finetune的时候为何没有把<|Human|>的loss给mask掉？

        with open(os.path.join(self.data_dir, f'{self.data_type}.jsonl'), 'r') as f:
            for line in f:
                sample = json.loads(line)

                chat = sample['chat']
                num_turns = int(sample['num_turns'])

                meta_instruction = sample['meta_instruction']
                instruction_ids = self.tokenizer.encode(meta_instruction)
                assert isinstance(instruction_ids, list) and len(instruction_ids) > 0
                
                input_ids = copy.deepcopy(instruction_ids)
                no_loss_spans = [(0, len(instruction_ids))]

                for i in range(num_turns):
                    cur_turn_ids = []
                    cur_no_loss_spans = []
                    cur_turn = chat[f'turn_{i+1}']
                    for key, value in cur_turn.items():

                        cur_ids = self.tokenizer.encode(value)

                        if key == 'Tool Responses':
                            # The format tokens (<|Results|>:...<eor>\n) should have losses. 
                            cur_no_loss_spans.append((len(input_ids + cur_turn_ids) + 5, len(input_ids + cur_turn_ids + cur_ids) - 2))    

                        assert isinstance(cur_ids, list) and len(cur_ids) > 0

                        cur_turn_ids.extend(cur_ids)

                    if len(input_ids + cur_turn_ids) > 2048:
                        break

                    input_ids.extend(cur_turn_ids)
                    no_loss_spans.extend(cur_no_loss_spans)

                if len(input_ids) == len(instruction_ids):
                    continue

                assert len(input_ids) > 0 and len(input_ids) <= 2048

                self.data.append(input_ids)
                self.no_loss_spans.append(no_loss_spans)

The issue shows a Python JSONL-loading loop that builds input_ids and no_loss_spans; start by locating where self.no_loss_spans is consumed during fine-tuning. Trace how the and Tool Responses sections are represented, then verify that the loss mask matches the intended training spans. Done means the masking behavior is clarified or corrected and covered by the project's existing validation path.