def load_from_file(fname, vocab=None, alphabet=None, tags=None):
"""Load dataset from the given file."""
reader = conllu.reader(fname)
learn_tags, learn_vocab, tagset, vocab, alphabet = \
TaggingDataset.initialize_vocab_and_tags(tags, vocab, alphabet)
seqs = []
for sentence in reader:
words = []
tags = []
chars = []
lemma_chars = []
for word in sentence:
word_id, char_ids, tag_id, lemma_char_ids = \
TaggingDataset.get_word_and_tag_id(word, vocab, alphabet, tagset,
learn_vocab, learn_tags)
words.append(word_id)
chars.append(char_ids)
tags.append(tag_id)
lemma_chars.append(lemma_char_ids)
seqs.append((words, chars, tags, lemma_chars))
res = TaggingDataset(seqs, vocab, alphabet, tagset)
return res
def load_from_file(fname, vocab=None, alphabet=None, tags=None):
"""Load dataset from the given file."""
reader = conllu.reader(fname)
learn_tags, learn_vocab, tagset, vocab, alphabet = \
TaggingDataset.initialize_vocab_and_tags(tags, vocab, alphabet)
seqs = []
for sentence in reader:
words = []
tags = []
chars = []
lemma_chars = []
for word in sentence:
word_id, char_ids, tag_id, lemma_char_ids = \
TaggingDataset.get_word_and_tag_id(word, vocab, alphabet, tagset,
learn_vocab, learn_tags)
words.append(word_id)
chars.append(char_ids)
tags.append(tag_id)
lemma_chars.append(lemma_char_ids)
seqs.append((words, chars, tags, lemma_chars))
res = TaggingDataset(seqs, vocab, alphabet, tagset)
return res
def evaluate_tagger_and_writeout(tagger):
stdin = conllu.reader()
stdout = conllu.writer()
for sentence in stdin:
x = []
for word in sentence:
x.append(tagger.vocab.get(TaggingDataset.word_obj_to_str(word), tagger.vocab['#OOV']))
x = np.array([x], dtype='int32')
y_hat = tagger.predict(x)[0]
y_hat_str = [tagger.tags.rev(tag_id) for tag_id in y_hat]
for word, utag in zip(sentence, y_hat_str):
word.upos = utag
stdout.write_sentence(sentence)
def evaluate_tagger_and_writeout(tagger):
stdin = conllu.reader()
stdout = conllu.writer()
for sentence in stdin:
x = []
for word in sentence:
x.append(
tagger.vocab.get(TaggingDataset.word_obj_to_str(word),
tagger.vocab['#OOV']))
x = np.array([x], dtype='int32')
y_hat = tagger.predict(x)[0]
y_hat_str = [tagger.tags.rev(tag_id) for tag_id in y_hat]
for word, utag in zip(sentence, y_hat_str):
word.upos = utag
stdout.write_sentence(sentence)
def load_from_file(fname, vocab=None, tags=None):
"""Load dataset from the given file."""
reader = conllu.reader(fname)
learn_tags, learn_vocab, tags, vocab = TaggingDataset.initialize_vocab_and_tags(tags, vocab)
seqs = []
for sentence in reader:
x = []
y = []
for word in sentence:
word_id, tag_id = TaggingDataset.get_word_and_tag_id(word, vocab, tags, learn_vocab, learn_tags)
x.append(word_id)
y.append(tag_id)
seqs.append((x, y))
res = TaggingDataset(seqs, vocab, tags)
return res
def load_from_file(fname, vocab=None, tags=None):
"""Load dataset from the given file."""
reader = conllu.reader(fname)
learn_tags, learn_vocab, tags, vocab = TaggingDataset.initialize_vocab_and_tags(
tags, vocab)
seqs = []
for sentence in reader:
x = []
y = []
for word in sentence:
word_id, tag_id = TaggingDataset.get_word_and_tag_id(
word, vocab, tags, learn_vocab, learn_tags)
x.append(word_id)
y.append(tag_id)
seqs.append((x, y))
res = TaggingDataset(seqs, vocab, tags)
return res
# Mathematics and Physics, Charles University in Prague, Czech Republic.
#
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at http://mozilla.org/MPL/2.0/.
import collections
import sys
import conllu
dictionary = {}
# Process all arguments as training files
for arg in sys.argv[1:]:
reader = conllu.reader(arg)
sentence = []
while reader.next_sentence(sentence):
for word in sentence:
dictionary.setdefault(
word.form, collections.defaultdict(lambda: 0))["\t".join(
[word.lemma, word.upos, word.lpos, word.feats])] += 1
# Find most frequent analysis, using the lexicographically smaller when equal
for form in dictionary:
best, best_count = '', 0
for analysis, count in dictionary[form].iteritems():
if count > best_count or (count == best_count and analysis < best):
best, best_count = analysis, count
dictionary[form] = best
# Mathematics and Physics, Charles University in Prague, Czech Republic.
#
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at http://mozilla.org/MPL/2.0/.
import collections
import sys
import conllu
dictionary = {}
# Process all arguments as training files
for arg in sys.argv[1:]:
reader = conllu.reader(arg)
sentence = []
while reader.next_sentence(sentence):
for word in sentence:
dictionary.setdefault(word.form, collections.defaultdict(lambda:0))["\t".join([word.lemma, word.upos, word.lpos, word.feats])] += 1
# Find most frequent analysis, using the lexicographically smaller when equal
for form in dictionary:
best, best_count = '', 0
for analysis, count in dictionary[form].iteritems():
if count > best_count or (count == best_count and analysis < best):
best, best_count = analysis, count
dictionary[form] = best
# Analyse all data passed on standard input to standard output
stdin = conllu.reader()
#!/usr/bin/env python
# This file is part of RH_NNTagging <http://github.com/ufal/rh_nntagging/>.
#
# Copyright 2015 Institute of Formal and Applied Linguistics, Faculty of
# Mathematics and Physics, Charles University in Prague, Czech Republic.
#
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at http://mozilla.org/MPL/2.0/.
# Remove all annotations from CoNLL-U file except for form.
# Reads from files given as arguments and writes to standard output.
import sys
import conllu
stdout = conllu.writer()
sentence = []
for arg in sys.argv[1:]:
reader = conllu.reader(arg)
while reader.next_sentence(sentence):
for word in sentence:
word.lemma, word.upos, word.lpos, word.feats, word.head, word.deprel, word.deps, word.misc = '', '', '', '', -1, '', '', ''
stdout.write_sentence(sentence)
