transformers.tokenization_utils_base.PreTrainedTokenizerBase._from_pretrained() got multiple val解决方案

BERT label_encoder.fit_transform(data['intent'])

[INFO|tokenization_utils_base.py:2500] 2025-03-04 21:54:07,927 >> tokenizer config file saved in saves/DeepSeek-R1-1.5B-Distill/lora/train_2025-03-04-21-49-43/tokenizer_config.json [INFO|tokenization_utils_base.py:2509] 2025-03-04 21:54:07,927 >> Special tokens file saved in saves/DeepSeek-R1-1.5B-Distill/lora/train_2025-03-04-21-49-43/special_tokens_map.json ***** train metrics ***** epoch = 7.5714 num_input_tokens_seen = 149568 total_flos = 1297916GF train_loss = 2.2018 train_runtime = 0:00:50.19 train_samples_per_second = 11.156 train_steps_per_second = 0.598 Figure saved at: saves/DeepSeek-R1-1.5B-Distill/lora/train_2025-03-04-21-49-43/training_loss.png [WARNING|2025-03-04 21:54:08] llamafactory.extras.ploting:162 >> No metric eval_loss to plot. [WARNING|2025-03-04 21:54:08] llamafactory.extras.ploting:162 >> No metric eval_accuracy to plot. [INFO|modelcard.py:449] 2025-03-04 21:54:08,103 >> Dropping the following result as it does not have all the necessary fields: {'task': {'name': 'Causal Language Modeling', 'type': 'text-generation'}} swanlab: Experiment horse-8 has completed swanlab: 🌟 Run `swanlab watch /root/autodl-tmp/ai/LLaMA-Factory/swanlog` to view SwanLab Experiment Dashboard locally