>>> # Initializing a model (with random weights) from the google-bert/bert-base-uncased style configuration >>> model = BertModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "bert" def __init__() ... 1. 2. 3. 4....
cd /mnt/sda1/transdat/bert-demo/bert/ export BERT_BASE_DIR=/mnt/sda1/transdat/bert-demo/bert/chinese_L-12_H-768_A-12 export GLUE_DIR=/mnt/sda1/transdat/bert-demo/bert/data export TRAINED_CLASSIFIER=/mnt/sda1/transdat/bert-demo/bert/output export EXP_NAME=mobile_0 sudo python run_mo...
BERT的全称是Bidirectional Encoder Representation from Transformers,即双向Transformer的Encoder,是一种用于自然语言处理(NLP)的预训练技术。Bert-base模型是一个12层,768维,12个自注意头(self attention head),110M参数的神经网络结构,它的整体框架是由多层transformer的编码器堆叠而成的。
bert4torch是一个基于pytorch的训练框架,前期以效仿和实现bert4keras的主要功能为主,方便加载多类预训练模型进行finetune,提供了中文注释方便用户理解模型结构。主要是期望应对新项目时,可以直接调用不同的预训练模型直接finetune,或方便用户基于bert进行修改,快速验证自己的idea;节省在github上clone各种项目耗时耗力,且本...
(self,bert: BertForMaskedLM,model_args: ModelArguments,):super(RetroMAEForPretraining, self).__init__()self.lm = bertifhasattr(self.lm,'bert'):self.decoder_embeddings = self.lm.bert.embeddingselif hasattr(self.lm,'roberta'):self.d...
"""Configuration for `BertModel`.""" def __init__(self, vocab_size, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, ...
ImageDataTypeConversion ImagePadding AIPP相关图形处理算子,性能硬件最优。 ☆☆☆ CropAndResize 仅功能支持,性能较差。 ☆ ResizeBilinear ResizeBilinearV2 Interp 大部分场景性能硬件最优,个别场景待优化。 ☆☆☆ ResizeNearestNeighbor Upsample 大部分场景性能硬件最优,个别场景待优化。 ☆☆☆ Crop 仅...
"model_type": "bert", "num_attention_heads": 12, "num_hidden_layers": 3, "output_past": true, "pad_token_id": 0, "pooler_fc_size": 768, "pooler_num_attention_heads": 12, "pooler_num_fc_layers": 3, "pooler_size_per_head": 128, ...
self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, input_ids, token_type_ids=None): seq_length = input_ids.size(1) position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device) ...
lang_model_name='hub/bert-base-uncased' launcher='none' load_from='glip_tiny_mmdet-c24ce662.pth' log_level='INFO' log_processor=dict( by_epoch=True, type='LogProcessor', window_size=50) model=dict( backbone=dict( attn_drop_rate=0.0, ...