{
  "id": "huggingface/trainer-early-stopping-config",
  "signature": "ValueError: Early stopping requires `load_best_model_at_end=True` in the training arguments, but it is set to False.",
  "signature_zh": "ValueError: 提前停止需要在训练参数中设置 `load_best_model_at_end=True`，但它当前为 False。",
  "regex": "ValueError: Early stopping requires `load_best_model_at_end=True` in the training arguments, but it is set to False\\.",
  "domain": "huggingface",
  "category": "config_error",
  "subcategory": null,
  "root_cause": "The `EarlyStoppingCallback` is used with `TrainingArguments` where `load_best_model_at_end` is False, but early stopping requires this flag to be True to save the best model.",
  "root_cause_type": "generic",
  "root_cause_zh": "在 `TrainingArguments` 中 `load_best_model_at_end` 为 False 时使用了 `EarlyStoppingCallback`，但提前停止需要此标志为 True 才能保存最佳模型。",
  "versions": [
    {
      "version": "transformers 4.39.0",
      "introduced": null,
      "deprecated": null,
      "removed": null,
      "behavior_change": null,
      "status": "active"
    },
    {
      "version": "Python 3.10",
      "introduced": null,
      "deprecated": null,
      "removed": null,
      "behavior_change": null,
      "status": "active"
    }
  ],
  "os_specific": {},
  "dead_ends": [
    {
      "action": "",
      "why_fails": "The default is False, so removing it doesn't change behavior; the error persists.",
      "fail_rate": 0.9,
      "condition": "",
      "sources": []
    },
    {
      "action": "",
      "why_fails": "This directly contradicts the requirement; early stopping cannot function without loading the best model.",
      "fail_rate": 0.95,
      "condition": "",
      "sources": []
    }
  ],
  "workarounds": [
    {
      "action": "Set `load_best_model_at_end=True` in `TrainingArguments` and also set `metric_for_best_model` and `greater_is_better` appropriately. For example:\n\nfrom transformers import TrainingArguments, Trainer, EarlyStoppingCallback\ntraining_args = TrainingArguments(\n    output_dir='./results',\n    load_best_model_at_end=True,\n    metric_for_best_model='eval_loss',\n    greater_is_better=False,\n    evaluation_strategy='steps',\n    save_strategy='steps',\n)\ntrainer = Trainer(\n    model=model,\n    args=training_args,\n    train_dataset=train_dataset,\n    eval_dataset=eval_dataset,\n    callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],\n)",
      "success_rate": 0.95,
      "how": "Set `load_best_model_at_end=True` in `TrainingArguments` and also set `metric_for_best_model` and `greater_is_better` appropriately. For example:\n\nfrom transformers import TrainingArguments, Trainer, EarlyStoppingCallback\ntraining_args = TrainingArguments(\n    output_dir='./results',\n    load_best_model_at_end=True,\n    metric_for_best_model='eval_loss',\n    greater_is_better=False,\n    evaluation_strategy='steps',\n    save_strategy='steps',\n)\ntrainer = Trainer(\n    model=model,\n    args=training_args,\n    train_dataset=train_dataset,\n    eval_dataset=eval_dataset,\n    callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],\n)",
      "condition": "",
      "sources": []
    },
    {
      "action": "If you don't need early stopping, remove the `EarlyStoppingCallback` from the callbacks list and rely on manual checkpoint selection.",
      "success_rate": 0.9,
      "how": "If you don't need early stopping, remove the `EarlyStoppingCallback` from the callbacks list and rely on manual checkpoint selection.",
      "condition": "",
      "sources": []
    }
  ],
  "workarounds_zh": [
    "在 `TrainingArguments` 中设置 `load_best_model_at_end=True`，并适当设置 `metric_for_best_model` 和 `greater_is_better`。例如：\n\nfrom transformers import TrainingArguments, Trainer, EarlyStoppingCallback\ntraining_args = TrainingArguments(\n    output_dir='./results',\n    load_best_model_at_end=True,\n    metric_for_best_model='eval_loss',\n    greater_is_better=False,\n    evaluation_strategy='steps',\n    save_strategy='steps',\n)\ntrainer = Trainer(\n    model=model,\n    args=training_args,\n    train_dataset=train_dataset,\n    eval_dataset=eval_dataset,\n    callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],\n)",
    "如果不需要提前停止，从回调列表中移除 `EarlyStoppingCallback`，并依赖手动检查点选择。"
  ],
  "transition_graph": {
    "leads_to": [],
    "preceded_by": [],
    "frequently_confused_with": []
  },
  "official_doc_url": "https://huggingface.co/docs/transformers/en/main_classes/callback#transformers.EarlyStoppingCallback",
  "official_doc_section": null,
  "error_code": null,
  "verification_tier": "ai_generated",
  "confidence": 0.9,
  "fix_success_rate": 0.95,
  "resolvable": "true",
  "first_seen": "2024-04-05",
  "last_confirmed": "2024-06-01",
  "last_updated": "2024-06-01",
  "evidence_count": 1,
  "tags": [],
  "locale": "en",
  "aliases": []
}