README.md

metadata

license: cc-by-sa-3.0
config_names:
  - Abbreviation equality
  - Adjective inflection analogy
  - Clinical analogy
  - Clinical similarity
  - Noun inflection analogy
  - UMNSRS relatedness
  - UMNSRS similarity
  - Verb inflection analogy
configs:
  - config_name: Abbreviation equality
    data_files:
      - split: train
        path: Abbreviation equality/train*
  - config_name: Adjective inflection analogy
    data_files:
      - split: train
        path: Adjective inflection analogy/train*
  - config_name: Clinical analogy
    data_files:
      - split: train
        path: Clinical analogy/train*
  - config_name: Clinical similarity
    data_files:
      - split: train
        path: Clinical similarity/train*
  - config_name: Noun inflection analogy
    data_files:
      - split: train
        path: Noun inflection analogy/train*
  - config_name: UMNSRS relatedness
    data_files:
      - split: train
        path: UMNSRS relatedness/train*
  - config_name: UMNSRS similarity
    data_files:
      - split: train
        path: UMNSRS similarity/train*
  - config_name: Verb inflection analogy
    data_files:
      - split: train
        path: Verb inflection analogy/train*

Danish medical word embeddings

MeDa-We was trained on a Danish medical corpus of 123M tokens. The word embeddings are 300-dimensional and are trained using FastText.

The embeddings were trained for 10 epochs using a window size of 5 and 10 negative samples.

The development of the corpus and word embeddings is described further in our paper.

We also trained a transformer model on the developed corpus which can be found here.

Citing

@inproceedings{pedersen-etal-2023-meda,
    title = "{M}e{D}a-{BERT}: A medical {D}anish pretrained transformer model",
    author = "Pedersen, Jannik  and
      Laursen, Martin  and
      Vinholt, Pernille  and
      Savarimuthu, Thiusius Rajeeth",
    booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)",
    month = may,
    year = "2023",
    address = "T{\'o}rshavn, Faroe Islands",
    publisher = "University of Tartu Library",
    url = "https://aclanthology.org/2023.nodalida-1.31",
    pages = "301--307",
}