inputs = {"input_ids":batch[0],"attention_mask":batch[1],"labels":batch[3]}if args.model_type !="distilbert":# XLM and RoBERTa don"t use segment_idsinputs["token_type_ids"] = (batch[2]if args.model_type in ["bert","xlnet"] else None) outputs = model(**inputs) outputs ...
please pass your input's attention_mask to obtain reliable results.” 这意味着你在输入数据时没有正确设置attention_mask和pad token id,这可能会影响模型的输出结果。 1. 确认问题 你遇到的问题是attention_mask和pad token id未设置,这可能导致模型在处理输入数据时产生不可预期的行为。 2. 解释为何需要设置...
{ - 'input_ids': inputs['input_ids'].squeeze(0), - 'attention_mask': inputs['attention_mask'].squeeze(0), - 'labels': torch.tensor(label, dtype=torch.float) - } - - elif self.prompt_style == 101: - new_dataset = row['new_dataset'] - SOTA = row['SOTA'] - OA = row...
During these events we' re not paying attention to the current world around us. Instead, we' re recalling memories, or creating and processing imagined futures.When engaged in mind wandering, our brains process these mental images using the same pathways used to receive inputs fr...
input_ids = [1,0]#"not""all"output_ids = generate(input_ids,3)# output_ids = [2, 4, 6]output_tokens = [vocab[i]foriinoutput_ids]#"heroes""wear""capes" 这个预测未来值(回归),并将其加回输入(自动)的过程就是为什么你可能看到 GPT 被描述为自回归的原因。
{ - 'input_ids': inputs['input_ids'].squeeze(0), - 'attention_mask': inputs['attention_mask'].squeeze(0), - 'labels': torch.tensor(label, dtype=torch.float) - } - - elif self.prompt_style == 101: - new_dataset = row['new_dataset'] - SOTA = row['SOTA'] - OA = row...