[to #42322933]support type str for for zero-shot labels' input

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10506320

modelscope/pipelines/nlp/zero_shot_classification_pipeline.py

@@ -74,7 +74,8 @@ class ZeroShotClassificationPipeline(Pipeline):
         preprocess_params = {}
         postprocess_params = {}
         if 'candidate_labels' in kwargs:
-            candidate_labels = kwargs.pop('candidate_labels')
+            candidate_labels = self._parse_labels(
+                kwargs.pop('candidate_labels'))
             preprocess_params['candidate_labels'] = candidate_labels
             postprocess_params['candidate_labels'] = candidate_labels
         else:
@@ -84,6 +85,14 @@ class ZeroShotClassificationPipeline(Pipeline):
         postprocess_params['multi_label'] = kwargs.pop('multi_label', False)
         return preprocess_params, {}, postprocess_params
 
+    def _parse_labels(self, labels):
+        if isinstance(labels, str):
+            labels = labels.replace('，', ',')  # replace cn comma to en comma
+            labels = [
+                label.strip() for label in labels.split(',') if label.strip()
+            ]
+        return labels
+
     def forward(self, inputs: Dict[str, Any],
                 **forward_params) -> Dict[str, Any]:
         return self.model(**inputs, **forward_params)

tests/pipelines/test_zero_shot_classification.py

@@ -21,6 +21,7 @@ class ZeroShotClassificationTest(unittest.TestCase, DemoCompatibilityCheck):
 
     sentence = '全新突破 解放军运20版空中加油机曝光'
     labels = ['文化', '体育', '娱乐', '财经', '家居', '汽车', '教育', '科技', '军事']
+    labels_str = '文化, 体育, 娱乐, 财经, 家居, 汽车, 教育, 科技, 军事'
     template = '这篇文章的标题是{}'
     regress_tool = MsRegressTool(baseline=False)
 
@@ -40,6 +41,10 @@ class ZeroShotClassificationTest(unittest.TestCase, DemoCompatibilityCheck):
             f'sentence: {self.sentence}\n'
             f'pipeline1:{pipeline1(input=self.sentence,candidate_labels=self.labels)}'
         )
+        print(
+            f'sentence: {self.sentence}\n'
+            f'pipeline2: {pipeline2(self.sentence,candidate_labels=self.labels_str,hypothesis_template=self.template)}'
+        )
         print(
             f'sentence: {self.sentence}\n'
             f'pipeline2: {pipeline2(self.sentence,candidate_labels=self.labels,hypothesis_template=self.template)}'