==============
deep-keyphrase
==============
Implement some keyphrase generation algorithm
.. image:: https://img.shields.io/github/workflow/status/supercoderhawk/deep-keyphrase/ci.svg
.. image:: https://img.shields.io/pypi/v/deep-keyphrase.svg
:target: https://pypi.org/project/deep-keyphrase
.. image:: https://img.shields.io/pypi/dm/deep-keyphrase.svg
:target: https://pypi.org/project/pysenal
Description
===========
Implemented Paper
>>>>>>>>>>>>>>>>>>>>>
CopyRNN
`Deep Keyphrase Generation (Meng et al., 2017)`__
.. __: https://arxiv.org/abs/1704.06879
ToDo List
>>>>>>>>>>>>>>>
CopyCNN
CopyTransformer
Usage
============
required files (4 files in total)
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
1. vocab_file: word line by line (don't with index!!!!) ::
this
paper
proposes
2. training, valid and test file
data format for training, valid and test
""""""""""""""""""""""""""""""""""""""""""""""""""
json line format, every line is a dict::
{'tokens': ['this', 'paper', 'proposes', 'using', 'virtual', 'reality', 'to', 'enhance', 'the', 'perception', 'of', 'actions', 'by', 'distant', 'users', 'on', 'a', 'shared', 'application', '.', 'here', ',', 'distance', 'may', 'refer', 'either', 'to', 'space', '(', 'e.g.', 'in', 'a', 'remote', 'synchronous', 'collaboration', ')', 'or', 'time', '(', 'e.g.', 'during', 'playback', 'of', 'recorded', 'actions', ')', '.', 'our', 'approach', 'consists', 'in', 'immersing', 'the', 'application', 'in', 'a', 'virtual', 'inhabited', '3d', 'space', 'and', 'mimicking', 'user', 'actions', 'by', 'animating', 'avatars', '.', 'we', 'illustrate', 'this', 'approach', 'with', 'two', 'applications', ',', 'the', 'one', 'for', 'remote', 'collaboration', 'on', 'a', 'shared', 'application', 'and', 'the', 'other', 'to', 'playback', 'recorded', 'sequences', 'of', 'user', 'actions', '.', 'we', 'suggest', 'this', 'could', 'be', 'a', 'low', 'cost', 'enhancement', 'for', 'telepresence', '.'] ,
'keyphrases': [['telepresence'], ['animation'], ['avatars'], ['application', 'sharing'], ['collaborative', 'virtual', 'environments']]}
Training
>>>>>>>>>>>>>>>
download the kp20k_
.. _kp20k: https://drive.google.com/uc?id=1ZTQEGZSq06kzlPlOv4yGjbUpoDrNxebR&export=download
::
mkdir data
mkdir data/raw
mkdir data/raw/kp20k_new
# !! please unzip kp20k data put the files into above folder manually
python -m nltk.downloader punkt
bash scripts/prepare_kp20k.sh
bash scripts/train_copyrnn_kp20k.sh
# start tensorboard
# enter the experiment result dir, suffix is time that experiment starts
cd data/kp20k/copyrnn_kp20k_basic-20191212-080000
# start tensorboard services
tenosrboard --bind_all --logdir logs --port 6006
Notes
=============================
1. compared with the original :code:`seq2seq-keyphrase-pytorch`
1. fix the implementation error:
1. copy mechanism
2. train and inference are not correspond (training doesn\'t have input feeding and inference has input feeding)
2. easy data preparing
3. tensorboard support
4. **faster beam search (6x faster used cpu and more than 10x faster used gpu)**
