readme: ---
  language:
  - de
  tags:
  - tinybert
  - fill-mask
  datasets:
  - wiki
  ---
  
  
  Here is represented tinybert model for German language (de). The model was created by distilling of bert base cased model(https://huggingface.co/dbmdz/bert-base-german-cased) in the way described in https://arxiv.org/abs/1909.10351 (TinyBERT: Distilling BERT for Natural Language Understanding)
  
  Dataset:
  German Wikipedia Text Corpus - https://github.com/t-systems-on-site-services-gmbh/german-wikipedia-text-corpus
  
  Versions:
    torch==1.4.0
    transformers==4.8.1
  
  How to load model for LM(fill-mask) task: 
  
    tokenizer = transformers.BertTokenizer.from_pretrained(model_dir + '/vocab.txt', do_lower_case=False)
    config = transformers.BertConfig.from_json_file(model_dir+'config.json')
    model = transformers.BertModel(config=config)
    model.pooler = nn.Sequential(nn.Linear(in_features=model.config.hidden_size, out_features=model.config.hidden_size, bias=True),
                                 nn.LayerNorm((model.config.hidden_size,), eps=1e-12, elementwise_affine=True),
                                 nn.Linear(in_features=model.config.hidden_size, out_features=len(tokenizer), bias=True))
  
    model.resize_token_embeddings(len(tokenizer))
      
    checkpoint = torch.load(model_dir+'/pytorch_model.bin', map_location=torch.device('cuda'))
    model.load_state_dict(checkpoint)
  
  
  In case of NER or Classification task we have to load model for LM task and change pooler: 
  
    model.pooler = nn.Sequential(nn.Dropout(p=config.hidden_dropout_prob, inplace=False), 
                                 nn.Linear(in_features=config.hidden_size, out_features=n_classes, bias=True))
                                 
  