Roberta With Kornli

🚀 零樣本分類模型：roberta_with_kornli

本模型基於klue/roberta-base，使用kor_nli數據集中的mnli和xnli進行微調，可用於零樣本分類任務。它解決了在特定模型下無法直接應用零樣本pipeline的問題，通過自定義參數處理類實現了零樣本分類功能。

🚀 快速開始

模型信息

參考鏈接

本模型參考了以下GitHub倉庫：https://github.com/Huffon/klue-transformers-tutorial.git

訓練參數

注意事項

RoBERTa這類不使用token_type_ids的模型，在transformers==4.7.0版本下無法直接應用零樣本pipeline。因此，需要添加如下轉換代碼，該代碼是參考上述GitHub倉庫代碼修改而來。

💻 使用示例

基礎用法

class ArgumentHandler(ABC):
    """
    Base interface for handling arguments for each :class:`~transformers.pipelines.Pipeline`.
    """

    @abstractmethod
    def __call__(self, *args, **kwargs):
        raise NotImplementedError()


class CustomZeroShotClassificationArgumentHandler(ArgumentHandler):
    """
    Handles arguments for zero-shot for text classification by turning each possible label into an NLI
    premise/hypothesis pair.
    """

    def _parse_labels(self, labels):
        if isinstance(labels, str):
            labels = [label.strip() for label in labels.split(",")]
        return labels

    def __call__(self, sequences, labels, hypothesis_template):
        if len(labels) == 0 or len(sequences) == 0:
            raise ValueError("You must include at least one label and at least one sequence.")
        if hypothesis_template.format(labels[0]) == hypothesis_template:
            raise ValueError(
                (
                    'The provided hypothesis_template "{}" was not able to be formatted with the target labels. '
                    "Make sure the passed template includes formatting syntax such as {{}} where the label should go."
                ).format(hypothesis_template)
            )

        if isinstance(sequences, str):
            sequences = [sequences]
        labels = self._parse_labels(labels)

        sequence_pairs = []
        for label in labels:
            # 修正部分：為防止輸入兩個句子對時自動添加 `token_type_ids`，提前按 `sep_token` 拼接兩個句子
            sequence_pairs.append(f"{sequences} {tokenizer.sep_token} {hypothesis_template.format(label)}")

        return sequence_pairs, sequences

高級用法

在定義分類器時應用上述代碼：

classifier = pipeline(
    "zero-shot-classification",
    args_parser=CustomZeroShotClassificationArgumentHandler(),
    model="pongjin/roberta_with_kornli"
)

分類示例

sequence = "배당락 D-1 코스피, 2330선 상승세...외인·기관 사자"	
candidate_labels =["외환",'환율', "경제", "금융", "부동산","주식"]

classifier(
    sequence,
    candidate_labels,
    hypothesis_template='이는 {}에 관한 것이다.',
)

>>{'sequence': '배당락 D-1 코스피, 2330선 상승세...외인·기관 사자',
 'labels': ['주식', '금융', '경제', '외환', '환율', '부동산'],
 'scores': [0.5052872896194458,
  0.17972524464130402,
  0.13852974772453308,
  0.09460823982954025,
  0.042949128895998,
  0.038900360465049744]}

📄 許可證

本項目採用Apache-2.0許可證。