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Pre-trained embeddings

ELMo

ELMo (Embeddings from Language Models) representations are pre-trained contextual representations from large-scale bidirectional language models. See a paper Deep contextualized word representations for more information about the algorithm and a detailed analysis.

License

The pre-trained models are distributed under the License Apache 2.0.

Downloads

The models can be downloaded and run by configuration file or tensorflow hub module from:

Description Dataset parameters Perplexity Configuration file and tensorflow hub module
ELMo on Russian Wikipedia lines = 1M, tokens = 386M, size = 5GB 43.692 config_file, module_spec
ELMo on Russian WMT News lines = 63M, tokens = 946M, size = 12GB 49.876 config_file, module_spec
ELMo on Russian Twitter lines = 104M, tokens = 810M, size = 8.5GB 94.145 config_file, module_spec

fastText

We are publishing pre-trained word vectors for Russian language. These vectors were trained on joint Russian Wikipedia and Lenta.ru corpora.

All vectors are 300-dimentional. We used fastText skpip-gram (see Bojanowski et al. (2016)) for vectors training as well as various preprocessing options (see below).

You can get vectors either in binary or in text (vec) formats both for fastText and GloVe.

License

The pre-trained word vectors are distributed under the License Apache 2.0.

Downloads

The models can be downloaded from:

Model Preprocessing Vectors
fastText (skipgram) tokenize (nltk word_tokenize), lemmatize (pymorphy2) bin, vec
fastText (skipgram) tokenize (nltk word_tokenize), lowercasing bin, vec
fastText (skipgram) tokenize (nltk wordpunсt_tokenize) bin, vec
fastText (skipgram) tokenize (nltk word_tokenize) bin, vec
fastText (skipgram) tokenize (nltk word_tokenize), remove stopwords bin, vec

Word vectors training parameters

These word vectors were trained with following parameters ([...] is for default value):

fastText (skipgram)

  • lr [0.1]
  • lrUpdateRate [100]
  • dim 300
  • ws [5]
  • epoch [5]
  • neg [5]
  • loss [softmax]
  • pretrainedVectors []
  • saveOutput [0]