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 ...
添加上自动补齐参数padding=True后,可以看到attention_mask中补齐的token其mask=0: padded_sequences = tokenizer([sequence_a, sequence_b], padding=True) print(padded_sequences["input_ids"]) print(padded_sequences["attention_mask"]) # 输出:[[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0...
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...
将 input_ids 中对应的 token id 改成「词缀token id」。大功告成。
We strongly recommend passing in anattention_masksince your input_ids may be padded. Seehttps://huggingface.co/docs/transformers/troubleshooting#incorrect-output-when-padding-tokens-arent-masked. You may ignore this warning if yourpad_token_id(0) is identical to thebos_token_id(0),eos_token_id...
= self.tokenizer.pad_token_id).sum().item() - if token_count > self.max_length: - print("The text has been truncated.") - - return { - 'input_ids': inputs['input_ids'].squeeze(0), - 'attention_mask': inputs['attention_mask'].squeeze(0), - 'labels': torch.tensor(label,...
在ModelScope中，dummy_inputs可以有多种格式。以下是一些常见的dummy_inputs格式示例：...
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...
vocab= ["all","not","heroes","the","wear",".","capes"]inputs= [1,0,2,4] #"not""all""heroes""wear"output= gpt(inputs)next_token_id= np.argmax(output[-1]) # next_token_id =6next_token= vocab[next_token_id] # next_token ="capes" ...
= self.tokenizer.pad_token_id).sum().item() - if token_count > self.max_length: - print("The text has been truncated.") - - return { - 'input_ids': inputs['input_ids'].squeeze(0), - 'attention_mask': inputs['attention_mask'].squeeze(0), - 'labels': torch.tensor(label,...