Need help for retraining and cross validation and see if the ROUGE score matches exactly (or better) with the numbers reported in the paper.
I just train for 500k iteration (with batch size 8) with pointer generation enabled + coverage loss disabled and next 100k iteration (with batch size 8) with pointer generation enabled + coverage loss enabled.
a contextual, biasable, word-or-sentence-or-paragraph extractive summarizer powered by the latest in text embeddings (Bert, Universal Sentence Encoder, Flair)
😉🌀🍓 TextRank implementation in Golang with extendable features (summarization, phrase extraction) and multithreading (goroutine) support (Go 1.8, 1.9, 1.10)
A next step for better generation is to implement a beam search for the generation. An example of it can be seen on the huggingface repo here, and this would need adding such a function to the GenerativeT5 model in onnxt5/models.py
Source code for the NAACL 2019 paper "SEQ^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression"
Need help for retraining and cross validation and see if the ROUGE score matches exactly (or better) with the numbers reported in the paper.
I just train for 500k iteration (with batch size 8) with pointer generation enabled + coverage loss disabled and next 100k iteration (with batch size 8) with pointer generation enabled + coverage loss enabled.
It would be great if someone can help re-r