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Hindi: Adds tests for lexical attributes (norm and like_num) (#5829)
* Hindi: Adds tests for lexical attributes (norm and like_num) * Signs and sdds the contributor agreement * Add ordinal numbers to be tagged as like_num * Adds alternate pronunciation for 31 and 39
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# spaCy contributor agreement
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This spaCy Contributor Agreement (**"SCA"**) is based on the
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## Contributor Details
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| Field | Entry |
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|------------------------------- | -------------------- |
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| Name | Rahul Gupta |
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| Company name (if applicable) | |
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| Title or role (if applicable) | |
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| Date | 28 July 2020 |
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| GitHub username | rahul1990gupta |
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| Website (optional) | |
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@ -13,23 +13,26 @@ _stem_suffixes = [
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["ाएगी", "ाएगा", "ाओगी", "ाओगे", "एंगी", "ेंगी", "एंगे", "ेंगे", "ूंगी", "ूंगा", "ातीं", "नाओं", "नाएं", "ताओं", "ताएं", "ियाँ", "ियों", "ियां"],
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["ाएंगी", "ाएंगे", "ाऊंगी", "ाऊंगा", "ाइयाँ", "ाइयों", "ाइयां"]
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]
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# fmt: on
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# reference 1:https://en.wikipedia.org/wiki/Indian_numbering_system
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# reference 1: https://en.wikipedia.org/wiki/Indian_numbering_system
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# reference 2: https://blogs.transparent.com/hindi/hindi-numbers-1-100/
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# reference 3: https://www.mindurhindi.com/basic-words-and-phrases-in-hindi/
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_num_words = [
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_one_to_ten = [
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"शून्य",
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"एक",
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"दो",
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"तीन",
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"चार",
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"पांच",
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"पांच", "पाँच",
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"छह",
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"सात",
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"आठ",
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"नौ",
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"दस",
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]
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_eleven_to_beyond = [
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"ग्यारह",
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"बारह",
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"तेरह",
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@ -40,13 +43,85 @@ _num_words = [
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"अठारह",
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"उन्नीस",
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"बीस",
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"इकीस", "इक्कीस",
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"बाईस",
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"तेइस",
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"चौबीस",
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"पच्चीस",
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"छब्बीस",
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"सताइस", "सत्ताइस",
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"अट्ठाइस",
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"उनतीस",
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"तीस",
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"इकतीस", "इकत्तीस",
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"बतीस", "बत्तीस",
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"तैंतीस",
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"चौंतीस",
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"पैंतीस",
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"छतीस", "छत्तीस",
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"सैंतीस",
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"अड़तीस",
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"उनतालीस", "उनत्तीस",
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"चालीस",
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"इकतालीस",
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"बयालीस",
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"तैतालीस",
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"चवालीस",
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"पैंतालीस",
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"छयालिस",
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"सैंतालीस",
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"अड़तालीस",
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"उनचास",
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"पचास",
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"इक्यावन",
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"बावन",
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"तिरपन", "तिरेपन",
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"चौवन", "चउवन",
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"पचपन",
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"छप्पन",
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"सतावन", "सत्तावन",
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"अठावन",
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"उनसठ",
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"साठ",
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"इकसठ",
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"बासठ",
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"तिरसठ", "तिरेसठ",
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"चौंसठ",
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"पैंसठ",
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"छियासठ",
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"सड़सठ",
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"अड़सठ",
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"उनहत्तर",
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"सत्तर",
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"इकहत्तर"
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"बहत्तर",
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"तिहत्तर",
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"चौहत्तर",
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"पचहत्तर",
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"छिहत्तर",
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"सतहत्तर",
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"अठहत्तर",
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"उन्नासी", "उन्यासी"
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"अस्सी",
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"इक्यासी",
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"बयासी",
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"तिरासी",
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"चौरासी",
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"पचासी",
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"छियासी",
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"सतासी",
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"अट्ठासी",
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"नवासी",
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"नब्बे",
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"इक्यानवे",
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"बानवे",
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"तिरानवे",
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"चौरानवे",
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"पचानवे",
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"छियानवे",
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"सतानवे",
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"अट्ठानवे",
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"निन्यानवे",
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"सौ",
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"हज़ार",
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"लाख",
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@ -55,6 +130,22 @@ _num_words = [
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"खरब",
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]
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_num_words = _one_to_ten + _eleven_to_beyond
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_ordinal_words_one_to_ten = [
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"प्रथम", "पहला",
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"द्वितीय", "दूसरा",
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"तृतीय", "तीसरा",
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"चौथा",
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"पांचवाँ",
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"छठा",
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"सातवाँ",
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"आठवाँ",
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"नौवाँ",
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"दसवाँ",
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]
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_ordinal_suffix = "वाँ"
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# fmt: on
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def norm(string):
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# normalise base exceptions, e.g. punctuation or currency symbols
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@ -67,7 +158,7 @@ def norm(string):
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for suffix_group in reversed(_stem_suffixes):
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length = len(suffix_group[0])
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if len(string) <= length:
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break
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continue
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for suffix in suffix_group:
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if string.endswith(suffix):
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return string[:-length]
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@ -77,7 +168,7 @@ def norm(string):
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def like_num(text):
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if text.startswith(("+", "-", "±", "~")):
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text = text[1:]
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text = text.replace(", ", "").replace(".", "")
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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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@ -86,6 +177,14 @@ def like_num(text):
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return True
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if text.lower() in _num_words:
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return True
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# check ordinal numbers
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# reference: http://www.englishkitab.com/Vocabulary/Numbers.html
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if text in _ordinal_words_one_to_ten:
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return True
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if text.endswith(_ordinal_suffix):
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if text[:-len(_ordinal_suffix)] in _eleven_to_beyond:
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return True
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return False
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@ -123,6 +123,11 @@ def he_tokenizer():
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return get_lang_class("he").Defaults.create_tokenizer()
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@pytest.fixture(scope="session")
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def hi_tokenizer():
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return get_lang_class("hi").Defaults.create_tokenizer()
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@pytest.fixture(scope="session")
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def hr_tokenizer():
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return get_lang_class("hr").Defaults.create_tokenizer()
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0
spacy/tests/lang/hi/__init__.py
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0
spacy/tests/lang/hi/__init__.py
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44
spacy/tests/lang/hi/test_lex_attrs.py
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44
spacy/tests/lang/hi/test_lex_attrs.py
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# coding: utf-8
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from __future__ import unicode_literals
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import pytest
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from spacy.lang.hi.lex_attrs import norm, like_num
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def test_hi_tokenizer_handles_long_text(hi_tokenizer):
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text = """
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ये कहानी 1900 के दशक की है। कौशल्या (स्मिता जयकर) को पता चलता है कि उसका
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छोटा बेटा, देवदास (शाहरुख खान) वापस घर आ रहा है। देवदास 10 साल पहले कानून की
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पढ़ाई करने के लिए इंग्लैंड गया था। उसके लौटने की खुशी में ये बात कौशल्या अपनी पड़ोस
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में रहने वाली सुमित्रा (किरण खेर) को भी बता देती है। इस खबर से वो भी खुश हो जाती है।
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"""
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tokens = hi_tokenizer(text)
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assert len(tokens) == 86
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@pytest.mark.parametrize(
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"word,word_norm",
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[
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("चलता", "चल"),
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("पढ़ाई", "पढ़"),
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("देती", "दे"),
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("जाती", "ज"),
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("मुस्कुराकर", "मुस्कुर"),
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],
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)
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def test_hi_norm(word, word_norm):
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assert norm(word) == word_norm
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@pytest.mark.parametrize(
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"word", ["१९८७", "1987", "१२,२६७", "उन्नीस", "पाँच", "नवासी", "५/१०"],
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)
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def test_hi_like_num(word):
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assert like_num(word)
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@pytest.mark.parametrize(
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"word", ["पहला", "तृतीय", "निन्यानवेवाँ", "उन्नीस", "तिहत्तरवाँ", "छत्तीसवाँ",],
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)
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def test_hi_like_num_ordinal_words(word):
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assert like_num(word)
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