It must be said that some problems still confuse me: 1. Whether to scale in the input layer (I try to replace it with layer specification); 2. The detailed setting of weight sharing between output projection matrix and embedding matrix in the adapter (I notice that inconsistent variance will lead to bad results); 3. The biggest confusion is that the variance increases with the calculation layer by layer (I am not sure if this phenomenon is reasonable, I will compare the behavior on the latest code). Finally, the detailed implementation is so important to the final performance, even if it is a subtle difference.
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| __init__.py | 正在载入提交数据... | |
| adaptive_loss.py | 正在载入提交数据... | |
| composite_loss.py | 正在载入提交数据... | |
| cross_entropy.py | 正在载入提交数据... | |
| ctc.py | 正在载入提交数据... | |
| fairseq_criterion.py | 正在载入提交数据... | |
| join_speech_and_text_loss.py | 正在载入提交数据... | |
| label_smoothed_cross_entropy.py | 正在载入提交数据... | |
| label_smoothed_cross_entropy_latency_augmented.py | 正在载入提交数据... | |
| label_smoothed_cross_entropy_with_alignment.py | 正在载入提交数据... | |
| label_smoothed_cross_entropy_with_ctc.py | 正在载入提交数据... | |
| legacy_masked_lm.py | 正在载入提交数据... | |
| masked_lm.py | 正在载入提交数据... | |
| model_criterion.py | 正在载入提交数据... | |
| nat_loss.py | 正在载入提交数据... | |
| sentence_prediction.py | 正在载入提交数据... | |
| sentence_ranking.py | 正在载入提交数据... | |
| wav2vec_criterion.py | 正在载入提交数据... |