SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

[XLNet] Fix mems behavior (#8567)

Browse Source 
* fix mems in xlnet

* fix use_mems

* fix use_mem_len

* fix use mems

* clean docs

* fix tf typo

* make xlnet tf for generation work

* fix tf test

* refactor use cache

* add use cache for missing models

* correct use_cache in generate

* correct use cache in tf generate

* fix tf

* correct getattr typo

* make sylvain happy

* change in docs as well

* do not apply to cookie cutter statements

* fix tf test

* make pytorch model fully backward compatible
This commit is contained in:
Patrick von Platen
2020-11-25 22:54:59 +01:00
committed by GitHub
parent 369f1d77b4
commit 2a6fbe6a40
47 changed files with 259 additions and 134 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
docs/source/model_doc/xlmprophetnet.rst
View File

@@ -19,7 +19,7 @@ just the next token. Its architecture is identical to ProhpetNet, but the model
The abstract from the paper is the following:
*In this paper, we present a new sequence-to-sequence pre-training model called ProphetNet, which introduces a novel
*In this paper, we present a new sequence-to-sequence pretraining model called ProphetNet, which introduces a novel
self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism. Instead of
the optimization of one-step ahead prediction in traditional sequence-to-sequence model, the ProphetNet is optimized by
n-step ahead prediction which predicts the next n tokens simultaneously based on previous context tokens at each time
@@ -27,7 +27,7 @@ step. The future n-gram prediction explicitly encourages the model to plan for t
overfitting on strong local correlations. We pre-train ProphetNet using a base scale dataset (16GB) and a large scale
dataset (160GB) respectively. Then we conduct experiments on CNN/DailyMail, Gigaword, and SQuAD 1.1 benchmarks for
abstractive summarization and question generation tasks. Experimental results show that ProphetNet achieves new
state-of-the-art results on all these datasets compared to the models using the same scale pre-training corpus.*
state-of-the-art results on all these datasets compared to the models using the same scale pretraining corpus.*
The Authors' code can be found `here <https://github.com/microsoft/ProphetNet>`__.