<commit_before># coding: utf8

from __future__ import unicode_literals
from nltk.tag.api import TaggerI
from nltk.metrics import accuracy
from wapiti import Model


class SequenceTagger(TaggerI):
	""" wrapper for [Wapiti](http://wapiti.limsi.fr) sequence tagger

	>>> tagger = SequenceTagger(patterns=['*', 'u:word-%x[0,0]'])
	>>> tagger.train([[('من', 'PRO'), ('به', 'P'), ('مدرسه', 'N'), ('رفته_بودم', 'V'), ('.', 'PUNC')]])
	>>> tagger.tag_sents([['من', 'به', 'مدرسه', 'رفته_بودم', '.']])
	[[('من', 'PRO'), ('به', 'P'), ('مدرسه', 'N'), ('رفته_بودم', 'V'), ('.', 'PUNC')]]

	>>> tagger.save_model('resources/test.model')
	>>> SequenceTagger(model='resources/test.model').tag_sents([['من', 'به', 'مدرسه', 'رفته_بودم', '.']])
	[[('من', 'PRO'), ('به', 'P'), ('مدرسه', 'N'), ('رفته_بودم', 'V'), ('.', 'PUNC')]]
	"""

	def __init__(self, patterns=[], **options):
		self.model = Model(patterns='\n'.join(patterns), **options)

	def train(self, sentences):
		self.model.train(['\n'.join([' '.join(word) for word in sentence]) for sentence in sentences])

	def save_model(self, filename):
		self.model.save(filename)

	def tag_sents(self, sentences):
		sentences = list(sentences)
		lines = '\n\n'.join(['\n'.join(sentence) for sentence in sentences])
		results = self.model.label_sequence(lines).decode('utf8')
		tags = iter(results.strip().split('\n'))
		return [[(word, next(tags)) for word in sentence] for sentence in sentences]


class IOBTagger(SequenceTagger):
	""" wrapper for [Wapiti](http://wapiti.limsi.fr) sequence tagger

	>>> tagger = IOBTagger(patterns=['*', 'U:word-%x[0,0]', 'U:word-%x[0,1]'])
	>>> tagger.train([[('من', 'PRO', 'B-NP'), ('به', 'P', 'B-PP'), ('مدرسه', 'N', 'B-NP'), ('رفته_بودم', 'V', 'B-VP'), ('.', 'PUNC', 'O')]])
	>>> tagger.tag_sents([[('من', 'PRO'), ('به', 'P'), ('مدرسه', 'N'), ('رفته_بودم', 'V'), ('.', 'PUNC')]])
	[[('من', 'PRO', 'B-NP'), ('به', 'P', 'B-PP'), ('مدرسه', 'N', 'B-NP'), ('رفته_بودم', 'V', 'B-VP'), ('.', 'PUNC', 'O')]]
	"""

	def tag_sents(self, sentences):
		sentences = list(sentences)
		lines = '\n\n'.join(['\n'.join([' '.join(word) for word in sentence]) for sentence in sentences])
		results = self.model.label_sequence(lines).decode('utf8')
		tags = iter(results.strip().split('\n'))
		return [[(word[0], word[1], next(tags)) for word in sentence] for sentence in sentences]

	def evaluate(self, gold):
		tagged_sents = self.tag_sents(([tokens[:-1] for tokens in sent] for sent in gold))
		return accuracy(sum(gold, []), sum(tagged_sents, []))<commit_msg>fix IOBTagger for inputs with more than 3 columns<commit_after># coding: utf8

from __future__ import unicode_literals
from nltk.tag.api import TaggerI
from nltk.metrics import accuracy
from wapiti import Model


class SequenceTagger(TaggerI):
	""" wrapper for [Wapiti](http://wapiti.limsi.fr) sequence tagger

	>>> tagger = SequenceTagger(patterns=['*', 'u:word-%x[0,0]'])
	>>> tagger.train([[('من', 'PRO'), ('به', 'P'), ('مدرسه', 'N'), ('رفته_بودم', 'V'), ('.', 'PUNC')]])
	>>> tagger.tag_sents([['من', 'به', 'مدرسه', 'رفته_بودم', '.']])
	[[('من', 'PRO'), ('به', 'P'), ('مدرسه', 'N'), ('رفته_بودم', 'V'), ('.', 'PUNC')]]

	>>> tagger.save_model('resources/test.model')
	>>> SequenceTagger(model='resources/test.model').tag_sents([['من', 'به', 'مدرسه', 'رفته_بودم', '.']])
	[[('من', 'PRO'), ('به', 'P'), ('مدرسه', 'N'), ('رفته_بودم', 'V'), ('.', 'PUNC')]]
	"""

	def __init__(self, patterns=[], **options):
		self.model = Model(patterns='\n'.join(patterns), **options)

	def train(self, sentences):
		self.model.train(['\n'.join([' '.join(word) for word in sentence]) for sentence in sentences])

	def save_model(self, filename):
		self.model.save(filename)

	def tag_sents(self, sentences):
		sentences = list(sentences)
		lines = '\n\n'.join(['\n'.join(sentence) for sentence in sentences])
		results = self.model.label_sequence(lines).decode('utf8')
		tags = iter(results.strip().split('\n'))
		return [[(word, next(tags)) for word in sentence] for sentence in sentences]


class IOBTagger(SequenceTagger):
	""" wrapper for [Wapiti](http://wapiti.limsi.fr) sequence tagger

	>>> tagger = IOBTagger(patterns=['*', 'U:word-%x[0,0]', 'U:word-%x[0,1]'])
	>>> tagger.train([[('من', 'PRO', 'B-NP'), ('به', 'P', 'B-PP'), ('مدرسه', 'N', 'B-NP'), ('رفته_بودم', 'V', 'B-VP'), ('.', 'PUNC', 'O')]])
	>>> tagger.tag_sents([[('من', 'PRO'), ('به', 'P'), ('مدرسه', 'N'), ('رفته_بودم', 'V'), ('.', 'PUNC')]])
	[[('من', 'PRO', 'B-NP'), ('به', 'P', 'B-PP'), ('مدرسه', 'N', 'B-NP'), ('رفته_بودم', 'V', 'B-VP'), ('.', 'PUNC', 'O')]]
	"""

	def tag_sents(self, sentences):
		sentences = list(sentences)
		lines = '\n\n'.join(['\n'.join([' '.join(word) for word in sentence]) for sentence in sentences])
		results = self.model.label_sequence(lines).decode('utf8')
		tags = iter(results.strip().split('\n'))
		return [[word + (next(tags),) for word in sentence] for sentence in sentences]

	def evaluate(self, gold):
		tagged_sents = self.tag_sents(([word[:-1] for word in sentence] for sentence in gold))
		return accuracy(sum(gold, []), sum(tagged_sents, []))
{'fraction_non_alphanumeric': 0.2706766917293233, 'fraction_numerical': 0.004550850811238623, 'mean_word_length': 5.505791505791506, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 51, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '6575110', 'n_tokens_mistral': 2146, 'n_tokens_neox': 2064, 'n_words': 429}