Comments (2)
@QQQrookie Hi,
we didn't use the bert-base-uncased
model to initialize word_embedding
. The dimensions 128 and 768 are projected by self.input_up_proj
. We didn't initialize the model with the pre-trained model, just leaving the code implementation. You are free to try.
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@summmeer Thank you for the prompt reply.
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Related Issues (20)
- Error when decoding HOT 1
- Only one gpu
- About the usage of `batch` in dataset (form of `batch`, `cond`) HOT 2
- Working with larger datasets HOT 2
- Dataset(2) in "text_datasets.py" HOT 1
- the Multi-GPU training acutally duplicates data in each GPU ? HOT 19
- Where decode "src" and "trg" ? HOT 1
- Nothing generated from decode HOT 2
- NCCL error
- License HOT 1
- Question About top-p sampling HOT 1
- q<n> metrics HOT 1
- NLL for q0-q2 is 0 but for q3 is >2 HOT 5
- Typo? train.py HOT 1
- Decoding only [UNK] token ? HOT 1
- Gradient accumulation HOT 1
- Taken <Pad> as a regular token could make model only learn the <Pad> information? HOT 7
- RoFormer model instead of BERT? HOT 1
- Size of the hidden dimension HOT 2
- Why is trained embedding orthogonal? HOT 1
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