def test_count_vocab_items_uses_pos_tags(self):
tokens = self.tokenizer.split_words("This is a sentence.")
tokens = [Token("<S>")] + [t for t in tokens] + [Token("</S>")]
indexer = DepLabelIndexer()
counter = defaultdict(lambda: defaultdict(int))
for token in tokens:
indexer.count_vocab_items(token, counter)
assert counter["dep_labels"] == {"ROOT": 1, "nsubj": 1, "advmod": 3, "NONE": 2}
def test_tokens_to_indices_uses_pos_tags(self):
tokens = self.tokenizer.split_words("This is a sentence.")
tokens = [t for t in tokens] + [Token("</S>")]
vocab = Vocabulary()
root_index = vocab.add_token_to_namespace('ROOT', namespace='dep_labels')
none_index = vocab.add_token_to_namespace('NONE', namespace='dep_labels')
indexer = DepLabelIndexer()
assert indexer.tokens_to_indices([tokens[1]], vocab, "tokens1") == {"tokens1": [root_index]}
assert indexer.tokens_to_indices([tokens[-1]], vocab, "tokens-1") == {"tokens-1": [none_index]}
def test_tokens_to_indices_uses_pos_tags(self):
tokens = self.tokenizer.split_words("This is a sentence.")
tokens = [t for t in tokens] + [Token("</S>")]
vocab = Vocabulary()
root_index = vocab.add_token_to_namespace('ROOT', namespace='dep_labels')
none_index = vocab.add_token_to_namespace('NONE', namespace='dep_labels')
indexer = DepLabelIndexer()
assert indexer.tokens_to_indices([tokens[1]], vocab, "tokens1") == {"tokens1": [root_index]}
assert indexer.tokens_to_indices([tokens[-1]], vocab, "tokens-1") == {"tokens-1": [none_index]}
def test_tokens_to_indices_uses_pos_tags(self):
tokens = self.tokenizer.tokenize("This is a sentence.")
tokens = [t for t in tokens] + [Token("</S>")]
vocab = Vocabulary()
root_index = vocab.add_token_to_namespace("ROOT", namespace="dep_labels")
none_index = vocab.add_token_to_namespace("NONE", namespace="dep_labels")
indexer = DepLabelIndexer()
assert indexer.tokens_to_indices([tokens[1]], vocab) == {"tokens": [root_index]}
assert indexer.tokens_to_indices([tokens[-1]], vocab) == {"tokens": [none_index]}
def test_count_vocab_items_uses_pos_tags(self):
tokens = self.tokenizer.split_words("This is a sentence.")
tokens = [Token("<S>")] + [t for t in tokens] + [Token("</S>")]
indexer = DepLabelIndexer()
counter = defaultdict(lambda: defaultdict(int))
for token in tokens:
indexer.count_vocab_items(token, counter)
assert counter["dep_labels"] == {"ROOT": 1, "nsubj": 1,
"det": 1, "NONE": 2, "attr": 1, "punct": 1}
def test_count_vocab_items_uses_pos_tags(self):
tokens = self.tokenizer.split_words(u"This is a sentence.")
tokens = [Token(u"<S>")] + [t for t in tokens] + [Token(u"</S>")]
indexer = DepLabelIndexer()
counter = defaultdict(lambda: defaultdict(int))
for token in tokens:
indexer.count_vocab_items(token, counter)
assert counter[u"dep_labels"] == {
u"ROOT": 1,
u"nsubj": 1,
u"det": 1,
u"NONE": 2,
u"attr": 1,
u"punct": 1
}
def update_sentence_dep(
self,
fields,
tokens
) -> Field:
indexers = {'dep_tag': DepLabelIndexer(namespace='dep_tag')}
textfield = TextField(tokens, indexers)
return textfield
def test_as_array_produces_token_sequence(self):
indexer = DepLabelIndexer()
padded_tokens = indexer.as_padded_tensor({"key": [1, 2, 3, 4, 5]},
{"key": 10}, {})
assert padded_tokens["key"].tolist() == [1, 2, 3, 4, 5, 0, 0, 0, 0, 0]
def test_padding_functions(self):
indexer = DepLabelIndexer()
assert indexer.get_padding_lengths(0) == {}
def test_as_array_produces_token_sequence(self):
indexer = DepLabelIndexer()
padded_tokens = indexer.pad_token_sequence([1, 2, 3, 4, 5], 10, {})
assert padded_tokens == [1, 2, 3, 4, 5, 0, 0, 0, 0, 0]
def test_as_array_produces_token_sequence(self):
indexer = DepLabelIndexer()
padded_tokens = indexer.pad_token_sequence({u'key': [1, 2, 3, 4, 5]},
{u'key': 10}, {})
assert padded_tokens == {u'key': [1, 2, 3, 4, 5, 0, 0, 0, 0, 0]}
def test_as_array_produces_token_sequence(self):
indexer = DepLabelIndexer()
padded_tokens = indexer.pad_token_sequence({'key': [1, 2, 3, 4, 5]}, {'key': 10}, {})
assert padded_tokens == {'key': [1, 2, 3, 4, 5, 0, 0, 0, 0, 0]}
def test_padding_functions(self):
indexer = DepLabelIndexer()
assert indexer.get_padding_token() == 0
assert indexer.get_padding_lengths(0) == {}
def test_as_array_produces_token_sequence(self):
indexer = DepLabelIndexer()
padded_tokens = indexer.pad_token_sequence([1, 2, 3, 4, 5], 10, {})
assert padded_tokens == [1, 2, 3, 4, 5, 0, 0, 0, 0, 0]
train_dataset_folder = "C:/Users/t-ofarvi/PycharmProjects/UCCA_Dataset_29-06-09/tryout" #"C:/Users/t-ofarvi/Desktop/train_allen"
validation_dataset_folder = "C:/Users/t-ofarvi/PycharmProjects/UCCA_Dataset_29-06-09/tryout-validation" #"C:/Users/t-ofarvi/Desktop/dev_allen"
model_dir = "C:/Users/t-ofarvi/PycharmProjects/tryout-model"
vocab_dir = f'{model_dir}/vocabulary'
# NOTE: The word tokenizer is a SpaCy tokenizer, which is a little different from the BERT tokenizer.
# This was done for convince.
word_tokenizer = SpacyMultilingualWhitespaceWordSplitter()
bert_indexer = PretrainedBertIndexer(pretrained_model=bert_mode,
do_lowercase=bert_do_lowercase,
truncate_long_sequences=False)
word_indexer = {
"bert": bert_indexer,
"deps": DepLabelIndexer(namespace="deps_tags"),
"ner": NerTagIndexer(),
"pos": PosTagIndexer(),
"lang": LanguageIndexer()
}
train_ds, validation_ds = (
UccaSpanParserDatasetReader(word_tokenizer, word_indexer).read(folder)
for folder in [train_dataset_folder, validation_dataset_folder])
if os.path.exists(vocab_dir):
vocab = Vocabulary.from_files(vocab_dir)
else:
vocab = Vocabulary.from_instances(
itertools.chain(train_ds, validation_ds))
vocab.save_to_files(vocab_dir)