zero-shot pipeline multi_class -> multi_label (#10727)

examples/research_projects/zero-shot-distillation/distill_classifier.py

@@ -49,7 +49,7 @@ class TeacherModelArguments:
     teacher_batch_size: Optional[int] = field(
         default=32, metadata={"help": "Batch size for generating teacher predictions."}
     )
-    multi_class: Optional[bool] = field(
+    multi_label: Optional[bool] = field(
         default=False,
         metadata={
             "help": (
@@ -163,7 +163,7 @@ def get_teacher_predictions(
     hypothesis_template: str,
     batch_size: int,
     temperature: float,
-    multi_class: bool,
+    multi_label: bool,
     use_fast_tokenizer: bool,
     no_cuda: bool,
     fp16: bool,
@@ -203,7 +203,7 @@ def get_teacher_predictions(
     logits = torch.cat(logits, dim=0)  # N*K x 3
     nli_logits = logits.reshape(len(examples), len(class_names), -1)[..., [contr_id, entail_id]]  # N x K x 2
 
-    if multi_class:
+    if multi_label:
         # softmax over (contr, entail) logits for each class independently
         nli_prob = (nli_logits / temperature).softmax(-1)
     else:
@@ -285,7 +285,7 @@ def main():
         teacher_args.hypothesis_template,
         teacher_args.teacher_batch_size,
         teacher_args.temperature,
-        teacher_args.multi_class,
+        teacher_args.multi_label,
         data_args.use_fast_tokenizer,
         training_args.no_cuda,
         training_args.fp16,