fix #5324

Former-commit-id: f7aa06c9c0b18c28419ea5792410915d3f322cbf

src/llamafactory/train/sft/metric.py

@@ -45,6 +45,9 @@ if is_rouge_available():
 
 
 def eval_logit_processor(logits: "torch.Tensor", labels: "torch.Tensor") -> "torch.Tensor":
+    r"""
+    Computes the token with the largest likelihood to reduce memory footprint.
+    """
     if isinstance(logits, (list, tuple)):
         if logits[0].dim() == 3:  # (batch_size, seq_len, vocab_size)
             logits = logits[0]
@@ -59,6 +62,9 @@ def eval_logit_processor(logits: "torch.Tensor", labels: "torch.Tensor") -> "tor
 
 @dataclass
 class ComputeAccuracy:
+    r"""
+    Computes accuracy and supports `batch_eval_metrics`.
+    """
     def _dump(self) -> Optional[Dict[str, float]]:
         result = None
         if hasattr(self, "score_dict"):
@@ -84,6 +90,8 @@ class ComputeAccuracy:
 @dataclass
 class ComputeSimilarity:
     r"""
+    Computes text similarity scores and supports `batch_eval_metrics`.
+
     Wraps the tokenizer into metric functions, used in CustomSeq2SeqTrainer.
     """