emb = output.last_hidden_state.mean(dim=1) 在这个示例代码中,我们首先使用GPT2Model.from_pretrained函数加载GPT-3模型。然后,使用GPT2Tokenizer.from_pretrained函数创建一个tokenizer对象,并将输入数据转换为对应的input_ids张量。接着,我们将input_ids张量输入到模型中,进行前向推理。最后,使用output.last_hidden...
mean(axis=0, keepdim=True) return ((weight_tensor - 1) * embs), pooled_base + pooled def mask_inds(tokens, inds, mask_token): clip_len = len(tokens[0]) inds_set = set(inds) new_tokens = [[mask_token if i*clip_len + j in inds_set else t for j, t in ...
[x] + [Zflat], dim=1) elif self.arch_interaction_op == "cat": # concatenation features (into a row vector) R = torch.cat([x] + ly, dim=1) else: sys.exit( "ERROR: --arch-interaction-op=" + self.arch_interaction_op + " is not supported" ) return R def forward(self, ...