Vectors/norlm/norelmo
Contents
NorELMo: Embeddings from Language Models for Norwegian
NorELMo is a set of bidirectional recurrent ELMo language models trained from scratch on Norwegian Wikipedia, trained as part of the ongoing NorLM initiative. ELMo was the first contextualized architecture to become well-known in the NLP community. [Peters et al 2018] describing it got the Best Paper award at the NAACL 2018 conference.
NorELMO models can be used to achieve state-of-the-art results for various Norwegian natural language processing tasks. In many cases, they may be a viable alternative to NorBERT, especially if computational resources are scarce.
Download from the NLPL Vector repository:
- ID 210: trained on lemmatized Norwegian Wikipedia Dump of September 2020, about 160 million words (download)
- ID 211: trained on tokenized Norwegian Wikipedia Dump of September 2020, about 160 million words (download)
- ID 217 (NorELMo30): trained on the NorBERT training corpus (Wikipedia and Norsk Aviskorpus) with 30 000 words in the target vocabulary (download)
- ID 218 (NorELMo100): trained on the NorBERT training corpus (Wikipedia and Norsk Aviskorpus) with 100 000 words in the target vocabulary (download)
Preprocessing
All the models were trained on the corpus tokenized using UDPipe. The lemmatized model is trained on the version of the corpus where raw word forms were replaced with their lemmas (`kontorer' --> `kontor'). For different tasks, different models can be better.
Vocabulary
Independent of the vocabulary size, an ELMo model can process arbitrary word tokens, due to its architecture (where the first CNN layer converts input strings to non-contextual word embeddings). Thus, the size of the vocabulary controls only the number of words used as targets for the language modelling task in the course of training. Supposedly, the model with a larger vocabulary is more effective in treating less frequent words at the cost of being less effective with more frequent words.
The vocabulary sizes for NorELMo models are stated earlier. The vocabularies are published together with the models in the archives linked above.
Training workflow
Each models was trained for 3 epochs with batch size 192. We employed a version of the original ELMo training code updated to work better with the recent TensorFlow versions. All the hyperparameters were left at their default values, except LSTM dimensionality reduced to 2048 from the default 4096.
Evaluation
See the description of the evaluation setup here
In parentheses, the time required to adapt the model to a particular task (fine-tuning or training a classifier over a frozen model) is given in minutes.
| Task/Model | mBERT | NorBERT | NB-BERT-Base | NorELMo30 | NorELMo100 |
|---|---|---|---|---|---|
| Part-of-Speech tagging (accuracy) | 98.0 (245) | 98.5 (238) | 98.7 (244) | 98.1 (8) | 98.0 (8) |
| Fine-grained sentiment analysis (Sentiment Graph F1) | 31.7 (444) | 34.8 (438) | 34.8 (404) | 34.5 (446) | 34.2 (434) |
| Sentence-level binary sentiment classification (F1 score) | 67.7 (37) | 77.1 (35) | 83.9 (37) | 75.0 (5) | 75.0 (5) |
Usage
The NorELMO models are published in two formats:
1. TensorFlow checkpoints
2. HDF5 model files
We recommend to use our simple-elmo Python library to do stuff with NorELMo models.
