Comments (3)
commented on June 28, 2024
1
您好，感谢您的提问，
一般来说Learning rate和能否正常训练没有太大关系。在我们的任务上8e-6和2e-5的区别 应该只是在最后结果上的差别1-2个F1，但是应该并不会造成没有办法收敛的情况。
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littlesulley
commented on June 28, 2024
不正常。可能其他地方哪里出错了。
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Maybewuss
commented on June 28, 2024
我直接用的dense，没有用MultiNonLinearClassifier，然后样本构造应该没啥问题，不知道是不是解码的问题，我解码的时候是要求start end的概率大于0.5并且要求span[start, end]的值也大于0.5。训练过程发现loss下降到一定程度后就不变 了，然后f1值也是一直接近0。然后一般finetune的时候lr都是在5e-5到2e-5之间这样，然后看到这边设置的是8e-6，是专门为了span loss调整的吗？
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Related Issues (20)
- 关于dataset处理offset的部分的问题 HOT 1
- 请问中文ner数据集（msra onto4） 您论文里的结果是在验证集上，还是在测试集上的呢?
- MSRA数据集start,end很快收敛，span的recall <50%
- RuntimeError: NCCL error in: /opt/conda/conda-bld/pytorch_1607370128159/work/torch/lib/c10d/ProcessGroupNCCL.cpp:31, unhandled cuda error, NCCL version 2.7.8
- 换成自己的数据集报错，不能训练 HOT 14
- train problem 卡在以下最后一行内容current training loss is : 0.01826123334467411 不动
- When reproduce zh_msra for both bert-tagger and bert-mrc, which bert pretrain model is used?
- conll03.sh can't reproduce the f1 score in paper
- mrc-ner脚本必须使用第0块显卡的问题
- inference 遇到错误 HOT 3
- Inference stage complains BertQueryNER were not initialized from the model checkpoint
- Inference stage complains BertQueryNER were not initialized from the model checkpoint
- Ontonoyes5.0的query HOT 1
- Project dependencies may have API risk issues
- the msra2src.py may have some problems HOT 4
- BERT DIR?
- Annotation Guideline Notes
- 使用sh文件复现时，修改DATA_DIR, BERT_DIR, OUTPUT_DIR后依旧无法运行 HOT 1
- 关于NER转换为MRC任务
- 大家是如何准备自己的数据集的？ HOT 3
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