Gh 5339 language class for malayalam (#5342)

* Initialize Malayalam Language class

* Add lex_attrs and examples for Malayalam

* Add spaCy Contributor Agreement

* Add test for ml tokenizer

.github/contributors/sabiqueqb.md

@@ -0,0 +1,106 @@
+# spaCy contributor agreement
+
+This spaCy Contributor Agreement (**"SCA"**) is based on the
+[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
+The SCA applies to any contribution that you make to any product or project
+managed by us (the **"project"**), and sets out the intellectual property rights
+you grant to us in the contributed materials. The term **"us"** shall mean
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+**"you"** shall mean the person or entity identified below.
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+## Contributor Agreement
+
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+6. This SCA is governed by the laws of the State of California and applicable
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+    actual authority to contractually bind that entity.
+
+## Contributor Details
+
+| Field                          | Entry                |
+|------------------------------- | -------------------- |
+| Name                           | Sabique Ahammed Lava |
+| Company name (if applicable)   | QBurst               |
+| Title or role (if applicable)  | Senior Engineer      |
+| Date                           | 24 Apr 2020          |
+| GitHub username                | sabiqueqb            |
+| Website (optional)             |                      |

spacy/lang/ml/__init__.py

@@ -0,0 +1,18 @@
+# coding: utf8
+from __future__ import unicode_literals
+
+from .stop_words import STOP_WORDS
+
+from ...language import Language
+
+
+class MalayalamDefaults(Language.Defaults):
+    stop_words = STOP_WORDS
+
+
+class Malayalam(Language):
+    lang = "ml"
+    Defaults = MalayalamDefaults
+
+
+__all__ = ["Malayalam"]

spacy/lang/ml/examples.py

@@ -0,0 +1,19 @@
+# coding: utf8
+from __future__ import unicode_literals
+
+
+"""
+Example sentences to test spaCy and its language models.
+
+>>> from spacy.lang.ml.examples import sentences
+>>> docs = nlp.pipe(sentences)
+"""
+
+
+sentences = [
+    "അനാവശ്യമായി കണ്ണിലും മൂക്കിലും വായിലും സ്പർശിക്കാതിരിക്കുക",
+    "പൊതുരംഗത്ത് മലയാള ഭാഷയുടെ സമഗ്രപുരോഗതി ലക്ഷ്യമാക്കി പ്രവർത്തിക്കുന്ന സംഘടനയായ മലയാളഐക്യവേദിയുടെ വിദ്യാർത്ഥിക്കൂട്ടായ്മയാണ് വിദ്യാർത്ഥി മലയാളവേദി",
+    "എന്താണ്‌ കവാടങ്ങൾ?",
+    "ചുരുക്കത്തിൽ വിക്കിപീഡിയയുടെ ഉള്ളടക്കത്തിലേക്കുള്ള പടിപ്പുരകളാണ്‌‌ കവാടങ്ങൾ. അവ ലളിതവും വായനക്കാരനെ ആകർഷിക്കുന്നതുമായിരിക്കും",
+    "പതിനൊന്നുപേർ വീതമുള്ള രണ്ടു ടീമുകൾ കളിക്കുന്ന സംഘകായിക വിനോദമാണു ക്രിക്കറ്റ്",
+]

spacy/lang/ml/lex_attrs.py

@@ -0,0 +1,80 @@
+# coding: utf8
+from __future__ import unicode_literals
+
+from ...attrs import LIKE_NUM
+
+
+# reference 2: https://www.omniglot.com/language/numbers/malayalam.htm
+
+_num_words = [
+    "പൂജ്യം ",
+    "ഒന്ന് ",
+    "രണ്ട് ",
+    "മൂന്ന് ",
+    "നാല്‌ ",
+    "അഞ്ച് ",
+    "ആറ് ",
+    "ഏഴ് ",
+    "എട്ട് ",
+    "ഒന്‍പത് ",
+    "പത്ത് ",
+    "പതിനൊന്ന്",
+    "പന്ത്രണ്ട്",
+    "പതി മൂന്നു",
+    "പതിനാല്",
+    "പതിനഞ്ച്",
+    "പതിനാറ്",
+    "പതിനേഴ്",
+    "പതിനെട്ട്",
+    "പത്തൊമ്പതു",
+    "ഇരുപത്",
+    "ഇരുപത്തിഒന്ന്",
+    "ഇരുപത്തിരണ്ട്‌",
+    "ഇരുപത്തിമൂന്ന്",
+    "ഇരുപത്തിനാല്",
+    "ഇരുപത്തിഅഞ്ചു",
+    "ഇരുപത്തിആറ്",
+    "ഇരുപത്തിഏഴ്",
+    "ഇരുപത്തിഎട്ടു",
+    "ഇരുപത്തിഒന്‍പത്",
+    "മുപ്പത്",
+    "മുപ്പത്തിഒന്ന്",
+    "മുപ്പത്തിരണ്ട്",
+    "മുപ്പത്തിമൂന്ന്",
+    "മുപ്പത്തിനാല്",
+    "മുപ്പത്തിഅഞ്ചു",
+    "മുപ്പത്തിആറ്",
+    "മുപ്പത്തിഏഴ്",
+    "മുപ്പത്തിഎട്ട്",
+    "മുപ്പത്തിഒന്‍പതു",
+    "നാല്‍പത്‌ ",
+    "അന്‍പത് ",
+    "അറുപത് ",
+    "എഴുപത് ",
+    "എണ്‍പത് ",
+    "തൊണ്ണൂറ് ",
+    "നുറ് ",
+    "ആയിരം ",
+    "പത്തുലക്ഷം"
+]
+
+
+def like_num(text):
+    """
+    Check if text resembles a number
+    """
+    if text.startswith(("+", "-", "±", "~")):
+        text = text[1:]
+    text = text.replace(",", "").replace(".", "")
+    if text.isdigit():
+        return True
+    if text.count("/") == 1:
+        num, denom = text.split("/")
+        if num.isdigit() and denom.isdigit():
+            return True
+    if text in _num_words:
+        return True
+    return False
+
+
+LEX_ATTRS = {LIKE_NUM: like_num}

spacy/lang/ml/stop_words.py

@@ -0,0 +1,18 @@
+# coding: utf8
+from __future__ import unicode_literals
+
+
+STOP_WORDS = set(
+
+    """
+അത്
+ഇത്
+ആയിരുന്നു
+ആകുന്നു
+വരെ
+അന്നേരം
+അന്ന്
+ഇന്ന്
+ആണ്
+""".split()
+)

spacy/tests/conftest.py

@@ -150,6 +150,11 @@ def lt_tokenizer():
     return get_lang_class("lt").Defaults.create_tokenizer()
 
 
+@pytest.fixture(scope="session")
+def ml_tokenizer():
+    return get_lang_class("ml").Defaults.create_tokenizer()
+
+
 @pytest.fixture(scope="session")
 def nb_tokenizer():
     return get_lang_class("nb").Defaults.create_tokenizer()

spacy/tests/lang/ml/test_text.py

@@ -0,0 +1,16 @@
+# coding: utf-8
+from __future__ import unicode_literals
+
+import pytest
+
+
+def test_ml_tokenizer_handles_long_text(ml_tokenizer):
+    text = """അനാവശ്യമായി കണ്ണിലും മൂക്കിലും വായിലും സ്പർശിക്കാതിരിക്കുക"""
+    tokens = ml_tokenizer(text)
+    assert len(tokens) == 5
+
+
+@pytest.mark.parametrize("text,length", [("എന്നാൽ അച്ചടിയുടെ ആവിർഭാവം ലിപിയിൽ കാര്യമായ മാറ്റങ്ങൾ വരുത്തിയത് കൂട്ടക്ഷരങ്ങളെ അണുഅക്ഷരങ്ങളായി പിരിച്ചുകൊണ്ടായിരുന്നു", 10), ("പരമ്പരാഗതമായി മലയാളം ഇടത്തുനിന്ന് വലത്തോട്ടാണ് എഴുതുന്നത്", 5)])
+def test_ml_tokenizer_handles_cnts(ml_tokenizer, text, length):
+    tokens = ml_tokenizer(text)
+    assert len(tokens) == length