| Here is represented tinybert model for German language. The model was created by distilling of bert base cased model in the way described in https://arxiv.org/abs/1909.10351 (TinyBERT: Distilling BERT for Natural Language Understanding) | |
| Versions: | |
| How to load model for LM 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)) |