mirror of https://github.com/explosion/spaCy.git
Tamil language support (#3154)
Tamil language support to spaCy Description Hereby, creating new PR to add support for Tamil language in spaCy added stop words, examples and numerical attributes <--Working on other language data--> Types of change Enhancement Checklist [ x] I have submitted the spaCy Contributor Agreement. [x ] I ran the tests, and all new and existing tests passed. [ x] My changes don't require a change to the documentation, or if they do, I've added all required information.
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# spaCy contributor agreement
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This spaCy Contributor Agreement (**"SCA"**) is based on the
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[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
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The SCA applies to any contribution that you make to any product or project
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managed by us (the **"project"**), and sets out the intellectual property rights
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you grant to us in the contributed materials. The term **"us"** shall mean
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[ExplosionAI UG (haftungsbeschränkt)](https://explosion.ai/legal). The term
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**"you"** shall mean the person or entity identified below.
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should be your GitHub username, with the extension `.md`. For example, the user
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Read this agreement carefully before signing. These terms and conditions
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## Contributor Agreement
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1. The term "contribution" or "contributed materials" means any source code,
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## Contributor Details
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| Field | Entry |
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|------------------------------- | -------------------- |
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| Name | Loghi Perinpanayagam |
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| Company name (if applicable) | |
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| Title or role (if applicable) | Student |
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| Date | 13 Jan, 2019 |
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| GitHub username | loghijiaha |
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| Website (optional) | |
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# import language-specific data
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from .stop_words import STOP_WORDS
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from .lex_attrs import LEX_ATTRS
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from ..tokenizer_exceptions import BASE_EXCEPTIONS
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from ...language import Language
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from ...attrs import LANG
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from ...util import update_exc
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# create Defaults class in the module scope (necessary for pickling!)
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class TamilDefaults(Language.Defaults):
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lex_attr_getters = dict(Language.Defaults.lex_attr_getters)
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lex_attr_getters[LANG] = lambda text: 'ta' # language ISO code
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# optional: replace flags with custom functions, e.g. like_num()
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lex_attr_getters.update(LEX_ATTRS)
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# create actual Language class
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class Tamil(Language):
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lang = 'ta' # language ISO code
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Defaults = TamilDefaults # override defaults
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# set default export – this allows the language class to be lazy-loaded
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__all__ = ['Tamil']
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# coding: utf8
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from __future__ import unicode_literals
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"""
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Example sentences to test spaCy and its language models.
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>>> from spacy.lang.ta.examples import sentences
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>>> docs = nlp.pipe(sentences)
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"""
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sentences = [
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"கிறிஸ்துமஸ் மற்றும் இனிய புத்தாண்டு வாழ்த்துக்கள்",
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"எனக்கு என் குழந்தைப் பருவம் நினைவிருக்கிறது",
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"உங்கள் பெயர் என்ன?",
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"ஏறத்தாழ இலங்கைத் தமிழரில் மூன்றிலொரு பங்கினர் இலங்கையை விட்டு வெளியேறிப் பிற நாடுகளில் வாழ்கின்றனர்",
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"இந்த ஃபோனுடன் சுமார் ரூ.2,990 மதிப்புள்ள போட் ராக்கர்ஸ் நிறுவனத்தின் ஸ்போர்ட் புளூடூத் ஹெட்போன்ஸ் இலவசமாக வழங்கப்படவுள்ளது.",
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"மட்டக்களப்பில் பல இடங்களில் வீட்டுத் திட்டங்களுக்கு இன்று அடிக்கல் நாட்டல்",
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"ஐ போன்க்கு முகத்தை வைத்து அன்லாக் செய்யும் முறை மற்றும் விரலால் தொட்டு அன்லாக் செய்யும் முறையை வாட்ஸ் ஆப் நிறுவனம் இதற்கு முன் கண்டுபிடித்தது"
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]
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# coding: utf8
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from __future__ import unicode_literals
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from ...attrs import LIKE_NUM
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_numeral_suffixes = {'பத்து': 'பது', 'ற்று': 'று', 'ரத்து':'ரம்' , 'சத்து': 'சம்'}
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_num_words = ['பூச்சியம்', 'ஒரு', 'ஒன்று', 'இரண்டு', 'மூன்று', 'நான்கு', 'ஐந்து', 'ஆறு', 'ஏழு',
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'எட்டு', 'ஒன்பது', 'பத்து', 'பதினொன்று', 'பன்னிரண்டு', 'பதின்மூன்று', 'பதினான்கு',
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'பதினைந்து', 'பதினாறு', 'பதினேழு', 'பதினெட்டு', 'பத்தொன்பது', 'இருபது',
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'முப்பது', 'நாற்பது', 'ஐம்பது', 'அறுபது', 'எழுபது', 'எண்பது', 'தொண்ணூறு',
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'நூறு', 'இருநூறு', 'முன்னூறு', 'நாநூறு', 'ஐநூறு', 'அறுநூறு', 'எழுநூறு', 'எண்ணூறு', 'தொள்ளாயிரம்',
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'ஆயிரம்', 'ஒராயிரம்', 'லட்சம்', 'மில்லியன்', 'கோடி', 'பில்லியன்', 'டிரில்லியன்']
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# 20-89 ,90-899,900-99999 and above have different suffixes
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def suffix_filter(text):
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# text without numeral suffixes
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for num_suffix in _numeral_suffixes.keys():
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length = len(num_suffix)
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if (len(text) < length):
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break
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elif text.endswith(num_suffix):
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return text[:-length] + _numeral_suffixes[num_suffix]
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return text
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def like_num(text):
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text = text.replace(',', '').replace('.', '')
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if text.isdigit():
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return True
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if text.count('/') == 1:
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num, denom = text.split('/')
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if num.isdigit() and denom.isdigit():
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return True
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print(suffix_filter(text))
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if text.lower() in _num_words:
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return True
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elif suffix_filter(text) in _num_words:
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return True
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return False
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LEX_ATTRS = {
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LIKE_NUM: like_num
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}
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# coding: utf8
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from __future__ import unicode_literals
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# Stop words
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STOP_WORDS = set("""
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ஒரு
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என்று
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மற்றும்
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இந்த
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இது
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என்ற
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கொண்டு
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என்பது
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பல
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ஆகும்
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அல்லது
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அவர்
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நான்
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உள்ள
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அந்த
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இவர்
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என
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முதல்
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என்ன
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இருந்து
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சில
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என்
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போன்ற
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வேண்டும்
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வந்து
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இதன்
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அது
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அவன்
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தான்
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பலரும்
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என்னும்
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மேலும்
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பின்னர்
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கொண்ட
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இருக்கும்
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தனது
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உள்ளது
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போது
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என்றும்
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அதன்
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தன்
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பிறகு
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அவர்கள்
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வரை
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அவள்
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நீ
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ஆகிய
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இருந்தது
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உள்ளன
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வந்த
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இருந்த
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மிகவும்
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இங்கு
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மீது
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ஓர்
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இவை
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இந்தக்
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பற்றி
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வரும்
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வேறு
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இரு
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இதில்
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போல்
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இப்போது
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அவரது
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மட்டும்
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இந்தப்
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எனும்
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மேல்
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பின்
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சேர்ந்த
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ஆகியோர்
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எனக்கு
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இன்னும்
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அந்தப்
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அன்று
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ஒரே
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மிக
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அங்கு
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பல்வேறு
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விட்டு
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பெரும்
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அதை
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பற்றிய
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உன்
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அதிக
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அந்தக்
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பேர்
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இதனால்
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அவை
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அதே
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ஏன்
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முறை
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யார்
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என்பதை
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எல்லாம்
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மட்டுமே
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இங்கே
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அங்கே
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இடம்
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இடத்தில்
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அதில்
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நாம்
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அதற்கு
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எனவே
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பிற
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சிறு
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மற்ற
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விட
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எந்த
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எனவும்
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எனப்படும்
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எனினும்
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அடுத்த
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இதனை
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இதை
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கொள்ள
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இந்தத்
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இதற்கு
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அதனால்
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தவிர
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போல
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வரையில்
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சற்று
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எனக்
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""".split())
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