readme: ---
  language: da
  tags:
  - bert
  - masked-lm
  license: cc-by-4.0
  datasets:
  - common_crawl
  - wikipedia
  pipeline_tag: fill-mask
  widget:
  - text: "København er [MASK] i Danmark."
  ---
  
  # Danish BERT (uncased) model 
  
  [BotXO.ai](https://www.botxo.ai/) developed this model. For data and training details see their [GitHub repository](https://github.com/botxo/nordic_bert).  
  
  The original model was trained in TensorFlow then I converted it to Pytorch using [transformers-cli](https://huggingface.co/transformers/converting_tensorflow_models.html?highlight=cli).
  
  For TensorFlow version download here: https://www.dropbox.com/s/19cjaoqvv2jicq9/danish_bert_uncased_v2.zip?dl=1
  
  
  ## Architecture
  
  ```python
  from transformers import AutoModelForPreTraining
  
  model = AutoModelForPreTraining.from_pretrained("DJSammy/bert-base-danish-uncased_BotXO,ai")
  
  params = list(model.named_parameters())
  print('danish_bert_uncased_v2 has {:} different named parameters.\n'.format(len(params)))
  
  print('==== Embedding Layer ====\n')
  for p in params[0:5]:
      print("{:<55} {:>12}".format(p[0], str(tuple(p[1].size()))))
  
  print('\n==== First Transformer ====\n')
  for p in params[5:21]:
      print("{:<55} {:>12}".format(p[0], str(tuple(p[1].size()))))
  
  print('\n==== Last Transformer ====\n')
  for p in params[181:197]:
      print("{:<55} {:>12}".format(p[0], str(tuple(p[1].size()))))
  
  print('\n==== Output Layer ====\n')
  for p in params[197:]:
      print("{:<55} {:>12}".format(p[0], str(tuple(p[1].size()))))
  
  # danish_bert_uncased_v2 has 206 different named parameters.
  
  # ==== Embedding Layer ====
  
  # bert.embeddings.word_embeddings.weight                  (32000, 768)
  # bert.embeddings.position_embeddings.weight                (512, 768)
  # bert.embeddings.token_type_embeddings.weight                (2, 768)
  # bert.embeddings.LayerNorm.weight                              (768,)
  # bert.embeddings.LayerNorm.bias                                (768,)
  
  # ==== First Transformer ====
  
  # bert.encoder.layer.0.attention.self.query.weight          (768, 768)
  # bert.encoder.layer.0.attention.self.query.bias                (768,)
  # bert.encoder.layer.0.attention.self.key.weight            (768, 768)
  # bert.encoder.layer.0.attention.self.key.bias                  (768,)
  # bert.encoder.layer.0.attention.self.value.weight          (768, 768)
  # bert.encoder.layer.0.attention.self.value.bias                (768,)
  # bert.encoder.layer.0.attention.output.dense.weight        (768, 768)
  # bert.encoder.layer.0.attention.output.dense.bias              (768,)
  # bert.encoder.layer.0.attention.output.LayerNorm.weight        (768,)
  # bert.encoder.layer.0.attention.output.LayerNorm.bias          (768,)
  # bert.encoder.layer.0.intermediate.dense.weight           (3072, 768)
  # bert.encoder.layer.0.intermediate.dense.bias                 (3072,)
  # bert.encoder.layer.0.output.dense.weight                 (768, 3072)
  # bert.encoder.layer.0.output.dense.bias                        (768,)
  # bert.encoder.layer.0.output.LayerNorm.weight                  (768,)
  # bert.encoder.layer.0.output.LayerNorm.bias                    (768,)
  
  # ==== Last Transformer ====
  
  # bert.encoder.layer.11.attention.self.query.weight         (768, 768)
  # bert.encoder.layer.11.attention.self.query.bias               (768,)
  # bert.encoder.layer.11.attention.self.key.weight           (768, 768)
  # bert.encoder.layer.11.attention.self.key.bias                 (768,)
  # bert.encoder.layer.11.attention.self.value.weight         (768, 768)
  # bert.encoder.layer.11.attention.self.value.bias               (768,)
  # bert.encoder.layer.11.attention.output.dense.weight       (768, 768)
  # bert.encoder.layer.11.attention.output.dense.bias             (768,)
  # bert.encoder.layer.11.attention.output.LayerNorm.weight       (768,)
  # bert.encoder.layer.11.attention.output.LayerNorm.bias         (768,)
  # bert.encoder.layer.11.intermediate.dense.weight          (3072, 768)
  # bert.encoder.layer.11.intermediate.dense.bias                (3072,)
  # bert.encoder.layer.11.output.dense.weight                (768, 3072)
  # bert.encoder.layer.11.output.dense.bias                       (768,)
  # bert.encoder.layer.11.output.LayerNorm.weight                 (768,)
  # bert.encoder.layer.11.output.LayerNorm.bias                   (768,)
  
  # ==== Output Layer ====
  
  # bert.pooler.dense.weight                                  (768, 768)
  # bert.pooler.dense.bias                                        (768,)
  # cls.predictions.bias                                        (32000,)
  # cls.predictions.transform.dense.weight                    (768, 768)
  # cls.predictions.transform.dense.bias                          (768,)
  # cls.predictions.transform.LayerNorm.weight                    (768,)
  # cls.predictions.transform.LayerNorm.bias                      (768,)
  # cls.seq_relationship.weight                                 (2, 768)
  # cls.seq_relationship.bias                                       (2,)
  ```
  
  ## Example Pipeline
  
  ```python
  from transformers import pipeline
  u