After 3 weeks of training textsum of tensorflow i didnt get any good result with my nvidia 1070 gtx , luckly i have found OpenNMT really nice job , I test Text Summarization and it gives a good result with the pretrained model .
I’m planning now on rebuild the model with larger vocab and start the training with pretrained wrd embedding and maybe I well go further by implementing new ideas , my questions are :
- How long did u take to achieve the result from Text Summarization pretrained model ?
- when i checked the preprocess documentation it requires valid_src & valid_tgt does that mean the model do the validation by itself while training (unlike the tensorflow model ) ? if so , will it stop automatically if it achieves a good avg_loss.
- as I can see u guys didnt implement the copy process for the unk tokkens , but u have the phrase_table. however speaking logically this table is used to replace unk with a specific tokken in the result , but what if i check that the tokken is OOV then i will add it to the phrase_table with the same tokken : example
if word “china” is OOV (unk) then I append this to phrase_table :
china|||china
isn’t that a workaround of the copy process.
Thank u