Comments (1)
Hi,
Thanks for the question!
I think modality='e2e' is used for conditional generation task of table-to-text, and this is some additional experiments we include in the repo, but not in the Diffusion-LM paper. So if you are trying to replicate experiments in the paper, you probably dont need to run this line. Try modality='e2e-tgt' to replicate the language modeling training of Diffusioin-LM on e2e dataset.
To answer this question, this script is MBR decoding for conditional table-text generation. For a code of very similar purpose, please refer to https://github.com/XiangLi1999/Diffusion-LM/blob/main/improved-diffusion/anlg_infill/mbr_eval.py and
.from diffusion-lm.
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
- Seq2Seq tasks with Diffusion LM
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