Word2Vec Models built from a Collection of French 20th-Century Novels

The models were trained using the Gensim library for Python, developed by Radim Rehurek, in 2017. All models are based on the same collection of 20th century French novels that covers the period from 1900 to 2010, with a large range of authors and genres respresented. The collection contains approximately 1,200 novels and about 60 million tokens. The models were created using the SGNS (Skip-Gram with Negative Sampling) architecture, the context window was always of size of 6 + 6 around the target word, and the texts were lemmatised and POS-tagged beforehand. POS-Tags remain attached to each token (as in "souris_nom"). Other parameters vary by model: some have 200, some have 300 dimensional vectors; the minimum frequency of the words in the model varies with values of 50, 100 and 200, something which influences the size of the vocabulary and the size of the model.本系列模型依托由拉迪姆·雷雷克（Radim Rehurek）于2017年开发的Python Gensim库完成训练。所有模型均基于同一套涵盖1900年至2010年的20世纪法国小说语料库，收录了大量不同作者与流派的作品。该语料库总计包含约1200部小说，总词元（Token）数约为6000万。模型采用带负采样的跳字模型（SGNS，Skip-Gram with Negative Sampling）架构构建，上下文窗口固定为目标词前后各6个词元，且所有文本均预先完成词形还原与词性标注（POS-tagging）操作。词性标注结果将保留在每个词元后，格式示例为"souris_nom"。其余参数因模型而异：部分模型的词向量维度为200，部分为300；模型的单词最低出现频次阈值设置为50、100与200不等，该参数将直接影响词表规模与模型体量。