Numberbatch 19.08 Word Embeddings (filtered, compressed, as single files)

Numberbatch 19.08 word embeddings obtained from https://github.com/commonsense/conceptnet-numberbatch, filtered (see below) and split into single language files. By splitting the original 20.8 GB numberbatch file in into separate, compressed files - and thus reducing the download and in-memory-size requirements of these embeddings by several magnitudes - it becomes feasible to work with them in highly memory-constrained environments such as Binder. This is useful for teaching purposes and a prerequisite for upcoming interactive publications we are working on. PREPROCESSING Note that words `w` with `w.isalpha() == False` (in Python) have been excluded, since these are seldomly useful, and this filtering step reduces the file sizes by roughly another 30%. ZIP files have been compressed in Python using zipfile.ZIP_LZMA. To load the uncompressed files into gensim, use `gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)`. LICENSE This data contains semantic vectors from ConceptNet Numberbatch, by Luminoso Technologies, Inc. You may redistribute or modify the data under the terms of the CC-By-SA 4.0 license.