Fix label mapping and boost training with batch_size=64.

@@ -29,6 +29,7 @@ os.environ.setdefault("CUDA_DEVICE_ORDER", "PCI_BUS_ID")
 # 清理可能由外部启动器注入的分布式环境变量，避免误触多卡/分布式
 for _k in ["RANK", "LOCAL_RANK", "WORLD_SIZE"]:
     os.environ.pop(_k, None)
+os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
 
 import numpy as np
 import torch
@@ -240,11 +241,22 @@ def main() -> None:
     text_col, label_col = autodetect_columns(train_df, args.text_col, args.label_col)
     print(f"[Info] 文本列: {text_col} | 标签列: {label_col}")
 
-    # 标签映射
-    label2id, id2label = build_label_mappings(train_df, label_col)
+    # 标签映射（使用 训练集∪验证集 的并集，避免验证集中出现新标签导致报错）
+    combined_labels_df = pd.concat([train_df[[label_col]], valid_df[[label_col]]], ignore_index=True)
+    label2id, id2label = build_label_mappings(combined_labels_df, label_col)
     if len(label2id) < 2:
         raise ValueError("标签类别数少于 2，无法训练分类模型。")
     print(f"[Info] 标签类别数: {len(label2id)}")
+    # 提示验证集中未出现在训练集的标签数量
+    try:
+        train_label_set = set(str(x) for x in train_df[label_col].dropna().astype(str).tolist())
+        valid_label_set = set(str(x) for x in valid_df[label_col].dropna().astype(str).tolist())
+        unseen_in_train = sorted(valid_label_set - train_label_set)
+        if unseen_in_train:
+            preview = ", ".join(unseen_in_train[:10])
+            print(f"[Warn] 验证集中存在 {len(unseen_in_train)} 个训练未出现的标签（已纳入映射以避免报错）。示例: {preview} ...")
+    except Exception:
+        pass
 
     # 数据集
     train_dataset = TextClassificationDataset(train_df, tokenizer, text_col, label_col, label2id, args.max_length)