PORORO: Platform Of neuRal mOdels for natuRal language prOcessing

Overview

PORORO: Platform Of neuRal mOdels for natuRal language prOcessing

GitHub release Apache 2.0 Docs Issues


pororo performs Natural Language Processing and Speech-related tasks.

It is easy to solve various subtasks in the natural language and speech processing field by simply passing the task name.


Installation

  • pororo is based on torch=1.6(cuda 10.1) and python>=3.6

  • You can install a package through the command below:

pip install pororo
  • Or you can install it locally:
git clone https://github.com/kakaobrain/pororo.git
cd pororo
pip install -e .
  • For library installation for specific tasks other than the common modules, please refer to INSTALL.md

  • For the utilization of Automatic Speech Recognition, wav2letter should be installed separately. For the installation, please run the asr-install.sh file

bash asr-install.sh

Usage

  • pororo can be used as follows:
  • First, in order to import pororo, you must execute the following snippet
>>> from pororo import Pororo
  • After the import, you can check the tasks currently supported by the pororo through the following commands
>>> from pororo import Pororo
>>> Pororo.available_tasks()
"Available tasks are ['mrc', 'rc', 'qa', 'question_answering', 'machine_reading_comprehension', 'reading_comprehension', 'sentiment', 'sentiment_analysis', 'nli', 'natural_language_inference', 'inference', 'fill', 'fill_in_blank', 'fib', 'para', 'pi', 'cse', 'contextual_subword_embedding', 'similarity', 'sts', 'semantic_textual_similarity', 'sentence_similarity', 'sentvec', 'sentence_embedding', 'sentence_vector', 'se', 'inflection', 'morphological_inflection', 'g2p', 'grapheme_to_phoneme', 'grapheme_to_phoneme_conversion', 'w2v', 'wordvec', 'word2vec', 'word_vector', 'word_embedding', 'tokenize', 'tokenise', 'tokenization', 'tokenisation', 'tok', 'segmentation', 'seg', 'mt', 'machine_translation', 'translation', 'pos', 'tag', 'pos_tagging', 'tagging', 'const', 'constituency', 'constituency_parsing', 'cp', 'pg', 'collocation', 'collocate', 'col', 'word_translation', 'wt', 'summarization', 'summarisation', 'text_summarization', 'text_summarisation', 'summary', 'gec', 'review', 'review_scoring', 'lemmatization', 'lemmatisation', 'lemma', 'ner', 'named_entity_recognition', 'entity_recognition', 'zero-topic', 'dp', 'dep_parse', 'caption', 'captioning', 'asr', 'speech_recognition', 'st', 'speech_translation', 'ocr', 'srl', 'semantic_role_labeling', 'p2g', 'aes', 'essay', 'qg', 'question_generation', 'age_suitability']"
  • To check which models are supported by each task, you can go through the following process
>>> from pororo import Pororo
>>> Pororo.available_models("collocation")
'Available models for collocation are ([lang]: ko, [model]: kollocate), ([lang]: en, [model]: collocate.en), ([lang]: ja, [model]: collocate.ja), ([lang]: zh, [model]: collocate.zh)'
  • If you want to perform a specific task, you can put the task name in the task argument and the language name in the lang argument
>>> from pororo import Pororo
>>> ner = Pororo(task="ner", lang="en")
  • After object construction, it can be used in a way that passes the input value as follows:
>>> ner("Michael Jeffrey Jordan (born February 17, 1963) is an American businessman and former professional basketball player.")
[('Michael Jeffrey Jordan', 'PERSON'), ('(', 'O'), ('born', 'O'), ('February 17, 1963)', 'DATE'), ('is', 'O'), ('an', 'O'), ('American', 'NORP'), ('businessman', 'O'), ('and', 'O'), ('former', 'O'), ('professional', 'O'), ('basketball', 'O'), ('player', 'O'), ('.', 'O')]
  • If task supports multiple languages, you can change the lang argument to take advantage of models trained in different languages.
>>> ner = Pororo(task="ner", lang="ko")
>>> ner("마이클 제프리 조던(영어: Michael Jeffrey Jordan, 1963년 2월 17일 ~ )은 미국의 은퇴한 농구 선수이다.")
[('마이클 제프리 조던', 'PERSON'), ('(', 'O'), ('영어', 'CIVILIZATION'), (':', 'O'), (' ', 'O'), ('Michael Jeffrey Jordan', 'PERSON'), (',', 'O'), (' ', 'O'), ('1963년 2월 17일 ~', 'DATE'), (' ', 'O'), (')은', 'O'), (' ', 'O'), ('미국', 'LOCATION'), ('의', 'O'), (' ', 'O'), ('은퇴한', 'O'), (' ', 'O'), ('농구 선수', 'CIVILIZATION'), ('이다.', 'O')]
>>> ner = Pororo(task="ner", lang="ja")
>>> ner("マイケル・ジェフリー・ジョーダンは、アメリカ合衆国の元バスケットボール選手")
[('マイケル・ジェフリー・ジョーダン', 'PERSON'), ('は', 'O'), ('、アメリカ合衆国', 'O'), ('の', 'O'), ('元', 'O'), ('バスケットボール', 'O'), ('選手', 'O')]
>>> ner = Pororo(task="ner", lang="zh")
>>> ner("麥可·傑佛瑞·喬丹是美國退役NBA職業籃球運動員,也是一名商人,現任夏洛特黃蜂董事長及主要股東")
[('麥可·傑佛瑞·喬丹', 'PERSON'), ('是', 'O'), ('美國', 'GPE'), ('退', 'O'), ('役', 'O'), ('nba', 'ORG'), ('職', 'O'), ('業', 'O'), ('籃', 'O'), ('球', 'O'), ('運', 'O'), ('動', 'O'), ('員', 'O'), (',', 'O'), ('也', 'O'), ('是', 'O'), ('一', 'O'), ('名', 'O'), ('商', 'O'), ('人', 'O'), (',', 'O'), ('現', 'O'), ('任', 'O'), ('夏洛特黃蜂', 'ORG'), ('董', 'O'), ('事', 'O'), ('長', 'O'), ('及', 'O'), ('主', 'O'), ('要', 'O'), ('股', 'O'), ('東', 'O')]
  • If the task supports multiple models, you can change the model argument to use another model.
>>> from pororo import Pororo
>>> mt = Pororo(task="mt", lang="multi", model="transformer.large.multi.mtpg")
>>> fast_mt = Pororo(task="mt", lang="multi", model="transformer.large.multi.fast.mtpg")

Documentation

For more detailed information, see full documentation

If you have any questions or requests, please report the issue.


Citation

If you apply this library to any project and research, please cite our code:

@misc{pororo,
  author       = {Heo, Hoon and Ko, Hyunwoong and Kim, Soohwan and
                  Han, Gunsoo and Park, Jiwoo and Park, Kyubyong},
  title        = {PORORO: Platform Of neuRal mOdels for natuRal language prOcessing},
  howpublished = {\url{https://github.com/kakaobrain/pororo}},
  year         = {2021},
}

Contributors

Hoon Heo, Hyunwoong Ko, Soohwan Kim, Gunsoo Han, Jiwoo Park and Kyubyong Park


License

PORORO project is licensed under the terms of the Apache License 2.0.

Copyright 2021 Kakao Brain Corp. https://www.kakaobrain.com All Rights Reserved.

Comments
  • Fix typo on para_gen docstrings and html

    Fix typo on para_gen docstrings and html

    Title

    • fix typo on para_gen docstrings and html

    Description

    • Englosh to English

    Linked Issues

    • resolved #43

    MRC랑 한번에 PR 했어야 했는데.. 여러모로 번거롭게 해드려서 죄송합니다...

    opened by SDSTony 1
  • Fix typo on machine_reading_comprehension.py and mrc.html

    Fix typo on machine_reading_comprehension.py and mrc.html

    Title

    • Fix typo on machine_reading_comprehension.py and mrc.html

    Description

    • Fix typo comprehesion to comprehension found on
    • machine_reading_comprehension.py docstring
    • mrc.html

    Linked Issues

    • resolved #41
    opened by SDSTony 1
  • Fix typo on age_suitability.html

    Fix typo on age_suitability.html

    fix typo from nudiy to nudity

    Title

    • fix typo on age_suitability.html

    Description

    • There is a typo on age_suitability.html page. I think the word Nudiy should be fixed into Nudity. I've edited the html file directly in this PR. If this isn't a proper way to edit a published web document, please cancel this PR. Thank you.

    Linked Issues

    • #39
    opened by SDSTony 1
  • Improve MRC inference and change output

    Improve MRC inference and change output

    Title

    • Improve MRC inference and change output

    Summary

    • Predict span using top10 start&end position
    • Add score output
    • Add logit output

    Description

    In predicting span in the MRC, the existing code used only the maximum value of start position and end position. For a more accurate inference, the top 10 start positions and end positions were used to predict the highest score span. At this time, the score is defined as the sum of start logit and end logit. Finally, I added logit and score to the output for user convenience.

    Examples

    >>> mrc = Pororo(task="mrc", lang="ko")
    >>> mrc(
    >>>    "카카오브레인이 공개한 것은?",
    >>>    "카카오 인공지능(AI) 연구개발 자회사 카카오브레인이 AI 솔루션을 첫 상품화했다. 카카오는 카카오브레인 '포즈(pose·자세분석) API'를 유료 공개한다고 24일 밝혔다. 카카오브레인이 AI 기술을 유료 API를 공개하는 것은 처음이다. 공개하자마자 외부 문의가 쇄도한다. 포즈는 AI 비전(VISION, 영상·화면분석) 분야 중 하나다. 카카오브레인 포즈 API는 이미지나 영상을 분석해 사람 자세를 추출하는 기능을 제공한다."
    >>> )
    ('포즈(pose·자세분석) API',
     (33, 44),
     (5.7833147048950195, 4.649877548217773),
     10.433192253112793)
    >>> # when mecab doesn't work well for postprocess, you can set `postprocess` option as `False`
    >>> mrc("카카오브레인이 공개한 라이브러리 이름은?", "카카오브레인은 자연어 처리와 음성 관련 태스크를 쉽게 수행할 수 있도록 도와 주는 라이브러리 pororo를 공개하였습니다.", postprocess=False)
    ('pororo', (31, 35), (8.656489372253418, 8.14583683013916), 16.802326202392578)
    
    opened by skaurl 0
  • Fixed Code Quality Issues

    Fixed Code Quality Issues

    Title

    • Fixed Code Quality Issues

    Description

    Summary:

    • Remove unnecessary generator
    • Remove methods with an unnecessary super delegation
    • Remove redundant None
    • Add .deepsource.toml

    I ran a DeepSource Analysis on my fork of this repository. You can see all the issues raised by DeepSource here.

    DeepSource helps you to automatically find and fix issues in your code during code reviews. This tool looks for anti-patterns, bug risks, performance problems, and raises issues. There are plenty of other issues in relation to Bug Discovery and Anti-Patterns which you would be interested to take a look at.

    If you do not want to use DeepSource to continuously analyze this repo, I'll remove the .deepsource.toml from this PR and you can merge the rest of the fixes. If you want to setup DeepSource for Continuous Analysis, I can help you set that up.

    opened by HarshCasper 0
  • Update TTS example comment

    Update TTS example comment

    Title

    • Update TTS example comment

    Description

    • Update TTS example comment (Cross-lingual Voice Style Transfer => Code-Switching)

    Linked Issues

    • resolved #00
    opened by sooftware 0
  • Delete unuse files & Add tts example ipynb

    Delete unuse files & Add tts example ipynb

    Title

    • Delete unuse files & Add tts example ipynb

    Description

    • Delete unuse files (examples/.ipynb/, examples/Untitle.ipynb)
    • Add examples/speech_synthesis.ipynb

    Linked Issues

    • resolved #00
    opened by sooftware 0
  • Update TTS

    Update TTS

    Title

    • Denote TTS INSTALL.md & 3rd_party_model & Add tts-install.sh

    Description

    • Denote TTS install requirements
    • Denote 3rd_party_model (TTS)
    • Add tts-install.sh
    • Test complete
    • docstring example update

    Linked Issues

    • resolved #00
    opened by sooftware 0
  • Mount TTS

    Mount TTS

    Title

    • Mount TTS

    Description

    • Mount TTS (Text-To-Speech) Task
    • Update LICENSE.3rd_party_library
    • Add test file (tts)
    • demo page (Not yet completed)

    Linked Issues

    • resolved #00
    opened by sooftware 0
  • Feature/6 kwargs

    Feature/6 kwargs

    Title

    • Add kwargs to __call__ and predict

    Description

    • Add kwargs to __call__ and predict to prevent generate unnecessary custom predict function

    Linked Issues

    • resolved #6
    opened by Huffon 0
  • fix: prevent OSError: read-only file system error

    fix: prevent OSError: read-only file system error

    Description

    I found that there is a chance of OSError to occur when we try to load models into a temporary directory such as in the strictly managed environment like some containers on the cloud.

    [2022-03-23 04:07:37,080] {ecs.py:362} INFO - [2022-03-23T04:07:12.901000]     review_scoring_model = Pororo(task="review", lang="ko")
    [2022-03-23 04:07:37,080] {ecs.py:362} INFO - [2022-03-23T04:07:12.901000]   File "/usr/local/lib/python3.8/site-packages/pororo/pororo.py", line 203, in __new__
    [2022-03-23 04:07:37,080] {ecs.py:362} INFO - [2022-03-23T04:07:12.901000]     task_module = SUPPORTED_TASKS[task](
    [2022-03-23 04:07:37,080] {ecs.py:362} INFO - [2022-03-23T04:07:12.901000]   File "/usr/local/lib/python3.8/site-packages/pororo/tasks/review_scoring.py", line 86, in load
    [2022-03-23 04:07:37,081] {ecs.py:362} INFO - [2022-03-23T04:07:12.901000]     model = (BrainRobertaModel.load_model(
    [2022-03-23 04:07:37,081] {ecs.py:362} INFO - [2022-03-23T04:07:12.901000]   File "/usr/local/lib/python3.8/site-packages/pororo/models/brainbert/BrainRoBERTa.py", line 33, in load_model
    [2022-03-23 04:07:37,081] {ecs.py:362} INFO - [2022-03-23T04:07:12.901000]     ckpt_dir = download_or_load(model_name, lang)
    [2022-03-23 04:07:37,081] {ecs.py:362} INFO - [2022-03-23T04:07:12.901000]   File "/usr/local/lib/python3.8/site-packages/pororo/tasks/utils/download_utils.py", line 318, in download_or_load
    [2022-03-23 04:07:37,081] {ecs.py:362} INFO - [2022-03-23T04:07:12.901000]     return download_or_load_bert(info)
    [2022-03-23 04:07:37,081] {ecs.py:362} INFO - [2022-03-23T04:07:12.901000]   File "/usr/local/lib/python3.8/site-packages/pororo/tasks/utils/download_utils.py", line 104, in download_or_load_bert
    [2022-03-23 04:07:37,081] {ecs.py:362} INFO - [2022-03-23T04:07:12.901000]     type_dir = download_from_url(
    [2022-03-23 04:07:37,081] {ecs.py:362} INFO - [2022-03-23T04:07:12.901000]   File "/usr/local/lib/python3.8/site-packages/pororo/tasks/utils/download_utils.py", line 288, in download_from_url
    [2022-03-23 04:07:37,081] {ecs.py:362} INFO - [2022-03-23T04:07:12.901000]     wget.download(url, type_dir)
    [2022-03-23 04:07:37,081] {ecs.py:362} INFO - [2022-03-23T04:07:12.901000]   File "/usr/local/lib/python3.8/site-packages/wget.py", line 506, in download
    [2022-03-23 04:07:37,081] {ecs.py:362} INFO - [2022-03-23T04:07:12.901000]     (fd, tmpfile) = tempfile.mkstemp(".tmp", prefix=prefix, dir=".")
    [2022-03-23 04:07:37,081] {ecs.py:362} INFO - [2022-03-23T04:07:12.901000]   File "/usr/local/lib/python3.8/tempfile.py", line 331, in mkstemp
    [2022-03-23 04:07:37,081] {ecs.py:362} INFO - [2022-03-23T04:07:12.901000]     return _mkstemp_inner(dir, prefix, suffix, flags, output_type)
    [2022-03-23 04:07:37,081] {ecs.py:362} INFO - [2022-03-23T04:07:12.901000]   File "/usr/local/lib/python3.8/tempfile.py", line 250, in _mkstemp_inner
    [2022-03-23 04:07:37,081] {ecs.py:362} INFO - [2022-03-23T04:07:12.901000]     fd = _os.open(file, flags, 0o600)
    [2022-03-23 04:07:37,082] {ecs.py:362} INFO - [2022-03-23T04:07:12.901000] OSError: [Errno 30] Read-only file system: './brainbert.base.ko.review_rating.zip4zkvg88b.tmp'
    

    This commit will prevent that to happen. The code for the new function 'download' is originated from wget library written by anatoly techtonik with slight revision done by me.

    opened by daun-io 0
  • Improve MRC inference and change output

    Improve MRC inference and change output

    Title

    • Improve MRC inference and change output

    Summary

    • Predict span using top10 start&end position
    • Add score output
    • Add logit output

    Description

    In predicting span in the MRC, the existing code used only the maximum value of start position and end position. For a more accurate inference, the top 10 start positions and end positions were used to predict the highest score span. At this time, the score is defined as the sum of start logit and end logit. Finally, I added logit and score to the output for user convenience.

    Examples

    >>> mrc = Pororo(task="mrc", lang="ko")
    >>> mrc(
    >>>    "카카오브레인이 공개한 것은?",
    >>>    "카카오 인공지능(AI) 연구개발 자회사 카카오브레인이 AI 솔루션을 첫 상품화했다. 카카오는 카카오브레인 '포즈(pose·자세분석) API'를 유료 공개한다고 24일 밝혔다. 카카오브레인이 AI 기술을 유료 API를 공개하는 것은 처음이다. 공개하자마자 외부 문의가 쇄도한다. 포즈는 AI 비전(VISION, 영상·화면분석) 분야 중 하나다. 카카오브레인 포즈 API는 이미지나 영상을 분석해 사람 자세를 추출하는 기능을 제공한다."
    >>> )
    ('포즈(pose·자세분석) API',
     (33, 44),
     (5.7833147048950195, 4.649877548217773),
     10.433192253112793)
    >>> # when mecab doesn't work well for postprocess, you can set `postprocess` option as `False`
    >>> mrc("카카오브레인이 공개한 라이브러리 이름은?", "카카오브레인은 자연어 처리와 음성 관련 태스크를 쉽게 수행할 수 있도록 도와 주는 라이브러리 pororo를 공개하였습니다.", postprocess=False)
    ('pororo', (31, 35), (8.656489372253418, 8.14583683013916), 16.802326202392578)
    
    opened by skaurl 0
Releases(0.4.0)
  • 0.4.0(Feb 12, 2021)

  • 0.3.2(Feb 3, 2021)

  • 0.3.1(Feb 2, 2021)

    PORORO: Platform Of neuRal mOdels for natuRal language prOcessing

    pororo performs Natural Language Processing and Speech-related tasks.

    It is easy to solve various subtasks in the natural language and speech processing field by simply passing the task name.


    Supported Tasks

    You can see more information here !


    TEXT CLASSIFICATION

    • Automated Essay Scoring
    • Age Suitability Prediction
    • Natural Language Inference
    • Paraphrase Identification
    • Review Scoring
    • Semantic Textual Similarity
    • Sentence Embedding
    • Sentiment Analysis
    • Zero-shot Topic Classification

    SEQUENCE TAGGING

    • Contextualized Embedding
    • Dependency Parsing
    • Fill-in-the-blank
    • Machine Reading Comprehension
    • Named Entity Recognition
    • Part-of-Speech Tagging
    • Semantic Role Labeling

    SEQ2SEQ

    • Constituency Parsing
    • Grammatical Error Correction
    • Grapheme-to-Phoneme
    • Phoneme-to-Grapheme
    • Machine Translation
    • Paraphrase Generation
    • Question Generation
    • Text Summarization

    MISC.

    • Automatic Speech Recognition
    • Image Captioning
    • Collocation
    • Lemmatization
    • Morphological Inflection
    • Optical Character Recognition
    • Tokenization
    • Word Translation
    Source code(tar.gz)
    Source code(zip)
Owner
Kakao Brain
Kakao Brain Corp.
Kakao Brain
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