Automatic segmentation, tokenization and morphological and syntactic annotations of raw texts in 45 languages, generated by UDPipe (http://ufal.mff.cuni.cz/udpipe), together with word embeddings of dimension 100 computed from lowercased texts by word2vec (https://code.google.com/archive/p/word2vec/).
For each language, automatic annotations in CoNLL-U format are provided in a separate archive. The word embeddings for all languages are distributed in one archive.
Note that the CC BY-SA-NC 4.0 license applies to the automatically generated annotations and word embeddings, not to the underlying data, which may have different license and impose additional restrictions.
Update 2018-09-03
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Added data in the 4 “surprise languages” from the 2017 ST: Buryat, Kurmanji, North Sami and Upper Sorbian. This has been promised before, during CoNLL-ST 2018 we gave the participants a link to this record saying the data was here. It wasn't, sorry. But now it is.
Tokenizer, POS Tagger, Lemmatizer and Parser models for 84 treebanks of 56 languages of Universal Depenencies 2.3 Treebanks, created solely using UD 2.3 data (http://hdl.handle.net/11234/1-2895). The model documentation including performance can be found at http://ufal.mff.cuni.cz/udpipe/models#universal_dependencies_23_models .
To use these models, you need UDPipe binary version at least 1.2, which you can download from http://ufal.mff.cuni.cz/udpipe .
In addition to models itself, all additional data and value of hyperparameters used for training are available in the second archive, allowing reproducible training.