bpemb

Why we included this project

BPEmb saves neural NLP projects the work of assembling their own subword pipeline: it ships a Byte-Pair Encoding segmenter and pretrained embeddings for 275 languages, trained on Wikipedia. You load a model by language code and dimensionality, and the library downloads the right files and returns embeddings as a gensim KeyedVectors object, so it drops into existing vector workflows without a custom format. The vocabulary-size setting is the main thing to think about before choosing a model. Smaller vocabularies split words into more subwords, which helps with morphology and out-of-vocabulary tokens, while larger ones keep frequent words whole. That tradeoff, plus the language coverage, makes it handy for multilingual classification, sequence labeling, or translation systems that want subword-aware inputs without training embeddings from scratch.