Comments (2)
It may happen that certain weights get very large. Try using smaller gradient clipping (like 1, 0.1).
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It may happen that certain weights get very large. Try using smaller gradient clipping (like 1, 0.1).
@zcgzcgzcg1 Thanks for your reply. But it doesnt work according to your suggestion (gradient clipping 1, 0.1).
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Related Issues (20)
- Inference/Prediction module HOT 3
- train with bert model HOT 1
- train error with BERT model in pytorch 0.4.0 or 0.4.1 HOT 1
- Fluctuation on dev sanity checks
- where is the device information and where is the training step information?
- 关于fine-tuning bert的问题
- 关于TypeError的问题 HOT 3
- Why training with ELMo instead of BERT does not improve results ?
- How to use func::gen_upper_triangle
- Pre-trained model ?
- Download bert models HOT 5
- Python 3.4 compatibility
- This repo is missing important files
- Please add support for pytorch 1.0.0 and msgpack-numpy 0.4.4.1 HOT 2
- tf version HOT 2
- When context is larger than BERT_MAX_LEN HOT 1
- Mapping Bert model contents HOT 2
- CUDA error: out of memory HOT 1
- I ran this code directly, why do I just get 70.772% F1?
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