Comments (1)
Hi,
Thanks for the questions.
re 1: there is top_p in the code because I am curious about truncated sampling, similar to what people do in the Big GAN paper. It helps the sample quality a bit, and could be a interested sampling approach, but this experiment is not included in any results in the paper.
re 2: Technically this is not necessary, but this makes implementation easier, adoted from the openai codebase. You could turn it on or off, as long as you are consistent during training and inference.
Hope this helps!
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Related Issues (20)
- some problems on reproducing the results
- I wander where to find the model in the predictability HOT 1
- Training on A100
- Separate weights for word embedding and lm-head?
- Questions about the result of success rate of PPLM? HOT 2
- Why not directly use Emb(W) as X_0? HOT 2
- Error when running training script on Google Colab HOT 2
- Fail to load GPT2 pretrained model for attribute controled generation
- Reproducing Table 5: Sentence Infilling - CIDEr / BLEU-4 metrics HOT 1
- Baseline reproduction
- error when runing:Exception in thread Thread-4:·······ValueError: signal number 32 out of range
- Which classifier to use in custom_trainer.py for controllable generation?
- About the tT_loss HOT 1
- The difference between this code and the paper "IDDPM" in the run_loop function in train_util.py.
- The relevant code that caused the error is in the Controllable Text Generation section, after the model trained for 6 epochs and started evaluating, it raised a KeyError: 'eval_loss' HOT 2
- Questions about the NLL loss
- E2E training procedure
- Issue while generating controllable text generation
- How to Execute the Semantic Content Subtask with infill.py
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