Data loaders and abstractions for text and NLP
Project description
LanguageFlow
Data loaders and abstractions for text and NLP
Requirements
Install dependencies
$ pip install future, tox
$ pip install python-crfsuite==0.9.5
$ pip install Cython
$ pip install -U fasttext --no-cache-dir --no-deps --force-reinstall
$ pip install xgboost==0.82Installation
$ pip install languageflowComponents
Transformers: NumberRemover, CountVectorizer, TfidfVectorizer
Models: SGDClassifier, XGBoostClassifier, KimCNNClassifier, FastTextClassifier, CRF
Data
Download a dataset using download command
$ languageflow download DATASETList all dataset
$ languageflow listDatasets
The datasets module currently contains:
Tagged: VLSP2018-NER, VTB-CHUNK*, VLSP2016-NER*, VLSP2013-POS*, VLSP2013-WTK*
Categorized: AIVIVN2019_SA*, VLSP2018_SA*, UTS2017_BANK, VLSP2016_SA*, VNTC
Plaintext: VNESES, VNTQ_SMALL, VNTQ_BIG
Caution (*): With closed license dataset, you must provide URL to download
Example
Download UTS2017_BANK dataset
$ languageflow download UTS2017_BANKUse UTS2017_BANK dataset
>>> from languageflow.data_fetcher import DataFetcher, NLPData
>>> corpus = DataFetcher.load_corpus(NLPData.UTS2017_BANK_SA)
>>> print(corpus)
CategorizedCorpus: 1780 train + 197 dev + 494 test sentencesHistory
1.1.7 (2018-04-12)
Automatic deploy with travis and pypi
Fix dependencies hell
1.1.6 (2017-12-26)
Add data module to handle data downloading and data preprocessing
Add many new models: SGDClassifier, XGBoostClassier, FastTextClassifier, CRF
Add new feature: LanguageBoard
Automatic continuous integration with travis-ci
Build docs with readthedocs.org
1.1.5 (2017-12-11)
Refactor project to integrate with underthesea experiment
0.1.0 (2017-09-18)
First release on PyPI.
Release history Release notifications | RSS feed
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