模型推理结果不佳

[jqfu6868]

请教一下，本地复现跑的模型，进行inference后的结果与样例差距好多，并且预测的文本中并没有类似["[PLAYER]", "[TEAM]", "[COACH]", "[REFEREE]", "([TEAM])"]标识。

[jyrao]

哈咯，谢谢follow工作，这样给的信息量太少了，你可以提供一下你用的每一个checkpoint，命令行指令等尽可能多的具体信息。不然没有线索可以发现问题。

[jqfu6868]

hello 本地复现代码的过程中由于出现一个error，修改了一部分代码，不清楚是不是修改后的影响，附件文件附上. 我本地python环境是3.12.4，环境配置见requirements.txt。最终训练后的模型评分结果异常差 | | jqfumail5 | | ***@***.*** | ---- 回复的原邮件 ---- | 发件人 | Jiayuan ***@***.***> | | 发送日期 | 2026年1月29日 01:35 | | 收件人 | ***@***.***> | | 抄送人 | ***@***.***>, ***@***.***> | | 主题 | Re: [jyrao/MatchTime] 模型推理结果不佳 (Issue #8) | jyrao left a comment (jyrao/MatchTime#8) 哈咯，谢谢follow工作，这样给的信息量太少了，你可以提供一下你用的每一个checkpoint，命令行指令等尽可能多的具体信息。不然没有线索可以发现问题。 — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you authored the thread.Message ID: ***@***.***> accelerate==1.10.1 certifi==2025.8.3 charset-normalizer==3.4.3 click==8.3.0 filelock==3.19.1 fsspec==2025.9.0 hf-xet==1.1.10 huggingface-hub==0.35.0 idna==3.10 Jinja2==3.1.6 joblib==1.5.2 MarkupSafe==3.0.2 mpmath==1.3.0 networkx==3.5 numpy==2.3.3 nvidia-cublas-cu12==12.8.4.1 nvidia-cuda-cupti-cu12==12.8.90 nvidia-cuda-nvrtc-cu12==12.8.93 nvidia-cuda-runtime-cu12==12.8.90 nvidia-cudnn-cu12==9.10.2.21 nvidia-cufft-cu12==11.3.3.83 nvidia-cufile-cu12==1.13.1.3 nvidia-curand-cu12==10.3.9.90 nvidia-cusolver-cu12==11.7.3.90 nvidia-cusparse-cu12==12.5.8.93 nvidia-cusparselt-cu12==0.7.1 nvidia-nccl-cu12==2.27.3 nvidia-nvjitlink-cu12==12.8.93 nvidia-nvtx-cu12==12.8.90 packaging==25.0 psutil==7.1.0 PyYAML==6.0.2 regex==2025.9.18 requests==2.32.5 sacremoses==0.1.1 safetensors==0.6.2 sentencepiece==0.2.1 setuptools==72.1.0 sympy==1.14.0 tokenizers==0.22.1 torch==2.8.0 tqdm==4.67.1 transformers==4.56.2 triton==3.4.0 typing_extensions==4.15.0 urllib3==2.5.0 wheel==0.43.0 2026-03-08 21:23:39 - INFO - 训练日期：20260308，训练参数: dict_items([('feature_root', './features/Baidu_soccer_embeddings'), ('window', 3), ('tokenizer_name', '/root/autodl-tmp/huggingface/hub/llama-3-3b'), ('train_ann_root', './dataset/MatchTime/train'), ('train_batch_size', 16), ('train_num_workers', 4), ('train_timestamp_key', 'gameTime'), ('val_ann_root', './dataset/MatchTime/valid'), ('val_batch_size', 16), ('val_num_workers', 4), ('val_timestamp_key', 'gameTime'), ('lr', 0.0001), ('num_epoch', 1), ('num_query_tokens', 32), ('num_video_query_token', 32), ('num_features', 8576), ('fps', 1), ('model_output_dir', './ckpt/debug_train_output'), ('device', 'cuda:0'), ('continue_train', False), ('pre_max_CIDEr', 0.0), ('pre_epoch', 0), ('load_ckpt', './ckpt/model_save_best_val_CIDEr.pth')]) 2026-03-08 21:23:40 - INFO - 数据集加载完成 - 训练集: 2897, 验证集: 168 2026-03-08 21:23:40 - INFO - Batch大小 - 训练: 16, 验证: 16 2026-03-08 21:23:56 - INFO - Model and Checkpoints loaded! 2026-03-08 21:23:56 - INFO - Epoch 1/1 训练开始 2026-03-08 21:23:56 - INFO - mx_val_CIDEr before Epoch 1: 0.000 2026-03-08 21:25:14 - INFO - Epoch 1 训练完成，耗时: 77.19 秒 2026-03-08 21:25:14 - INFO - Epoch 1/1 验证开始 2026-03-08 21:25:49 - INFO - ✅ 保存训练时样本预测: ./ckpt/debug_train_output/training_sample_predictions.json 2026-03-08 21:25:49 - INFO - Epoch 1 验证完成，耗时: 35.25 秒 2026-03-08 21:25:49 - INFO - Epoch 1 Summary: Average Training Loss: 3.090, Average Validation scores: C:4.690 2026-03-08 21:25:53 - INFO - 模型检查点已保存: ./ckpt/debug_train_output/model_save_1_4.690.pth 2026-03-08 21:25:57 - INFO - 新的最佳模型已保存: ./ckpt/debug_train_output/model_save_best_val_CIDEr.pth，训练批次：1，CIDEr分数: 4.690 2026-03-08 21:25:57 - INFO - Epoch 1 总结 - 平均训练 Loss: 3.0901, 平均验证 CIDEr: 0.0469, 训练时间: 77.19s, 验证时间: 35.25s, 总耗时: 120.88s 2026-03-08 21:25:57 - INFO - 训练完成！最佳验证CIDEr分数: 4.690，发生在第 1 个周期。 2026-03-08 21:25:57 - INFO - 训练完成，正在生成指标图表... 2026-03-08 21:25:58 - INFO - 指标图表已保存至: ./ckpt/debug_train_output/metrics_plot_20260308.png 2026-03-08 21:25:58 - INFO - 指标数据已保存至: ./ckpt/debug_train_output/metrics_history_20260308.csv 2026-03-08 21:28:29 - INFO - 测试数据集加载完成，样本数: 282 2026-03-08 21:28:47 - INFO - Loaded soccer related token IDs: [0, 16387, 16388, 8196, 6, 16390, 16392, 8202, 11, 12, 13, 98315, 15, 16, 17, 8208, 19, 18, 21, 22, 20, 8209, 25, 23, 24601, 8220, 24, 30, 29, 32, 16411, 37, 38, 41003, 49, 50, 51, 8244, 16437, 41013, 57399, 54, 8242, 64, 65, 66, 8258, 67, 73, 75, 98381, 73806, 79, 49230, 81, 82, 83, 8273, 32855, 88, 32859, 24674, 49264, 24694, 65657, 8314, 49277, 32898, 16514, 41092, 41094, 24714, 8330, 8332, 8334, 8338, 57492, 8346, 8348, 16546, 57507, 8356, 41131, 8363, 57520, 16567, 24770, 16595, 8405, 220, 8415, 16609, 49378, 8430, 8434, 73973, 16632, 8446, 33024, 24833, 8450, 259, 256, 261, 8454, 65795, 264, 263, 266, 258, 268, 265, 16654, 270, 273, 57619, 278, 279, 57625, 281, 282, 285, 287, 288, 291, 293, 294, 295, 296, 41253, 16677, 8489, 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49139, 24569, 8187, 8190] 2026-03-08 21:28:47 - INFO - 加载训练好的检查点... 2026-03-08 21:28:47 - INFO - ✅ 检查点加载成功 2026-03-08 21:28:47 - INFO - - 成功加载键: -76 个 2026-03-08 21:28:47 - INFO - - 缺失键（预期）: 196 个（LLM 主体层） 2026-03-08 21:28:47 - INFO - - 意外键: 0 个 2026-03-08 21:28:47 - INFO - CSV 文件已创建: ./inference_result/20260308-debug_train/predict_baidu_window_model_save_1_4.690.pth.csv 2026-03-08 21:28:47 - INFO - 开始推理，生成 1 个预测/样本 2026-03-08 21:29:40 - WARNING - 训练时预测文件不存在，跳过对比 2026-03-08 21:29:40 - INFO - 推理完成！总样本数: 282 2026-03-08 21:29:40 - INFO - 结果已保存至: ./inference_result/20260308-debug_train/predict_baidu_window_model_save_1_4.690.pth.csv 2026-03-08 21:29:40 - INFO - 测试数据集加载完成，样本数: 282 2026-03-08 21:29:57 - INFO - Loaded soccer related token IDs: [0, 16387, 16388, 8196, 6, 16390, 16392, 8202, 11, 12, 13, 98315, 15, 16, 17, 8208, 19, 18, 21, 22, 20, 8209, 25, 23, 24601, 8220, 24, 30, 29, 32, 16411, 37, 38, 41003, 49, 50, 51, 8244, 16437, 41013, 57399, 54, 8242, 64, 65, 66, 8258, 67, 73, 75, 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7878, 16070, 40649, 81610, 7882, 32464, 16087, 16089, 7902, 24288, 57067, 57075, 16117, 24313, 81660, 16127, 48897, 7940, 7941, 16136, 7945, 7946, 7951, 24335, 7953, 24344, 48926, 24354, 16179, 16181, 40758, 7995, 8009, 8010, 24400, 8020, 48987, 57192, 49002, 81770, 32623, 65393, 81779, 8056, 57215, 65410, 8071, 24456, 32654, 8084, 16280, 24473, 65437, 8099, 8101, 8104, 32689, 40884, 73652, 24510, 24512, 90049, 8137, 90058, 49101, 32719, 8147, 16343, 16358, 32746, 24559, 16367, 16370, 49139, 24569, 8187, 8190] 2026-03-08 21:29:57 - INFO - 加载训练好的检查点... 2026-03-08 21:29:57 - INFO - ✅ 检查点加载成功 2026-03-08 21:29:57 - INFO - - 成功加载键: -76 个 2026-03-08 21:29:57 - INFO - - 缺失键（预期）: 196 个（LLM 主体层） 2026-03-08 21:29:57 - INFO - - 意外键: 0 个 2026-03-08 21:29:57 - INFO - CSV 文件已创建: ./inference_result/20260308-debug_train/predict_baidu_window_model_save_best_val_CIDEr.pth.csv 2026-03-08 21:29:57 - INFO - 开始推理，生成 1 个预测/样本 2026-03-08 21:30:49 - WARNING - 训练时预测文件不存在，跳过对比 2026-03-08 21:30:49 - INFO - 推理完成！总样本数: 282 2026-03-08 21:30:49 - INFO - 结果已保存至: ./inference_result/20260308-debug_train/predict_baidu_window_model_save_best_val_CIDEr.pth.csv 2026-03-08 21:33:18 - INFO - 训练日期：20260308，训练参数: dict_items([('feature_root', './features/Baidu_soccer_embeddings'), ('window', 3), ('tokenizer_name', '/root/autodl-tmp/huggingface/hub/llama-3-3b'), ('train_ann_root', './dataset/MatchTime/train'), ('train_batch_size', 16), ('train_num_workers', 4), ('train_timestamp_key', 'gameTime'), ('val_ann_root', './dataset/MatchTime/valid'), ('val_batch_size', 16), ('val_num_workers', 4), ('val_timestamp_key', 'gameTime'), ('lr', 0.0001), ('num_epoch', 48), ('num_query_tokens', 32), ('num_video_query_token', 32), ('num_features', 8576), ('fps', 1), ('model_output_dir', './ckpt/debug_train_output'), ('device', 'cuda:0'), ('continue_train', False), ('pre_max_CIDEr', 0.0), ('pre_epoch', 0), ('load_ckpt', './ckpt/model_save_best_val_CIDEr.pth')]) 2026-03-08 21:33:19 - INFO - 数据集加载完成 - 训练集: 2897, 验证集: 168 2026-03-08 21:33:19 - INFO - Batch大小 - 训练: 16, 验证: 16 2026-03-08 21:33:36 - INFO - Model and Checkpoints loaded! 2026-03-08 21:33:36 - INFO - Epoch 1/48 训练开始 2026-03-08 21:33:36 - INFO - mx_val_CIDEr before Epoch 1: 0.000 2026-03-08 21:34:53 - INFO - Epoch 1 训练完成，耗时: 77.36 秒 2026-03-08 21:34:53 - INFO - Epoch 1/48 验证开始 2026-03-08 21:35:29 - INFO - ✅ 保存训练时样本预测: ./ckpt/debug_train_output/training_sample_predictions.json 2026-03-08 21:35:29 - INFO - Epoch 1 验证完成，耗时: 35.16 秒 2026-03-08 21:35:29 - INFO - Epoch 1 Summary: Average Training Loss: 3.090, Average Validation scores: C:4.690 2026-03-08 21:35:33 - INFO - 模型检查点已保存: ./ckpt/debug_train_output/model_save_1_4.690.pth 2026-03-08 21:35:37 - INFO - 新的最佳模型已保存: ./ckpt/debug_train_output/model_save_best_val_CIDEr.pth，训练批次：1，CIDEr分数: 4.690 2026-03-08 21:35:37 - INFO - Epoch 2/48 训练开始 2026-03-08 21:35:37 - INFO - mx_val_CIDEr before Epoch 1: 4.690 2026-03-08 21:36:55 - INFO - Epoch 2 训练完成，耗时: 78.21 秒 2026-03-08 21:36:55 - INFO - Epoch 2/48 验证开始 2026-03-08 21:37:11 - INFO - Epoch 2 验证完成，耗时: 15.77 秒 2026-03-08 21:37:11 - INFO - Epoch 2 Summary: Average Training Loss: 2.789, Average Validation scores: C:4.966 2026-03-08 21:37:16 - INFO - 新的最佳模型已保存: ./ckpt/debug_train_output/model_save_best_val_CIDEr.pth，训练批次：2，CIDEr分数: 4.966 2026-03-08 21:37:16 - INFO - Epoch 3/48 训练开始 2026-03-08 21:37:16 - INFO - mx_val_CIDEr before Epoch 2: 4.966 2026-03-08 21:38:33 - INFO - Epoch 3 训练完成，耗时: 77.56 秒 2026-03-08 21:38:33 - INFO - Epoch 3/48 验证开始 2026-03-08 21:38:51 - INFO - Epoch 3 验证完成，耗时: 17.99 秒 2026-03-08 21:38:51 - INFO - Epoch 3 Summary: Average Training Loss: 2.737, Average Validation scores: C:6.244 2026-03-08 21:38:56 - INFO - 新的最佳模型已保存: ./ckpt/debug_train_output/model_save_best_val_CIDEr.pth，训练批次：3，CIDEr分数: 6.244 2026-03-08 21:38:56 - INFO - Epoch 4/48 训练开始 2026-03-08 21:38:56 - INFO - mx_val_CIDEr before Epoch 3: 6.244 2026-03-08 21:40:14 - INFO - Epoch 4 训练完成，耗时: 78.04 秒 2026-03-08 21:40:14 - INFO - Epoch 4/48 验证开始 2026-03-08 21:40:38 - INFO - Epoch 4 验证完成，耗时: 24.35 秒 2026-03-08 21:40:38 - INFO - Epoch 4 Summary: Average Training Loss: 2.687, Average Validation scores: C:3.934 2026-03-08 21:40:38 - INFO - Epoch 5/48 训练开始 2026-03-08 21:40:38 - INFO - mx_val_CIDEr before Epoch 3: 6.244 2026-03-08 21:41:56 - INFO - Epoch 5 训练完成，耗时: 78.32 秒 2026-03-08 21:41:56 - INFO - Epoch 5/48 验证开始 2026-03-08 21:42:17 - INFO - Epoch 5 验证完成，耗时: 20.48 秒 2026-03-08 21:42:17 - INFO - Epoch 5 Summary: Average Training Loss: 2.664, Average Validation scores: C:10.683 2026-03-08 21:42:21 - INFO - 新的最佳模型已保存: ./ckpt/debug_train_output/model_save_best_val_CIDEr.pth，训练批次：5，CIDEr分数: 10.683 2026-03-08 21:42:21 - INFO - Epoch 6/48 训练开始 2026-03-08 21:42:21 - INFO - mx_val_CIDEr before Epoch 5: 10.683 2026-03-08 21:43:40 - INFO - Epoch 6 训练完成，耗时: 78.42 秒 2026-03-08 21:43:40 - INFO - Epoch 6/48 验证开始 2026-03-08 21:44:02 - INFO - Epoch 6 验证完成，耗时: 22.71 秒 2026-03-08 21:44:02 - INFO - Epoch 6 Summary: Average Training Loss: 2.634, Average Validation scores: C:5.031 2026-03-08 21:44:02 - INFO - Epoch 7/48 训练开始 2026-03-08 21:44:02 - INFO - mx_val_CIDEr before Epoch 5: 10.683 2026-03-08 21:45:20 - INFO - Epoch 7 训练完成，耗时: 78.18 秒 2026-03-08 21:45:20 - INFO - Epoch 7/48 验证开始 2026-03-08 21:45:45 - INFO - Epoch 7 验证完成，耗时: 24.22 秒 2026-03-08 21:45:45 - INFO - Epoch 7 Summary: Average Training Loss: 2.604, Average Validation scores: C:4.887 2026-03-08 21:45:45 - INFO - Epoch 8/48 训练开始 2026-03-08 21:45:45 - INFO - mx_val_CIDEr before Epoch 5: 10.683 2026-03-08 21:47:03 - INFO - Epoch 8 训练完成，耗时: 78.20 秒 2026-03-08 21:47:03 - INFO - Epoch 8/48 验证开始 2026-03-08 21:47:21 - INFO - Epoch 8 验证完成，耗时: 18.37 秒 2026-03-08 21:47:21 - INFO - Epoch 8 Summary: Average Training Loss: 2.559, Average Validation scores: C:10.802 2026-03-08 21:47:25 - INFO - 新的最佳模型已保存: ./ckpt/debug_train_output/model_save_best_val_CIDEr.pth，训练批次：8，CIDEr分数: 10.802 2026-03-08 21:47:25 - INFO - Epoch 9/48 训练开始 2026-03-08 21:47:25 - INFO - mx_val_CIDEr before Epoch 8: 10.802 2026-03-08 21:48:43 - INFO - Epoch 9 训练完成，耗时: 78.09 秒 2026-03-08 21:48:43 - INFO - Epoch 9/48 验证开始 2026-03-08 21:49:08 - INFO - Epoch 9 验证完成，耗时: 24.66 秒 2026-03-08 21:49:08 - INFO - Epoch 9 Summary: Average Training Loss: 2.534, Average Validation scores: C:9.025 2026-03-08 21:49:11 - INFO - 模型检查点已保存: ./ckpt/debug_train_output/model_save_9_9.025.pth 2026-03-08 21:49:11 - INFO - Epoch 10/48 训练开始 2026-03-08 21:49:11 - INFO - mx_val_CIDEr before Epoch 8: 10.802 2026-03-08 21:50:30 - INFO - Epoch 10 训练完成，耗时: 78.32 秒 2026-03-08 21:50:30 - INFO - Epoch 10/48 验证开始 2026-03-08 21:51:04 - INFO - Epoch 10 验证完成，耗时: 34.80 秒 2026-03-08 21:51:04 - INFO - Epoch 10 Summary: Average Training Loss: 2.492, Average Validation scores: C:7.101 2026-03-08 21:51:04 - INFO - Epoch 11/48 训练开始 2026-03-08 21:51:04 - INFO - mx_val_CIDEr before Epoch 8: 10.802 2026-03-08 21:52:23 - INFO - Epoch 11 训练完成，耗时: 78.61 秒 2026-03-08 21:52:23 - INFO - Epoch 11/48 验证开始 2026-03-08 21:52:42 - INFO - Epoch 11 验证完成，耗时: 19.01 秒 2026-03-08 21:52:42 - INFO - Epoch 11 Summary: Average Training Loss: 2.455, Average Validation scores: C:7.069 2026-03-08 21:52:42 - INFO - Epoch 12/48 训练开始 2026-03-08 21:52:42 - INFO - mx_val_CIDEr before Epoch 8: 10.802 2026-03-08 21:54:01 - INFO - Epoch 12 训练完成，耗时: 78.65 秒 2026-03-08 21:54:01 - INFO - Epoch 12/48 验证开始 2026-03-08 21:54:27 - INFO - Epoch 12 验证完成，耗时: 26.45 秒 2026-03-08 21:54:27 - INFO - Epoch 12 Summary: Average Training Loss: 2.426, Average Validation scores: C:8.695 2026-03-08 21:54:27 - INFO - Epoch 13/48 训练开始 2026-03-08 21:54:27 - INFO - mx_val_CIDEr before Epoch 8: 10.802 2026-03-08 21:55:46 - INFO - Epoch 13 训练完成，耗时: 78.34 秒 2026-03-08 21:55:46 - INFO - Epoch 13/48 验证开始 2026-03-08 21:56:15 - INFO - Epoch 13 验证完成，耗时: 29.00 秒 2026-03-08 21:56:15 - INFO - Epoch 13 Summary: Average Training Loss: 2.378, Average Validation scores: C:9.318 2026-03-08 21:56:15 - INFO - Epoch 14/48 训练开始 2026-03-08 21:56:15 - INFO - mx_val_CIDEr before Epoch 8: 10.802 2026-03-08 21:57:33 - INFO - Epoch 14 训练完成，耗时: 78.27 秒 2026-03-08 21:57:33 - INFO - Epoch 14/48 验证开始 2026-03-08 21:58:10 - INFO - Epoch 14 验证完成，耗时: 37.35 秒 2026-03-08 21:58:10 - INFO - Epoch 14 Summary: Average Training Loss: 2.355, Average Validation scores: C:7.803 2026-03-08 21:58:10 - INFO - Epoch 15/48 训练开始 2026-03-08 21:58:10 - INFO - mx_val_CIDEr before Epoch 8: 10.802 2026-03-08 21:59:29 - INFO - Epoch 15 训练完成，耗时: 78.64 秒 2026-03-08 21:59:29 - INFO - Epoch 15/48 验证开始 2026-03-08 21:59:56 - INFO - Epoch 15 验证完成，耗时: 26.74 秒 2026-03-08 21:59:56 - INFO - Epoch 15 Summary: Average Training Loss: 2.330, Average Validation scores: C:8.870 2026-03-08 21:59:56 - INFO - Epoch 16/48 训练开始 2026-03-08 21:59:56 - INFO - mx_val_CIDEr before Epoch 8: 10.802 2026-03-08 22:01:13 - INFO - Epoch 16 训练完成，耗时: 77.89 秒 2026-03-08 22:01:13 - INFO - Epoch 16/48 验证开始 2026-03-08 22:01:46 - INFO - Epoch 16 验证完成，耗时: 32.97 秒 2026-03-08 22:01:46 - INFO - Epoch 16 Summary: Average Training Loss: 2.289, Average Validation scores: C:9.706 2026-03-08 22:01:46 - INFO - Epoch 17/48 训练开始 2026-03-08 22:01:46 - INFO - mx_val_CIDEr before Epoch 8: 10.802 2026-03-08 22:03:04 - INFO - Epoch 17 训练完成，耗时: 77.96 秒 2026-03-08 22:03:04 - INFO - Epoch 17/48 验证开始 2026-03-08 22:03:37 - INFO - Epoch 17 验证完成，耗时: 32.86 秒 2026-03-08 22:03:37 - INFO - Epoch 17 Summary: Average Training Loss: 2.243, Average Validation scores: C:9.288 2026-03-08 22:03:41 - INFO - 模型检查点已保存: ./ckpt/debug_train_output/model_save_17_9.288.pth 2026-03-08 22:03:41 - INFO - Epoch 18/48 训练开始 2026-03-08 22:03:41 - INFO - mx_val_CIDEr before Epoch 8: 10.802 2026-03-08 22:04:59 - INFO - Epoch 18 训练完成，耗时: 78.38 秒 2026-03-08 22:04:59 - INFO - Epoch 18/48 验证开始 2026-03-08 22:05:26 - INFO - Epoch 18 验证完成，耗时: 26.73 秒 2026-03-08 22:05:26 - INFO - Epoch 18 Summary: Average Training Loss: 2.304, Average Validation scores: C:9.782 2026-03-08 22:05:26 - INFO - Epoch 19/48 训练开始 2026-03-08 22:05:26 - INFO - mx_val_CIDEr before Epoch 8: 10.802 2026-03-08 22:06:44 - INFO - Epoch 19 训练完成，耗时: 78.32 秒 2026-03-08 22:06:44 - INFO - Epoch 19/48 验证开始 2026-03-08 22:07:06 - INFO - Epoch 19 验证完成，耗时: 22.20 秒 2026-03-08 22:07:06 - INFO - Epoch 19 Summary: Average Training Loss: 2.194, Average Validation scores: C:15.629 2026-03-08 22:07:10 - INFO - 新的最佳模型已保存: ./ckpt/debug_train_output/model_save_best_val_CIDEr.pth，训练批次：19，CIDEr分数: 15.629 2026-03-08 22:07:10 - INFO - Epoch 20/48 训练开始 2026-03-08 22:07:10 - INFO - mx_val_CIDEr before Epoch 19: 15.629 2026-03-08 22:08:28 - INFO - Epoch 20 训练完成，耗时: 78.30 秒 2026-03-08 22:08:28 - INFO - Epoch 20/48 验证开始 2026-03-08 22:08:54 - INFO - Epoch 20 验证完成，耗时: 25.91 秒 2026-03-08 22:08:54 - INFO - Epoch 20 Summary: Average Training Loss: 2.147, Average Validation scores: C:7.624 2026-03-08 22:08:54 - INFO - Epoch 21/48 训练开始 2026-03-08 22:08:54 - INFO - mx_val_CIDEr before Epoch 19: 15.629 2026-03-08 22:10:13 - INFO - Epoch 21 训练完成，耗时: 78.86 秒 2026-03-08 22:10:13 - INFO - Epoch 21/48 验证开始 2026-03-08 22:10:42 - INFO - Epoch 21 验证完成，耗时: 29.27 秒 2026-03-08 22:10:42 - INFO - Epoch 21 Summary: Average Training Loss: 2.108, Average Validation scores: C:7.111 2026-03-08 22:10:42 - INFO - Epoch 22/48 训练开始 2026-03-08 22:10:42 - INFO - mx_val_CIDEr before Epoch 19: 15.629 2026-03-08 22:12:01 - INFO - Epoch 22 训练完成，耗时: 79.16 秒 2026-03-08 22:12:01 - INFO - Epoch 22/48 验证开始 2026-03-08 22:12:35 - INFO - Epoch 22 验证完成，耗时: 33.89 秒 2026-03-08 22:12:35 - INFO - Epoch 22 Summary: Average Training Loss: 2.060, Average Validation scores: C:13.192 2026-03-08 22:12:35 - INFO - Epoch 23/48 训练开始 2026-03-08 22:12:35 - INFO - mx_val_CIDEr before Epoch 19: 15.629 2026-03-08 22:13:55 - INFO - Epoch 23 训练完成，耗时: 79.37 秒 2026-03-08 22:13:55 - INFO - Epoch 23/48 验证开始 2026-03-08 22:14:30 - INFO - Epoch 23 验证完成，耗时: 35.26 秒 2026-03-08 22:14:30 - INFO - Epoch 23 Summary: Average Training Loss: 2.013, Average Validation scores: C:8.242 2026-03-08 22:14:30 - INFO - Epoch 24/48 训练开始 2026-03-08 22:14:30 - INFO - mx_val_CIDEr before Epoch 19: 15.629 2026-03-08 22:15:48 - INFO - Epoch 24 训练完成，耗时: 78.48 秒 2026-03-08 22:15:48 - INFO - Epoch 24/48 验证开始 2026-03-08 22:16:23 - INFO - Epoch 24 验证完成，耗时: 34.84 秒 2026-03-08 22:16:23 - INFO - Epoch 24 Summary: Average Training Loss: 1.972, Average Validation scores: C:8.924 2026-03-08 22:16:23 - INFO - Epoch 25/48 训练开始 2026-03-08 22:16:23 - INFO - mx_val_CIDEr before Epoch 19: 15.629 2026-03-08 22:17:42 - INFO - Epoch 25 训练完成，耗时: 78.44 秒 2026-03-08 22:17:42 - INFO - Epoch 25/48 验证开始 2026-03-08 22:18:13 - INFO - Epoch 25 验证完成，耗时: 31.15 秒 2026-03-08 22:18:13 - INFO - Epoch 25 Summary: Average Training Loss: 2.003, Average Validation scores: C:9.750 2026-03-08 22:18:16 - INFO - 模型检查点已保存: ./ckpt/debug_train_output/model_save_25_9.750.pth 2026-03-08 22:18:16 - INFO - Epoch 26/48 训练开始 2026-03-08 22:18:16 - INFO - mx_val_CIDEr before Epoch 19: 15.629 2026-03-08 22:19:35 - INFO - Epoch 26 训练完成，耗时: 78.55 秒 2026-03-08 22:19:35 - INFO - Epoch 26/48 验证开始 2026-03-08 22:20:10 - INFO - Epoch 26 验证完成，耗时: 35.39 秒 2026-03-08 22:20:10 - INFO - Epoch 26 Summary: Average Training Loss: 1.895, Average Validation scores: C:6.377 2026-03-08 22:20:10 - INFO - Epoch 27/48 训练开始 2026-03-08 22:20:10 - INFO - mx_val_CIDEr before Epoch 19: 15.629 2026-03-08 22:21:29 - INFO - Epoch 27 训练完成，耗时: 78.59 秒 2026-03-08 22:21:29 - INFO - Epoch 27/48 验证开始 2026-03-08 22:22:13 - INFO - Epoch 27 验证完成，耗时: 44.81 秒 2026-03-08 22:22:13 - INFO - Epoch 27 Summary: Average Training Loss: 1.870, Average Validation scores: C:8.897 2026-03-08 22:22:13 - INFO - Epoch 28/48 训练开始 2026-03-08 22:22:13 - INFO - mx_val_CIDEr before Epoch 19: 15.629 2026-03-08 22:23:32 - INFO - Epoch 28 训练完成，耗时: 78.60 秒 2026-03-08 22:23:32 - INFO - Epoch 28/48 验证开始 2026-03-08 22:24:01 - INFO - Epoch 28 验证完成，耗时: 28.75 秒 2026-03-08 22:24:01 - INFO - Epoch 28 Summary: Average Training Loss: 1.822, Average Validation scores: C:9.815 2026-03-08 22:24:01 - INFO - Epoch 29/48 训练开始 2026-03-08 22:24:01 - INFO - mx_val_CIDEr before Epoch 19: 15.629 2026-03-08 22:25:20 - INFO - Epoch 29 训练完成，耗时: 79.32 秒 2026-03-08 22:25:20 - INFO - Epoch 29/48 验证开始 2026-03-08 22:26:00 - INFO - Epoch 29 验证完成，耗时: 39.80 秒 2026-03-08 22:26:00 - INFO - Epoch 29 Summary: Average Training Loss: 1.796, Average Validation scores: C:9.216 2026-03-08 22:26:00 - INFO - Epoch 30/48 训练开始 2026-03-08 22:26:00 - INFO - mx_val_CIDEr before Epoch 19: 15.629 2026-03-08 22:27:19 - INFO - Epoch 30 训练完成，耗时: 78.97 秒 2026-03-08 22:27:19 - INFO - Epoch 30/48 验证开始 2026-03-08 22:27:55 - INFO - Epoch 30 验证完成，耗时: 36.48 秒 2026-03-08 22:27:55 - INFO - Epoch 30 Summary: Average Training Loss: 1.746, Average Validation scores: C:12.323 2026-03-08 22:27:55 - INFO - Epoch 31/48 训练开始 2026-03-08 22:27:55 - INFO - mx_val_CIDEr before Epoch 19: 15.629 2026-03-08 22:29:14 - INFO - Epoch 31 训练完成，耗时: 79.00 秒 2026-03-08 22:29:14 - INFO - Epoch 31/48 验证开始 2026-03-08 22:29:48 - INFO - Epoch 31 验证完成，耗时: 33.68 秒 2026-03-08 22:29:48 - INFO - Epoch 31 Summary: Average Training Loss: 1.652, Average Validation scores: C:7.719 2026-03-08 22:29:48 - INFO - Epoch 32/48 训练开始 2026-03-08 22:29:48 - INFO - mx_val_CIDEr before Epoch 19: 15.629 2026-03-08 22:31:07 - INFO - Epoch 32 训练完成，耗时: 78.78 秒 2026-03-08 22:31:07 - INFO - Epoch 32/48 验证开始 2026-03-08 22:31:30 - INFO - Epoch 32 验证完成，耗时: 23.13 秒 2026-03-08 22:31:30 - INFO - Epoch 32 Summary: Average Training Loss: 1.636, Average Validation scores: C:9.544 2026-03-08 22:31:30 - INFO - Epoch 33/48 训练开始 2026-03-08 22:31:30 - INFO - mx_val_CIDEr before Epoch 19: 15.629 2026-03-08 22:32:49 - INFO - Epoch 33 训练完成，耗时: 79.32 秒 2026-03-08 22:32:49 - INFO - Epoch 33/48 验证开始 2026-03-08 22:33:23 - INFO - Epoch 33 验证完成，耗时: 33.59 秒 2026-03-08 22:33:23 - INFO - Epoch 33 Summary: Average Training Loss: 1.585, Average Validation scores: C:6.192 2026-03-08 22:33:26 - INFO - 模型检查点已保存: ./ckpt/debug_train_output/model_save_33_6.192.pth 2026-03-08 22:33:26 - INFO - Epoch 34/48 训练开始 2026-03-08 22:33:26 - INFO - mx_val_CIDEr before Epoch 19: 15.629 2026-03-08 22:34:45 - INFO - Epoch 34 训练完成，耗时: 78.42 秒 2026-03-08 22:34:45 - INFO - Epoch 34/48 验证开始 2026-03-08 22:35:26 - INFO - Epoch 34 验证完成，耗时: 41.74 秒 2026-03-08 22:35:26 - INFO - Epoch 34 Summary: Average Training Loss: 1.545, Average Validation scores: C:7.246 2026-03-08 22:35:26 - INFO - Epoch 35/48 训练开始 2026-03-08 22:35:26 - INFO - mx_val_CIDEr before Epoch 19: 15.629 2026-03-08 22:36:45 - INFO - Epoch 35 训练完成，耗时: 78.44 秒 2026-03-08 22:36:45 - INFO - Epoch 35/48 验证开始 2026-03-08 22:37:22 - INFO - Epoch 35 验证完成，耗时: 37.05 秒 2026-03-08 22:37:22 - INFO - Epoch 35 Summary: Average Training Loss: 1.521, Average Validation scores: C:9.124 2026-03-08 22:37:22 - INFO - Epoch 36/48 训练开始 2026-03-08 22:37:22 - INFO - mx_val_CIDEr before Epoch 19: 15.629 2026-03-08 22:38:41 - INFO - Epoch 36 训练完成，耗时: 78.77 秒 2026-03-08 22:38:41 - INFO - Epoch 36/48 验证开始 2026-03-08 22:39:11 - INFO - Epoch 36 验证完成，耗时: 30.60 秒 2026-03-08 22:39:11 - INFO - Epoch 36 Summary: Average Training Loss: 1.521, Average Validation scores: C:12.693 2026-03-08 22:39:11 - INFO - Epoch 37/48 训练开始 2026-03-08 22:39:11 - INFO - mx_val_CIDEr before Epoch 19: 15.629 2026-03-08 22:40:30 - INFO - Epoch 37 训练完成，耗时: 78.98 秒 2026-03-08 22:40:30 - INFO - Epoch 37/48 验证开始 2026-03-08 22:41:04 - INFO - Epoch 37 验证完成，耗时: 33.98 秒 2026-03-08 22:41:04 - INFO - Epoch 37 Summary: Average Training Loss: 1.489, Average Validation scores: C:6.385 2026-03-08 22:41:04 - INFO - Epoch 38/48 训练开始 2026-03-08 22:41:04 - INFO - mx_val_CIDEr before Epoch 19: 15.629 2026-03-08 22:42:23 - INFO - Epoch 38 训练完成，耗时: 78.45 秒 2026-03-08 22:42:23 - INFO - Epoch 38/48 验证开始 2026-03-08 22:42:52 - INFO - Epoch 38 验证完成，耗时: 29.15 秒 2026-03-08 22:42:52 - INFO - Epoch 38 Summary: Average Training Loss: 1.464, Average Validation scores: C:7.097 2026-03-08 22:42:52 - INFO - Epoch 39/48 训练开始 2026-03-08 22:42:52 - INFO - mx_val_CIDEr before Epoch 19: 15.629 2026-03-08 22:44:10 - INFO - Epoch 39 训练完成，耗时: 78.15 秒 2026-03-08 22:44:10 - INFO - Epoch 39/48 验证开始 2026-03-08 22:44:44 - INFO - Epoch 39 验证完成，耗时: 34.15 秒 2026-03-08 22:44:44 - INFO - Epoch 39 Summary: Average Training Loss: 1.417, Average Validation scores: C:6.179 2026-03-08 22:44:44 - INFO - Epoch 40/48 训练开始 2026-03-08 22:44:44 - INFO - mx_val_CIDEr before Epoch 19: 15.629 2026-03-08 22:46:03 - INFO - Epoch 40 训练完成，耗时: 78.34 秒 2026-03-08 22:46:03 - INFO - Epoch 40/48 验证开始 2026-03-08 22:46:35 - INFO - Epoch 40 验证完成，耗时: 32.48 秒 2026-03-08 22:46:35 - INFO - Epoch 40 Summary: Average Training Loss: 1.406, Average Validation scores: C:4.746 2026-03-08 22:46:35 - INFO - Epoch 41/48 训练开始 2026-03-08 22:46:35 - INFO - mx_val_CIDEr before Epoch 19: 15.629 2026-03-08 22:47:54 - INFO - Epoch 41 训练完成，耗时: 78.63 秒 2026-03-08 22:47:54 - INFO - Epoch 41/48 验证开始 2026-03-08 22:48:28 - INFO - Epoch 41 验证完成，耗时: 34.45 秒 2026-03-08 22:48:28 - INFO - Epoch 41 Summary: Average Training Loss: 1.389, Average Validation scores: C:7.254 2026-03-08 22:48:31 - INFO - 模型检查点已保存: ./ckpt/debug_train_output/model_save_41_7.254.pth 2026-03-08 22:48:31 - INFO - Epoch 42/48 训练开始 2026-03-08 22:48:31 - INFO - mx_val_CIDEr before Epoch 19: 15.629 2026-03-08 22:49:50 - INFO - Epoch 42 训练完成，耗时: 78.87 秒 2026-03-08 22:49:50 - INFO - Epoch 42/48 验证开始 2026-03-08 22:50:28 - INFO - Epoch 42 验证完成，耗时: 37.31 秒 2026-03-08 22:50:28 - INFO - Epoch 42 Summary: Average Training Loss: 1.378, Average Validation scores: C:9.289 2026-03-08 22:50:28 - INFO - Epoch 43/48 训练开始 2026-03-08 22:50:28 - INFO - mx_val_CIDEr before Epoch 19: 15.629 2026-03-08 22:51:46 - INFO - Epoch 43 训练完成，耗时: 78.56 秒 2026-03-08 22:51:46 - INFO - Epoch 43/48 验证开始 2026-03-08 22:52:28 - INFO - Epoch 43 验证完成，耗时: 41.42 秒 2026-03-08 22:52:28 - INFO - Epoch 43 Summary: Average Training Loss: 1.376, Average Validation scores: C:4.679 2026-03-08 22:52:28 - INFO - Epoch 44/48 训练开始 2026-03-08 22:52:28 - INFO - mx_val_CIDEr before Epoch 19: 15.629 2026-03-08 22:53:47 - INFO - Epoch 44 训练完成，耗时: 79.03 秒 2026-03-08 22:53:47 - INFO - Epoch 44/48 验证开始 2026-03-08 22:54:24 - INFO - Epoch 44 验证完成，耗时: 36.85 秒 2026-03-08 22:54:24 - INFO - Epoch 44 Summary: Average Training Loss: 1.372, Average Validation scores: C:5.825 2026-03-08 22:54:24 - INFO - Epoch 45/48 训练开始 2026-03-08 22:54:24 - INFO - mx_val_CIDEr before Epoch 19: 15.629 2026-03-08 22:55:42 - INFO - Epoch 45 训练完成，耗时: 78.03 秒 2026-03-08 22:55:42 - INFO - Epoch 45/48 验证开始 2026-03-08 22:56:17 - INFO - Epoch 45 验证完成，耗时: 35.04 秒 2026-03-08 22:56:17 - INFO - Epoch 45 Summary: Average Training Loss: 1.357, Average Validation scores: C:7.409 2026-03-08 22:56:17 - INFO - Epoch 46/48 训练开始 2026-03-08 22:56:17 - INFO - mx_val_CIDEr before Epoch 19: 15.629 2026-03-08 22:57:35 - INFO - Epoch 46 训练完成，耗时: 78.49 秒 2026-03-08 22:57:35 - INFO - Epoch 46/48 验证开始 2026-03-08 22:58:22 - INFO - Epoch 46 验证完成，耗时: 47.17 秒 2026-03-08 22:58:22 - INFO - Epoch 46 Summary: Average Training Loss: 1.335, Average Validation scores: C:5.529 2026-03-08 22:58:22 - INFO - Epoch 47/48 训练开始 2026-03-08 22:58:22 - INFO - mx_val_CIDEr before Epoch 19: 15.629 2026-03-08 22:59:40 - INFO - Epoch 47 训练完成，耗时: 78.15 秒 2026-03-08 22:59:40 - INFO - Epoch 47/48 验证开始 2026-03-08 23:00:16 - INFO - Epoch 47 验证完成，耗时: 35.83 秒 2026-03-08 23:00:16 - INFO - Epoch 47 Summary: Average Training Loss: 1.394, Average Validation scores: C:5.485 2026-03-08 23:00:16 - INFO - Epoch 48/48 训练开始 2026-03-08 23:00:16 - INFO - mx_val_CIDEr before Epoch 19: 15.629 2026-03-08 23:01:35 - INFO - Epoch 48 训练完成，耗时: 78.66 秒 2026-03-08 23:01:35 - INFO - Epoch 48/48 验证开始 2026-03-08 23:02:17 - INFO - Epoch 48 验证完成，耗时: 41.82 秒 2026-03-08 23:02:17 - INFO - Epoch 48 Summary: Average Training Loss: 1.305, Average Validation scores: C:4.441 2026-03-08 23:02:17 - INFO - Epoch 48 总结 - 平均训练 Loss: 1.3046, 平均验证 CIDEr: 0.0444, 训练时间: 78.66s, 验证时间: 41.82s, 总耗时: 120.48s 2026-03-08 23:02:17 - INFO - 训练完成！最佳验证CIDEr分数: 15.629，发生在第 19 个周期。 2026-03-08 23:02:17 - INFO - 训练完成，正在生成指标图表... 2026-03-08 23:02:17 - INFO - 指标图表已保存至: ./ckpt/debug_train_output/metrics_plot_20260308.png 2026-03-08 23:02:17 - INFO - 指标数据已保存至: ./ckpt/debug_train_output/metrics_history_20260308.csv 2026-03-08 23:04:04 - INFO - 测试数据集加载完成，样本数: 282 2026-03-08 23:04:22 - INFO - Loaded soccer related token IDs: [0, 16387, 16388, 8196, 6, 16390, 16392, 8202, 11, 12, 13, 98315, 15, 16, 17, 8208, 19, 18, 21, 22, 20, 8209, 25, 23, 24601, 8220, 24, 30, 29, 32, 16411, 37, 38, 41003, 49, 50, 51, 8244, 16437, 41013, 57399, 54, 8242, 64, 65, 66, 8258, 67, 73, 75, 98381, 73806, 79, 49230, 81, 82, 83, 8273, 32855, 88, 32859, 24674, 49264, 24694, 65657, 8314, 49277, 32898, 16514, 41092, 41094, 24714, 8330, 8332, 8334, 8338, 57492, 8346, 8348, 16546, 57507, 8356, 41131, 8363, 57520, 16567, 24770, 16595, 8405, 220, 8415, 16609, 49378, 8430, 8434, 73973, 16632, 8446, 33024, 24833, 8450, 259, 256, 261, 8454, 65795, 264, 263, 266, 258, 268, 265, 16654, 270, 273, 57619, 278, 279, 57625, 281, 282, 285, 287, 288, 291, 293, 294, 295, 296, 41253, 16677, 8489, 57645, 303, 304, 305, 57648, 306, 311, 16696, 65848, 41273, 315, 33085, 16706, 323, 325, 326, 33095, 331, 16718, 8530, 16723, 8538, 49499, 349, 350, 8545, 57698, 357, 98662, 358, 359, 362, 74092, 364, 16750, 367, 369, 16757, 374, 375, 8577, 387, 388, 389, 8582, 8586, 398, 399, 8592, 82320, 16785, 403, 8599, 409, 410, 8603, 411, 16797, 8606, 414, 417, 420, 421, 422, 8614, 90536, 8617, 74157, 430, 74159, 8622, 433, 65970, 8625, 436, 434, 437, 439, 33207, 441, 442, 8638, 8640, 449, 65985, 451, 453, 459, 41420, 463, 466, 8659, 468, 470, 8668, 477, 481, 41442, 33251, 482, 25060, 484, 8681, 499, 502, 503, 8696, 505, 508, 8704, 514, 516, 8710, 520, 33291, 523, 8718, 527, 8723, 74260, 49685, 535, 82457, 25114, 539, 16924, 8736, 57890, 16930, 57891, 8741, 90663, 552, 555, 8748, 16944, 49715, 25139, 16949, 568, 98873, 16957, 574, 16961, 578, 8770, 8774, 584, 49737, 16399, 74317, 16975, 596, 16983, 603, 8797, 8798, 605, 8800, 16994, 16996, 613, 617, 8813, 8814, 8815, 626, 74354, 628, 634, 635, 636, 25215, 8835, 644, 645, 8838, 649, 17033, 653, 17037, 656, 658, 659, 660, 662, 66203, 41630, 671, 66209, 17058, 675, 25251, 17057, 682, 58026, 49836, 685, 8877, 690, 17078, 25270, 58038, 8893

[jyrao]

粗略的看，我们用的是meta-llama/Meta-Llama-3-8B-Instruct，我看你用llama3-3b，可能会出现这个代码不能让词库扩充的问题，需要调整一下。词库扩充的代码，在我这里是：https://github.com/jyrao/MatchTime/blob/main/models/matchvoice_model.py

    self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_ckpt)
    self.tokenizer.add_tokens(["[PLAYER]","[TEAM]","[COACH]","[REFEREE]","([TEAM])"], special_tokens=True)

可能不同模型不一样，得check一下。

[elite525625-hub]

感谢指导，确实是这一块没生效