Crawl Download

This page gathers several pre-trained word vectors trained using fastText.


StarWordz crawl generator content is licensed under the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported License except that which is quoted from elsewhere or attributed to others. In short, you may reproduce, reblog, and modify my content for non-commercial purposes, but you must provide proper attribution and share it under. Generates large dungeon structures in the overworld. Version 2.2.2 - Increased the distance between dungeons - Reduced the required player range of all spawners from 16 to 8. Crawl is the local multiplayer dungeon crawler where your friends control the monsters! It’s bullet-hell beat-em-up meets old-school RPG, in an arcadey party-game for up to 4 players. One player is the hero, exploring a randomly generated dungeon full of traps and monsters - all controlled by the other players.

Download pre-trained word vectors

Pre-trained word vectors learned on different sources can be downloaded below:

  1. 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: 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: 2 million word vectors trained on Common Crawl (600B tokens).
  4. crawl-300d-2M-subword.zip: 2 million word vectors trained with subword information on Common Crawl (600B tokens).
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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.These text models can easily be loaded in Python using the following code:



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These word vectors are distributed under the Creative Commons Attribution-Share-Alike License 3.0.

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If you use these word vectors, please cite the following paper:

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T. 7-in-1 brain sharpness bundle download free. Mikolov, E. Grave, P. Bojanowski, C. Puhrsch, A. Joulin. Advances in Pre-Training Distributed Word Representations