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'}]
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