head_num=num_attention_heads, size_per_head=attention_head_size) 通过以上修改,我们在使用transformer_op_module的时候,就不需要强制指定batch size 和 seq length了, 表示生成模型的时候,类似这么配置: input_ids= tf.placeholder(tf.int32,(None, None),'input_ids')input_mask= tf.placeholder(tf.float3...
sentence transformer 中 max sequence length 单位sentence transformer中max sequence length单位 sentence transformer中max sequence length单位:句子变换器中最大序列长度单位©2022 Baidu |由 百度智能云 提供计算服务 | 使用百度前必读 | 文库协议 | 网站地图 | 百度营销 ...
For now, though, tackling sequence-length limitations is still a work in progress. But one thing’s clear: when we finally break through these constraints, the possibilities will explode. Imagine analyzing entire libraries for insights, processing massive genomes in seconds, or creating AI th...
并且,可以用比数据集中现有的最大回报更高的回报来作为初始输入传给DT,这表明DT具备外推的能力 What is the benefit of using a longer context length 本节对比较长的context 即K值与不使用context(标准的强化学习),证明过去的状态是有效的。打破了传统RL的范式 Does Decision Transformer perform effective long-t...
What is the benefit of using a longer context length? 为了评估访问先前状态(state)、行动(actions)和回报(returns)的重要性,我们对上下文长度K进行了消融实验。这很有趣,因为通常认为先前的状态(即K = 1)就足够用于强化学习算法。 下表显示,当K = 1时,Decision Transformer的性能显着较差,这表明过去的信息...
Computation in a typical Transformer-based large language model (LLM) can be characterized by batch size, hidden dimension, number of layers, and sequence length. Until now, system works for accelerating LLM training have focused on the first three ...
transpose(-2, -1)) # factor*ln(L_q)*L_k return Q_K, M_top #将S所有embed置为mean(V),此为输出shape def _get_initial_context(self, V, L_Q): # V-->[batch, head, length_v, embed] B, H, L_V, D = V.shape if not self.mask_flag: # V_sum = V.sum(dim=-2) # V...
The researchers explain their Block-Recurrent Transformer’s “strikingly simple” recurrent cell consists for the most part of an ordinary transformer layer applied in a recurrent fashion along the sequence length and uses cross-attention to attend to both the recurrent s...
Security Insights Additional navigation options master 2Branches29Tags Code README MIT license Linear Attention Transformer A fully featured Transformer that mixes (QKᵀ)V local attention with Q(KᵀV) global attention (scales linearly with respect to sequence length) for efficient long-range languag...
max_seq_length: int train_batch_size: int gradient_accumulation_steps: int eval_batch_size: int num_train_epochs: int weight_decay: float learning_rate: float adam_epsilon: float max_grad_norm: float do_lower_case: bool evaluate_during_training evaluate_during_training_steps evaluate_during_...