Update README.md

README.md

@@ -9,4 +9,62 @@ metrics:
 pipeline_tag: zero-shot-classification
 ---
 
-This model is 
+This model has been referred to the following link : https://github.com/Huffon/klue-transformers-tutorial.git
+
+
+RoBERTa와 같이 token_type_ids를 사용하지 않는 모델의 경우, zero-shot pipeline을 바로 적용할 수 없습니다(transformers==4.7.0 기준)  
+따라서 다음과 같이 변환하는 코드를 넣어줘야 합니다. 해당 코드 또한 직접 수정하였습니다.  
+
+```python
+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:
+            # 수정부: 두 문장을 페어로 입력했을 때, `token_type_ids`가 자동으로 붙는 문제를 방지하기 위해 미리 두 문장을 `sep_token` 기준으로 이어주도록 함
+            sequence_pairs.append(f"{sequences} {tokenizer.sep_token} {hypothesis_template.format(label)}")
+
+        return sequence_pairs, sequences
+```
+
+이후 classifier를 정의할 때 이를 적용해야 됩니다.  
+```python
+classifier = pipeline(
+    "zero-shot-classification",
+    args_parser=CustomZeroShotClassificationArgumentHandler(),
+    model="pongjin/roberta_with_kornli"
+)
+```