self.word_embed = self.t5_base.get_encoder().get_input_embeddings() for param in self.t5_base.parameters(): param.requires_grad = requires_grad def forward(self, input_ids: torch.LongTensor = None, inputs_embeds: Optional[torch.FloatTensor] = None, ) -> torch.Tensor: if input_ids i...
BART-large:12encoder, 12decoder, 1024hidden T5-base:12encoder, 12decoder, 768 hidden, 220M parameters(2x bert-base) T5-large: 24encoder, 24decoder, 1024hidden, 770M parameters T5-large的模型大小是BART-large的两倍。 综合训练时间和模型大小,T5-large和BART-large可以互相比较,但是由于细节的实现上...
"sub_group_size": 1e9,"reduce_bucket_size": "auto","stage3_prefetch_bucket_size": "auto","stage3_param_persistence_threshold": "auto","stage3_max_live_parameters": 1e9,"stage3_max_reuse_distance": 1e9,
BART-large:12encoder, 12decoder, 1024hidden T5-base:12encoder, 12decoder, 768 hidden, 220M parameters(2x bert-base) T5-large: 24encoder, 24decoder, 1024hidden, 770M parameters T5-large的模型大小是BART-large的两倍。 综合训练时间和模型大小,T5-large和BART-large可以互相比较,但是由于细节的实现上...
{"params": [pforn, pinmodel.named_parameters()ifany(ndinnforndinno_decay)],"weight_decay": 0.0, }, ] optimizer= AdamW(optimizer_grouped_parameters, lr=self.hparams.learning_rate, eps=self.hparams.adam_epsilon) self.opt=optimizerreturn[optimizer]defoptimizer_step(self, epoch, batch_idx, op...
论文《How Much Knowledge Can You Pack Into the Parameters of a Language Model?》探讨了语言模型在没有外部知识的情况下,通过预训练来存储和检索知识的有效性。具体应用场景如下: 1.应用场景概述 开放域问答(Open-domain Question Answering,简称ODQA):模型在没有外部知识源的情况下,直接从其参数中检索知识以回...
{"stage":3,"overlap_comm":true,"contiguous_gradients":true,"sub_group_size":1e9,"reduce_bucket_size":"auto","stage3_prefetch_bucket_size":"auto","stage3_param_persistence_threshold":"auto","stage3_max_live_parameters":1e9,"stage3_max_reuse_distance":1e9,"stage3_gather_16bit_...
"stage3_max_live_parameters":1e9, "stage3_max_reuse_distance":1e9, "stage3_gather_16bit_weights_on_model_save":false }, "gradient_accumulation_steps":"auto", "gradient_clipping":"auto", "steps_per_print":2000, "train_batch_size":"auto", ...
Parameters item1 T1 The value of the tuple's first component. item2 T2 The value of the tuple's second component. item3 T3 The value of the tuple's third component. item4 T4 The value of the tuple's fourth component. item5 T5 The value of the tuple's fifth component. item...
"""# the following 2 hyperparameters are task-specificmax_source_length=512max_target_length=128# Suppose we have the following 2 training examples:input_sequence_1="Welcome to NYC"output_sequence_1="Bienvenue à NYC"input_sequence_2="HuggingFace is a company"output_sequence_2="HuggingFace est...