GetToThePoint
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Pytorch implementation of "Get to the point: Get To The Point: Summarization with Pointer-Generator Networks"
You are using **softmax + NLLLoss** You should either use **log_softmax + NLLLoss** or **softmax + CrossEntropyLoss** https://discuss.pytorch.org/t/is-log-softmax-nllloss-crossentropyloss/9352
code : while (epoch < args.epochs): epoch += 1 random.shuffle(file_list) print("length of file list is:",len(file_list)) for file in file_list: opt.zero_grad() with open(os.path.join(args.data_dir, file)) as f: minibatch = f.read() stories, summaries...
I am trying to train pointer generator network . After training for 10k iterations it starts overfitting. Any suggestions on why this might be happening. coverage_loss is not used. Note-...
when I use the package spacy to split words, I find the result has lot of error. maybe it will affect the final result.
https://github.com/mjc92/GetToThePoint/blob/e59c06de957d98cedab6a2eacc8d2d44dd1f3e33/model.py#L173 Why it's multiplied by attn_mask instead of mask?
the copydecoder not exists in the project
Hi, Thanks for your work on implementing this model in PyTorch. Just wanted to know if this implementation managed to reproduce the results mentioned in the paper?
Hi, Thanks for the pytorch implementation. I just wanted to know how do I go about running this? Considering that the numpy arrays are already saved, I just want to...