ut run passed

modelscope/models/nlp/sbert_for_nli.py

@@ -17,5 +17,5 @@ class SbertForNLI(SbertForSequenceClassificationBase):
             model_cls (Optional[Any], optional): model loader, if None, use the
                 default loader to load model weights, by default None.
         """
-        super().__init__(model_dir, *args, **kwargs)
+        super().__init__(model_dir, *args, model_args={"num_labels": 3}, **kwargs)
         assert self.model.config.num_labels == 3

modelscope/models/nlp/sbert_for_sentiment_classification.py

@@ -1,13 +1,14 @@
 from modelscope.utils.constant import Tasks
 from .sbert_for_sequence_classification import SbertForSequenceClassificationBase
 from ..builder import MODELS
+from modelscope.metainfo import Models
 
 __all__ = ['SbertForSentimentClassification']
 
 
 @MODELS.register_module(
     Tasks.sentiment_classification,
-    module_name=r'sbert-sentiment-classification')
+    module_name=Models.structbert)
 class SbertForSentimentClassification(SbertForSequenceClassificationBase):
 
     def __init__(self, model_dir: str, *args, **kwargs):

modelscope/models/nlp/sbert_for_sequence_classification.py

@@ -4,6 +4,7 @@ from ..base import Model
 import numpy as np
 import json
 import os
+import torch
 from sofa.models.sbert.modeling_sbert import SbertPreTrainedModel, SbertModel
 
 
@@ -33,9 +34,11 @@ class SbertTextClassfier(SbertPreTrainedModel):
 
 class SbertForSequenceClassificationBase(Model):
 
-    def __init__(self, model_dir: str, *args, **kwargs):
+    def __init__(self, model_dir: str, model_args=None, *args, **kwargs):
         super().__init__(model_dir, *args, **kwargs)
-        self.model = SbertTextClassfier.from_pretrained(model_dir)
+        if model_args is None:
+            model_args = {}
+        self.model = SbertTextClassfier.from_pretrained(model_dir, **model_args)
         self.id2label = {}
         self.label_path = os.path.join(self.model_dir, 'label_mapping.json')
         if os.path.exists(self.label_path):
@@ -43,8 +46,17 @@ class SbertForSequenceClassificationBase(Model):
                 self.label_mapping = json.load(f)
             self.id2label = {idx: name for name, idx in self.label_mapping.items()}
 
+    def train(self):
+        return self.model.train()
+
+    def eval(self):
+        return self.model.eval()
+
     def forward(self, input: Dict[str, Any]) -> Dict[str, np.ndarray]:
-        return self.model.forward(input)
+        input_ids = torch.tensor(input['input_ids'], dtype=torch.long)
+        token_type_ids = torch.tensor(
+            input['token_type_ids'], dtype=torch.long)
+        return self.model.forward(input_ids, token_type_ids)
 
     def postprocess(self, input, **kwargs):
         logits = input["logits"]

modelscope/models/nlp/sbert_for_zero_shot_classification.py

@@ -26,6 +26,12 @@ class SbertForZeroShotClassification(Model):
         from sofa import SbertForSequenceClassification
         self.model = SbertForSequenceClassification.from_pretrained(model_dir)
 
+    def train(self):
+        return self.model.train()
+
+    def eval(self):
+        return self.model.eval()
+    
     def forward(self, input: Dict[str, Any]) -> Dict[str, np.ndarray]:
         """return the result by the model
 

modelscope/pipelines/nlp/nli_pipeline.py

@@ -69,7 +69,7 @@ class NLIPipeline(Pipeline):
         new_result = list()
         for pred in preds:
             new_result.append({
-                'pred': self.label_id_to_name[pred],
+                'pred': self.model.id2label[pred],
                 'prob': float(probs[b][pred]),
                 'logit': float(logits[b][pred])
             })

modelscope/pipelines/nlp/sentiment_classification_pipeline.py

@@ -70,7 +70,7 @@ class SentimentClassificationPipeline(Pipeline):
         new_result = list()
         for pred in preds:
             new_result.append({
-                'pred': self.label_id_to_name[pred],
+                'pred': self.model.id2label[pred],
                 'prob': float(probs[b][pred]),
                 'logit': float(logits[b][pred])
             })

modelscope/pipelines/nlp/zero_shot_classification_pipeline.py

@@ -44,7 +44,7 @@ class ZeroShotClassificationPipeline(Pipeline):
         if preprocessor is None:
             preprocessor = ZeroShotClassificationPreprocessor(
                 sc_model.model_dir)
-        model.eval()
+        sc_model.eval()
         super().__init__(model=sc_model, preprocessor=preprocessor, **kwargs)
 
     def _sanitize_parameters(self, **kwargs):