diff --git a/model_cards/DJSammy/bert-base-danish-uncased_BotXO,ai/README.md b/model_cards/DJSammy/bert-base-danish-uncased_BotXO,ai/README.md new file mode 100644 index 00000000000000..f3c613b8650214 --- /dev/null +++ b/model_cards/DJSammy/bert-base-danish-uncased_BotXO,ai/README.md @@ -0,0 +1,146 @@ +--- +language: da +tags: +- pytorch +- bert +- masked-lm +- lm-head +- da +license: cc-by-4.0 +datasets: +- common_crawl +- wikipedia +pipeline_tag: +- fill-mask +widget: +- text: "København er 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 +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'}] +```