We're in the process of releasing BERT models as well. Get the first one here: https://github.com/mollerhoj/danish_bert
Inductive transfer learning has greatly impacted computer vision, but existing approaches in NLP still require task-specific modifications and training from scratch.
This repository contains the weights for the embedding layer of a UMLFiT language model that can be used as the first step in fine-tuning any Natural Language Processing task.
The weights were trained on 90% of all text in the corresponding language wikipedia as per 3. July 2018. The remaining 10% was used for validation.
Trained on 78,373,122 tokens, and validated on 7,837,310 tokens. We achieve a perplexity of 30.9. Download files: Link
Trained on 80,284,231 tokens, and validated on 8,920,387 tokens. We achieve a perplexity of 26.31. Download files: Link
Trained on 68,775,370 tokens, and validated on 7,641,571 tokens. We achieve a perplexity of 27.66
Training even higher performance models is possible, but require more (costly) training time. If you need a model with higher performance, feel free to contact us. Download files: Link
Our servers crashed when training the Swedish model, but if you're in need of it, contact us and we can train it for you.
See Universal Language Model Fine-tuning for Text Classification, Jeremy Howard, Sebastian Ruder, https://arxiv.org/abs/1801.06146
enc.h5 Contains the weights in 'Hierarchical Data Format'
enc.pth Contains the weights in 'Pytorch model format'
itos.pkl (Integers to Strings) contains the vocabulary mapping from ids (0 - 30000) to strings
This work was sponsored by Danish chatbot company BotXO http://www.botxo.co/
Thanks to Tobias Lindberg from Damvad Analytics for converting the vectors to pth-format.