Update tasks/text.py

tasks/text.py

@@ -37,7 +37,7 @@ class ConspiracyClassification768(
     PyTorchModelHubMixin, 
     # optionally, you can add metadata which gets pushed to the model card
 ):    
-    def __init__(self, num_classes):
+    def __init__(self, num_classes=8):
         super().__init__()
         self.h1 = nn.Linear(768, 100)
         self.h2 = nn.Linear(100, 100)
@@ -70,7 +70,7 @@ class CTBERT(
     PyTorchModelHubMixin, 
     # optionally, you can add metadata which gets pushed to the model card
 ):
-    def __init__(self, num_classes):
+    def __init__(self, num_classes=8):
         super().__init__()
         self.bert = BertForPreTraining.from_pretrained('digitalepidemiologylab/covid-twitter-bert-v2')    
         self.bert.cls.seq_relationship = nn.Linear(1024, num_classes)
@@ -86,7 +86,7 @@ class conspiracyModelBase(
     PyTorchModelHubMixin, 
     # optionally, you can add metadata which gets pushed to the model card
 ):    
-    def __init__(self, num_classes):
+    def __init__(self, num_classes=8):
         super().__init__()
         self.n_classes = num_classes
         self.bert = ModernBertForSequenceClassification.from_pretrained('answerdotai/ModernBERT-base', num_labels=num_classes)    
@@ -101,7 +101,7 @@ class conspiracyModelLarge(
     PyTorchModelHubMixin, 
     # optionally, you can add metadata which gets pushed to the model card
 ):    
-    def __init__(self, num_classes):
+    def __init__(self, num_classes=8):
         super().__init__()
         self.n_classes = num_classes
         self.bert = ModernBertForSequenceClassification.from_pretrained('answerdotai/ModernBERT-large', num_labels=num_classes)    
@@ -116,7 +116,7 @@ class gteModelLarge(
     PyTorchModelHubMixin, 
     # optionally, you can add metadata which gets pushed to the model card
 ):    
-    def __init__(self, num_classes):
+    def __init__(self, num_classes=8):
         super().__init__()
         self.n_classes = num_classes
         self.gte = AutoModel.from_pretrained('Alibaba-NLP/gte-large-en-v1.5', trust_remote_code=True)
@@ -133,7 +133,7 @@ class gteModel(
     PyTorchModelHubMixin, 
     # optionally, you can add metadata which gets pushed to the model card
 ):    
-    def __init__(self, num_classes):
+    def __init__(self, num_classes=8):
         super().__init__()
         self.n_classes = num_classes
         self.gte = AutoModel.from_pretrained('Alibaba-NLP/gte-base-en-v1.5', trust_remote_code=True)