A buffet of Chinese word embeddings, no training required

What it does

This repository distributes more than 100 pre-trained Chinese word vectors generated from corpora as varied as Wikipedia, Weibo, the People’s Daily, and the Complete Library in Four Sections. The release covers two mathematical representations—dense 300-dimensional SGNS embeddings and sparse PPMI vectors—each trained with several context configurations (word-only, plus n-gram, plus character, and combinations). Think of it as a systematic tasting menu: pick the vector set that matches your domain instead of training your own.

The interesting bit

The authors back up the downloads with CA8, a Chinese analogical reasoning dataset, and an evaluation toolkit so you can actually measure how well the embeddings behave. It is unusual to see this level of methodological rigor in a bulk release—every set shares the same hyperparameters, so comparisons are closer to apples-to-apples than usual.

Key highlights

• Dense SGNS (word2vec-style) and sparse PPMI vectors, each crossed with word, character, and n-gram context features.
• Coverage spans modern sources (Baidu Encyclopedia, Zhihu QA, financial news, Sogou News) to classical literature.
• Includes the CA8 benchmark and an evaluation toolkit for intrinsic quality testing.
• Consistent training recipe across the board: 300 dimensions, window size 5, dynamic windows, sub-sampling at 1e-5.
• A few sets are mirrored on Google Drive, though the majority of links point to Baidu Netdisk.

Caveats

• The README is essentially a large download matrix; visible training code or detailed API documentation is absent from the text.
• Most file hosting is on Baidu Netdisk, which can be slow or inaccessible outside mainland China.
• It is unclear from the README whether the underlying corpora are version-locked or if embeddings will be retrained.

Verdict

Ideal if you need off-the-shelf Chinese embeddings with documented provenance and a built-in benchmark. Less useful if you want a training framework or an actively maintained Python package—this is a curated research release, not a library.