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• LitLat BERT

  4 resources

  Trilingual BERT-like (Bidirectional Encoder Representations from Transformers) model, trained on Lithuanian, Latvian, and English data. State of the art tool representing words/tokens as contextually dependent word embeddings, used for various NLP classification tasks by fine-tuning the model end-to-end. LitLat BERT are neural network weights and configuration files in pytorch format (i.e. to be used with pytorch library). The corpora used for training the model have 4.07 billion tokens in total, of which 2.32 billion are English, 1.21 billion are Lithuanian and 0.53 billion are Latvian. LitLat BERT is based on XLM-RoBERTa model and comes in two versions, one for usage with transformers library (https://github.com/huggingface/transformers), and one for usage with fairseq library (https://github.com/pytorch/fairseq). More information is in the readme.txt.

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• ELMo embeddings models for seven languages

  8 resources

  ELMo language model (https://github.com/allenai/bilm-tf) used to produce contextual word embeddings, trained on large monolingual corpora for 7 languages: Slovenian, Croatian, Finnish, Estonian, Latvian, Lithuanian and Swedish. Each language's model was trained for approximately 10 epochs. Corpora sizes used in training range from over 270 M tokens in Latvian to almost 2 B tokens in Croatian. About 1 million most common tokens were provided as vocabulary during the training for each language model. The model can also infer OOV words, since the neural network input is on the character level. Each model is in its own .tar.gz archive, consisting of two files: pytorch weights (.hdf5) and options (.json). Both are needed for model inference, using allennlp (https://github.com/allenai/allennlp/blob/master/tutorials/how_to/elmo.md) python library.

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