[Question]: ERNIE模型支持512的token长度，那为何预测时要按照512的字符长度截断呢

[Mengyueke]

请提出你的问题

ERNIE模型支持512的token长度，那为何预测时要按照512的字符串长度截断呢，512字符串长度对于英文文本来说实在太短了

[Mengyueke]

在UIE中

[DrownFish19]

您好，可以提供具体代码位置吗？

[Mengyueke]

在PaddleNLP/paddlenlp/taskflow/information_extraction.py中，推理过程_single_stage_predict会调用auto_splitter函数对输入字符串进行切割

    def _single_stage_predict(self, inputs):
        input_texts = [d["text"] for d in inputs]
        prompts = [d["prompt"] for d in inputs]

        # max predict length should exclude the length of prompt and summary tokens
        max_predict_len = self._max_seq_len - len(max(prompts)) - self._summary_token_num

        if self._init_class in ["UIEX"]:
            bbox_list = [d["bbox"] for d in inputs]
            short_input_texts, short_bbox_list, input_mapping = self._auto_splitter(
                input_texts, max_predict_len, bbox_list=bbox_list, split_sentence=self._split_sentence
            )
        else:
            short_input_texts, input_mapping = self._auto_splitter(
                input_texts, max_predict_len, split_sentence=self._split_sentence
            )

也就是PaddleNLP/paddlenlp/taskflow
/task.py中的auto_splitter函数，这个函数是按照字符串长度进行切割的，如果对中文，字符串长度确实和token长度大致相同，但是英文则相差很大，这样切分的英文文本的token长度应该会远小于512

    def _auto_splitter(self, input_texts, max_text_len, bbox_list=None, split_sentence=False):
        """
        Split the raw texts automatically for model inference.
        Args:
            input_texts (List[str]): input raw texts.
            max_text_len (int): cutting length.
            bbox_list (List[float, float,float, float]): bbox for document input.
            split_sentence (bool): If True, sentence-level split will be performed.
                `split_sentence` will be set to False if bbox_list is not None since sentence-level split is not support for document.
        return:
            short_input_texts (List[str]): the short input texts for model inference.
            input_mapping (dict): mapping between raw text and short input texts.
        """
        input_mapping = {}
        short_input_texts = []
        cnt_org = 0
        cnt_short = 0
        with_bbox = False
        if bbox_list:
            with_bbox = True
            short_bbox_list = []
            if split_sentence:
                logger.warning(
                    "`split_sentence` will be set to False if bbox_list is not None since sentence-level split is not support for document."
                )
                split_sentence = False

        for idx in range(len(input_texts)):
            if not split_sentence:
                sens = [input_texts[idx]]
            else:
                sens = cut_chinese_sent(input_texts[idx])
            for sen in sens:
                lens = len(sen)
                if lens <= max_text_len:
                    short_input_texts.append(sen)
                    if with_bbox:
                        short_bbox_list.append(bbox_list[idx])
                    input_mapping.setdefault(cnt_org, []).append(cnt_short)
                    cnt_short += 1
                else:
                    temp_text_list = [sen[i : i + max_text_len] for i in range(0, lens, max_text_len)]
                    short_input_texts.extend(temp_text_list)
                    if with_bbox:
                        if bbox_list[idx] is not None:
                            temp_bbox_list = [
                                bbox_list[idx][i : i + max_text_len] for i in range(0, lens, max_text_len)
                            ]
                            short_bbox_list.extend(temp_bbox_list)
                        else:
                            short_bbox_list.extend([None for _ in range(len(temp_text_list))])
                    short_idx = cnt_short
                    cnt_short += math.ceil(lens / max_text_len)
                    temp_text_id = [short_idx + i for i in range(cnt_short - short_idx)]
                    input_mapping.setdefault(cnt_org, []).extend(temp_text_id)
            cnt_org += 1
        if with_bbox:
            return short_input_texts, short_bbox_list, input_mapping
        else:
            return short_input_texts, input_mapping