Edit model card

Danish BERT (uncased) model

BotXO.ai developed this model. For data and training details see their GitHub repository.

The original model was trained in TensorFlow then I converted it to Pytorch using transformers-cli.

For TensorFlow version download here: https://www.dropbox.com/s/19cjaoqvv2jicq9/danish_bert_uncased_v2.zip?dl=1

Architecture

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

from transformers import pipeline
unmasker = pipeline('fill-mask', model='DJSammy/bert-base-danish-uncased_BotXO,ai')

unmasker('København er [MASK] i Danmark.')

# Copenhagen is the [MASK] of Denmark.
# =>

# [{'score': 0.788068950176239,
#  'sequence': '[CLS] københavn er hovedstad i danmark. [SEP]',
#  'token': 12610,
#  'token_str': 'hovedstad'},
# {'score': 0.07606703042984009,
#  'sequence': '[CLS] københavn er hovedstaden i danmark. [SEP]',
#  'token': 8108,
#  'token_str': 'hovedstaden'},
# {'score': 0.04299738258123398,
#  'sequence': '[CLS] københavn er metropol i danmark. [SEP]',
#  'token': 23305,
#  'token_str': 'metropol'},
# {'score': 0.008163209073245525,
#  'sequence': '[CLS] københavn er ikke i danmark. [SEP]',
#  'token': 89,
#  'token_str': 'ikke'},
# {'score': 0.006238455418497324,
#  'sequence': '[CLS] københavn er ogsa i danmark. [SEP]',
#  'token': 25253,
#  'token_str': 'ogsa'}]
Downloads last month
21
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Dataset used to train DJSammy/bert-base-danish-uncased_BotXO-ai