Skip to main content

Pashto Natural Language Processing Toolkit

Project description

NLPashto – NLP Toolkit for Pashto

GitHub GitHub contributors code size

NLPashto is a Python suite for Pashto Natural Language Processing, initiated at Shanghai Jiao Tong University.

Prerequisites

To use NLPashto you will need:

  • Python 3.8+

Installing NLPashto

NLPashto can be installed from PyPi using this command

pip install nlpashto

Downloading Models

Call the download() function and pass the "model name" as argument.

nlpashto.download('space_correct')

Valid model names: 'space_correct', 'pos_tag', 'word_segment', 'pold', 'snd'

If the model name was not specified, all the available models will be downloaded

Basic Usage

Space Correction

Space correction module can be used to correct the space-omission and space-insertion errors. It will remove extra spaces from the text and will insert space where necessary. It’s a beta version and only recommended if the input text is extremely noisy.

from nlpashto import space_correct

noisy_text = 'ه  م  د  ا  ر  ن  ګ ه ت ی ر ه ش پ ه ا وورځپههیوادکېدکروناویروسلهامله۵تنهمړهشوي'
corrected = space_correct(noisy_text)
print(corrected)
Output:: همدارنګه تیره شپه او ورځ په هیواد کې د کرونا ویروس له امله ۵ تنه مړه شوي

Word Segmentatoin

from nlpashto import word_segment

text = 'همدارنګه تیره شپه او ورځ په هیواد کې د کرونا ویروس له امله ۵ تنه مړه شوي'
segmented_text = word_segment(text)
print(segmented_text)

Output:: ['همدارنګه', 'تیره', 'شپه', 'او', 'ورځ', 'په', 'هیواد', 'کې', 'د', 'کرونا ویروس', 'له امله', '۵', 'تنه', 'مړه', 'شوي']

Part-of-speech (POS) Tagging

For further detail about the POS tagger and the corpus used for training please have a look at our paper The Pashto Corpus and Machine Learning Model for Automatic POS Tagging

from nlpashto import pos_tag

text = 'همدارنګه تیره شپه او ورځ په هیواد کې د کرونا ویروس له امله ۵ تنه مړه شوي'
segmented_text = word_segment(text)
tagged = pos_tag(segmented_text)
print(tagged) 

Output:: [['همدارنګه', 'RB'], ['تیره', 'JJ'], ['شپه', 'NNF'], ['او', 'CC'], ['ورځ', 'NNM'], ['په', 'IN'], ['هیواد', 'NNM'], ['کې', 'PT'], ['د', 'IN'], ['کرونا ویروس', 'NNP'], ['له امله', 'RB'], ['۵', 'NB'], ['تنه', 'NNS'], ['مړه', 'JJ'], ['شوي', 'VBDX']]

Offensive Language Detection

A fine-tuned BERT model for toxicity detection in Pashto text

from nlpashto import pold

offensive_text = 'مړه یو کس وی صرف ځان شرموی او یو ستا غوندے جاهل وی چې قوم او ملت شرموی'
pold(text)

Output:: 1


normal_text = 'تاسو رښتیا وایئ خور 🙏'
pold(text)

Output:: 0

Spammy Names Detection

A Naive Bayes classifier model that will predict whether the string of characters is a valid name or not. It can be used to identify spammy profile names on social media.

from nlpashto import snd

not_a_name = 'مسافر لالی'
snd(not_a_name)

Output:: 0.2


valid_name = 'شاهد افريدی'
snd(text)

Output:: 1.0

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

nlpashto-0.0.12.tar.gz (5.5 kB view details)

Uploaded Source

Built Distribution

nlpashto-0.0.12-py3-none-any.whl (6.5 kB view details)

Uploaded Python 3

File details

Details for the file nlpashto-0.0.12.tar.gz.

File metadata

  • Download URL: nlpashto-0.0.12.tar.gz
  • Upload date:
  • Size: 5.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for nlpashto-0.0.12.tar.gz
Algorithm Hash digest
SHA256 f45cb2df93a8e566bd115ac1f7228fd4857bc4c15557e75f17b2c7e3dafe9465
MD5 cf6d4bcd580b2b74be07809f9d630f65
BLAKE2b-256 acd3b2b53bcd1222c16c6b74f7d54301b203cdc97b7b364d2c96978f6ac4200f

See more details on using hashes here.

Provenance

File details

Details for the file nlpashto-0.0.12-py3-none-any.whl.

File metadata

  • Download URL: nlpashto-0.0.12-py3-none-any.whl
  • Upload date:
  • Size: 6.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for nlpashto-0.0.12-py3-none-any.whl
Algorithm Hash digest
SHA256 47fbf3432c489e469918843c4023092867013d018377e666f330cacf545d08e0
MD5 494214a50f535b258f983a8fd4f9444c
BLAKE2b-256 f9080ebf2c4385d904d6d159c82c16caa3ee88bf8a7c9f4827ddaafc806e88a1

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page