This repository has a implementations of data augmentation for NLP for Japanese.

Related tags

Text Data & NLPdaaja
Overview

daaja

This repository has a implementations of data augmentation for NLP for Japanese:

Install

pip install daaja

How to use

EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Command

python -m aug_ja.eda.run --input input.tsv --output data_augmentor.tsv

The format of input.tsv is as follows:

1	この映画はとてもおもしろい
0	つまらない映画だった

In Python

from aug_ja.eda import EasyDataAugmentor
augmentor = EasyDataAugmentor(alpha_sr=0.1, alpha_ri=0.1, alpha_rs=0.1, p_rd=0.1, num_aug=4)
text = "日本語でデータ拡張を行う"
aug_texts = augmentor.augments(text)
print(aug_texts)
# ['日本語でを拡張データ行う', '日本語でデータ押広げるを行う', '日本語でデータ拡張を行う', '日本語で智見拡張を行う', '日本語でデータ拡張を行う']

An Analysis of Simple Data Augmentation for Named Entity Recognition

Command

python -m aug_ja.ner_sda.run --input input.tsv --output data_augmentor.tsv

The format of input.tsv is as follows:

私	O
は	O
田中	B-PER
と	O
いい	O
ます	O

In Python

from daaja.ner_sda import SimpleDataAugmentationforNER
tokens_list = [
    ["私", "は", "田中", "と", "いい", "ます"],
    ["筑波", "大学", "に", "所属", "して", "ます"],
    ["今日", "から", "筑波", "大学", "に", "通う"],
    ["茨城", "大学"],
]
labels_list = [
    ["O", "O", "B-PER", "O", "O", "O"],
    ["B-ORG", "I-ORG", "O", "O", "O", "O"],
    ["B-DATE", "O", "B-ORG", "I-ORG", "O", "O"],
    ["B-ORG", "I-ORG"],
]
augmentor = SimpleDataAugmentationforNER(tokens_list=tokens_list, labels_list=labels_list,
                                            p_power=1, p_lwtr=1, p_mr=1, p_sis=1, p_sr=1, num_aug=4)
tokens = ["吉田", "さん", "は", "株式", "会社", "A", "に", "出張", "予定", "だ"]
labels = ["B-PER", "O", "O", "B-ORG", "I-ORG", "I-ORG", "O", "O", "O", "O"]
augmented_tokens_list, augmented_labels_list = augmentor.augments(tokens, labels)
print(augmented_tokens_list)
# [['吉田', 'さん', 'は', '株式', '会社', 'A', 'に', '出張', '志す', 'だ'],
#  ['吉田', 'さん', 'は', '株式', '大学', '大学', 'に', '出張', '予定', 'だ'],
#  ['吉田', 'さん', 'は', '株式', '会社', 'A', 'に', '出張', '予定', 'だ'],
#  ['吉田', 'さん', 'は', '筑波', '大学', 'に', '出張', '予定', 'だ'],
#  ['吉田', 'さん', 'は', '株式', '会社', 'A', 'に', '出張', '予定', 'だ']]
print(augmented_labels_list)
# [['B-PER', 'O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O', 'O', 'O', 'O'],
#  ['B-PER', 'O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O', 'O', 'O', 'O'],
#  ['B-PER', 'O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O', 'O', 'O', 'O'],
#  ['B-PER', 'O', 'O', 'B-ORG', 'I-ORG', 'O', 'O', 'O', 'O'],
#  ['B-PER', 'O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O', 'O', 'O', 'O']]

Reference

Comments
  • too many progress bars

    too many progress bars

    When I use EasyDataAugmentor in the train process, there are too many progress bars in the console.

    So, can you make this line 19 tqdm selectable on-off when we define EasyDataAugmentor? https://github.com/kajyuuen/daaja/blob/12835943868d43f5c248cf1ea87ab60f67a6e03d/daaja/flows/sequential_flow.py#L19

    opened by Yongtae723 6
  • from daaja.methods.eda.easy_data_augmentor import EasyDataAugmentorにてエラー

    from daaja.methods.eda.easy_data_augmentor import EasyDataAugmentorにてエラー

    daajaをpipインストール後、from daaja.methods.eda.easy_data_augmentor import EasyDataAugmentorを行うと、 以下のエラーとなる。 ConnectionError: HTTPConnectionPool(host='compling.hss.ntu.edu.sg', port=80): Max retries exceeded with url: /wnja/data/1.1/wnjpn.db.gz (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7f3b6a6cced0>: Failed to establish a new connection: [Errno 110] Connection timed out'))

    opened by naoki1213mj 5
  • is it possible to use on GPU device?

    is it possible to use on GPU device?

    Hi!

    thank you for the great library. when I train with this augmentation, this takes so much more time than forward and backward process.

    therefore, can we possibly use this augmentation on GPU to save time?

    thank you

    opened by Yongtae723 3
  • Bump joblib from 1.1.0 to 1.2.0

    Bump joblib from 1.1.0 to 1.2.0

    Bumps joblib from 1.1.0 to 1.2.0.

    Changelog

    Sourced from joblib's changelog.

    Release 1.2.0

    • Fix a security issue where eval(pre_dispatch) could potentially run arbitrary code. Now only basic numerics are supported. joblib/joblib#1327

    • Make sure that joblib works even when multiprocessing is not available, for instance with Pyodide joblib/joblib#1256

    • Avoid unnecessary warnings when workers and main process delete the temporary memmap folder contents concurrently. joblib/joblib#1263

    • Fix memory alignment bug for pickles containing numpy arrays. This is especially important when loading the pickle with mmap_mode != None as the resulting numpy.memmap object would not be able to correct the misalignment without performing a memory copy. This bug would cause invalid computation and segmentation faults with native code that would directly access the underlying data buffer of a numpy array, for instance C/C++/Cython code compiled with older GCC versions or some old OpenBLAS written in platform specific assembly. joblib/joblib#1254

    • Vendor cloudpickle 2.2.0 which adds support for PyPy 3.8+.

    • Vendor loky 3.3.0 which fixes several bugs including:

      • robustly forcibly terminating worker processes in case of a crash (joblib/joblib#1269);

      • avoiding leaking worker processes in case of nested loky parallel calls;

      • reliability spawn the correct number of reusable workers.

    Release 1.1.1

    • Fix a security issue where eval(pre_dispatch) could potentially run arbitrary code. Now only basic numerics are supported. joblib/joblib#1327
    Commits
    • 5991350 Release 1.2.0
    • 3fa2188 MAINT cleanup numpy warnings related to np.matrix in tests (#1340)
    • cea26ff CI test the future loky-3.3.0 branch (#1338)
    • 8aca6f4 MAINT: remove pytest.warns(None) warnings in pytest 7 (#1264)
    • 067ed4f XFAIL test_child_raises_parent_exits_cleanly with multiprocessing (#1339)
    • ac4ebd5 MAINT add back pytest warnings plugin (#1337)
    • a23427d Test child raises parent exits cleanly more reliable on macos (#1335)
    • ac09691 [MAINT] various test updates (#1334)
    • 4a314b1 Vendor loky 3.2.0 (#1333)
    • bdf47e9 Make test_parallel_with_interactively_defined_functions_default_backend timeo...
    • Additional commits viewable in compare view

    Dependabot compatibility score

    Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

    Dependabot commands and options

    You can trigger Dependabot actions by commenting on this PR:

    • @dependabot rebase will rebase this PR
    • @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
    • @dependabot merge will merge this PR after your CI passes on it
    • @dependabot squash and merge will squash and merge this PR after your CI passes on it
    • @dependabot cancel merge will cancel a previously requested merge and block automerging
    • @dependabot reopen will reopen this PR if it is closed
    • @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
    • @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
    • @dependabot use these labels will set the current labels as the default for future PRs for this repo and language
    • @dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language
    • @dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language
    • @dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

    You can disable automated security fix PRs for this repo from the Security Alerts page.

    dependencies 
    opened by dependabot[bot] 0
  • Implement Data Augmentation using Pre-trained Transformer Models

    Implement Data Augmentation using Pre-trained Transformer Models

    opened by kajyuuen 0
  • Implement Contextual Augmentation

    Implement Contextual Augmentation

    opened by kajyuuen 0
  • Implement MixText

    Implement MixText

    opened by kajyuuen 0
Releases(v0.0.7)
Owner
Koga Kobayashi
Koga Kobayashi
GitHub Repository
Nested Named Entity Recognition for Chinese Biomedical Text

CBio-NAMER CBioNAMER (Nested nAMed Entity Recognition for Chinese Biomedical Text) is our method used in CBLUE (Chinese Biomedical Language Understand

8 Dec 25, 2022
Lumped-element impedance calculator and frequency-domain plotter.

fastZ: Lumped-Element Impedance Calculator fastZ is a small tool for calculating and visualizing electrical impedance in Python. Features include: Sup

Wesley Hileman 47 Nov 18, 2022
The model is designed to train a single and large neural network in order to predict correct translation by reading the given sentence.

Neural Machine Translation communication system The model is basically direct to convert one source language to another targeted language using encode

Nishant Banjade 7 Sep 22, 2022
Sentence boundary disambiguation tool for Japanese texts (日本語文境界判定器)

Bunkai Bunkai is a sentence boundary (SB) disambiguation tool for Japanese texts. Quick Start $ pip install bunkai $ echo -e '宿を予約しました♪!まだ2ヶ月も先だけど。早すぎ

Megagon Labs 160 Dec 23, 2022
This is the code for the EMNLP 2021 paper AEDA: An Easier Data Augmentation Technique for Text Classification

The baseline code is for EDA: Easy Data Augmentation techniques for boosting performance on text classification tasks

Akbar Karimi 81 Dec 09, 2022
This repository contains helper functions which can help you generate additional data points depending on your NLP task.

NLP Albumentations For Data Augmentation This repository contains helper functions which can help you generate additional data points depending on you

Aflah 6 May 22, 2022
A programming language with logic of Python, and syntax of all languages.

Pytov The idea was to take all well known syntaxes, and combine them into one programming language with many posabilities. Installation Install using

Yuval Rosen 14 Dec 07, 2022
CorNet Correlation Networks for Extreme Multi-label Text Classification

CorNet Correlation Networks for Extreme Multi-label Text Classification Prerequisites python==3.6.3 pytorch==1.2.0 torchgpipe==0.0.5 click==7.0 ruamel

Guangxu Xun 38 Dec 31, 2022
Text-Summarization-using-NLP - Text Summarization using NLP to fetch BBC News Article and summarize its text and also it includes custom article Summarization

Text-Summarization-using-NLP Text Summarization using NLP to fetch BBC News Arti

Kumar Saksham 21 Aug 06, 2022
A simple version of DeTR

DeTR-Lite A simple version of DeTR Before you enjoy this DeTR-Lite The purpose of this project is to allow you to learn the basic knowledge of DeTR. P

Jianhua Yang 11 Jun 13, 2022
MRC approach for Aspect-based Sentiment Analysis (ABSA)

B-MRC MRC approach for Aspect-based Sentiment Analysis (ABSA) Paper: Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet Extracti

Phuc Phan 1 Apr 05, 2022
To be a next-generation DL-based phenotype prediction from genome mutations.

Sequence -----------+-- 3D_structure -- 3D_module --+ +-- ? | |

Eric Alcaide 18 Jan 11, 2022
Simple program that translates the name of files into English

Simple program that translates the name of files into English. Useful for when editing/inspecting programs that were developed in a foreign language.

0 Dec 22, 2021
A natural language processing model for sequential sentence classification in medical abstracts.

NLP PubMed Medical Research Paper Abstract (Randomized Controlled Trial) A natural language processing model for sequential sentence classification in

Hemanth Chandran 1 Jan 17, 2022
结巴中文分词

jieba “结巴”中文分词：做最好的 Python 中文分词组件 "Jieba" (Chinese for "to stutter") Chinese text segmentation: built to be the best Python Chinese word segmentation

Sun Junyi 29.8k Jan 02, 2023
Python library for interactive topic model visualization. Port of the R LDAvis package.

pyLDAvis Python library for interactive topic model visualization. This is a port of the fabulous R package by Carson Sievert and Kenny Shirley. pyLDA

Ben Mabey 1.7k Dec 20, 2022
Python library to make development of portfolio analysis faster and easier

Trafalgar Python library to make development of portfolio analysis faster and easier Installation 🔥 For the moment, Trafalgar is still in beta develo

Santosh Passoubady 641 Jan 01, 2023
Artificial Conversational Entity for queries in Eulogio "Amang" Rodriguez Institute of Science and Technology (EARIST)

🤖 Coeus - EARIST A.C.E 💬 Coeus is an Artificial Conversational Entity for queries in Eulogio "Amang" Rodriguez Institute of Science and Technology,

Dids Irwyn Reyes 3 Oct 14, 2022
In this project, we compared Spanish BERT and Multilingual BERT in the Sentiment Analysis task.

Applying BERT Fine Tuning to Sentiment Classification on Amazon Reviews Abstract Sentiment analysis has made great progress in recent years, due to th

Alexander Leonardo Lique Lamas 5 Jan 03, 2022
GVT is a generic translation tool for parts of text on the PC screen with Text to Speak functionality.

GVT is a generic translation tool for parts of text on the PC screen with Text to Speech functionality. I wanted to create it because the existing tools that I experimented with did not satisfy me in

Nuked 1 Aug 21, 2022