def test_pipeline_without_word_features():
tokenizer_config = TokenizerConfiguration()
char_features = CharFeatures(
embedding_dim=2,
encoder={
"type": "gru",
"hidden_size": 2,
"num_layers": 1,
"bidirectional": True,
},
dropout=0.1,
)
features_config = FeaturesConfiguration(char=char_features)
encoder_spec = Seq2SeqEncoderConfiguration(type="gru",
hidden_size=2,
num_layers=1,
bidirectional=True)
head_spec = TaskHeadConfiguration(
type="TextClassification",
labels=["duplicate", "not_duplicate"],
pooler={"type": "boe"},
)
pipeline_config = PipelineConfiguration(
name="no_word_features",
head=head_spec,
features=features_config,
tokenizer=tokenizer_config,
encoder=encoder_spec,
)
pl = Pipeline.from_config(pipeline_config)
assert "word" not in pl.backbone.featurizer.indexer
assert "char" in pl.backbone.featurizer.indexer
def test_set_sentence_segmentation_with_max_number_of_sentences():
tokenizer = Tokenizer(TokenizerConfiguration(max_nr_of_sentences=2))
tokenized = tokenizer.tokenize_document([
"This is a sentence. This is another sentence.",
"One more sentence here.",
"Last sentence here.",
])
assert len(tokenized) == 2
def test_pipeline_config(pipeline_yaml):
tokenizer_config = TokenizerConfiguration(
text_cleaning={"rules": ["strip_spaces"]}, use_spacy_tokens=True)
word_features = WordFeatures(embedding_dim=2, lowercase_tokens=True)
char_features = CharFeatures(
embedding_dim=2,
encoder={
"type": "gru",
"hidden_size": 2,
"num_layers": 1,
"bidirectional": True,
},
dropout=0.1,
)
features_config = FeaturesConfiguration(word=word_features,
char=char_features)
encoder_spec = Seq2SeqEncoderConfiguration(type="gru",
hidden_size=2,
num_layers=1,
bidirectional=True)
head_spec = TaskHeadConfiguration(
type=TextClassification,
labels=["duplicate", "not_duplicate"],
pooler={"type": "boe"},
)
pipeline_config = PipelineConfiguration(
name="test_pipeline_config",
head=head_spec,
features=features_config,
tokenizer=tokenizer_config,
encoder=encoder_spec,
)
pl = Pipeline.from_config(pipeline_config)
pl_yaml = Pipeline.from_yaml(pipeline_yaml)
assert pl.named_trainable_parameters == pl_yaml.named_trainable_parameters
assert pl.num_trainable_parameters == pl_yaml.num_trainable_parameters
assert pl.num_parameters == pl_yaml.num_parameters
sample_text = "My simple text"
for instance in [
pl.backbone.featurizer(sample_text),
pl_yaml.backbone.featurizer(sample_text),
]:
for key, value in instance.items():
assert key == "record"
assert isinstance(value, ListField)
assert len(value) == 1
for text in value:
assert isinstance(text, TextField)
assert all(map(lambda t: isinstance(t, Token), text.tokens))
assert sample_text == " ".join([t.text for t in text.tokens])
def test_min_max_sentence_length():
tokenizer = Tokenizer(
TokenizerConfiguration(segment_sentences=True,
min_sentence_length=10,
max_sentence_length=15))
tokenized = tokenizer.tokenize_text(
"short. A very long sentence. This is fine")
assert len(tokenized) == 1
assert len(tokenized[0]) == 3
def test_pipeline_default_tokenizer(pipeline_dict):
pipeline_dict["features"].update({"word": {"embedding_dim": 2}})
pl = Pipeline.from_config(pipeline_dict)
assert pl.config.tokenizer_config == TokenizerConfiguration()
assert pl.config.features.transformers.mismatched is True
assert (type(pl.backbone.featurizer.indexer["transformers"]) is
PretrainedTransformerMismatchedIndexer)
assert type(pl.backbone.tokenizer) is Tokenizer
prediction = pl.predict("Test this!")
def test_document_cleaning():
tokenizer = Tokenizer(
TokenizerConfiguration(
text_cleaning={"rules": ["html_to_text", "strip_spaces"]},
segment_sentences=True,
))
tokenized = tokenizer.tokenize_document([html_text])
assert len(tokenized) == 2
assert (len(tokenized[0]) == 7
), "Expected [My, First, Heading, My, first, paragraph, .]"
assert len(tokenized[1]) == 4, "Expected [My, second, paragraph, .]"
def test_text_cleaning_with_sentence_segmentation_and_max_sequence():
tokenizer = Tokenizer(
TokenizerConfiguration(
max_sequence_length=8,
text_cleaning={"rules": ["html_to_text", "strip_spaces"]},
segment_sentences=True,
))
tokenized = tokenizer.tokenize_text(html_text)
assert len(tokenized) == 2
assert len(tokenized[0]) == 2, "Expected [My, First]"
assert len(tokenized[1]) == 2, "Expected [My, second]"
def test_using_allennlp_tokens():
tokenizer = Tokenizer(TokenizerConfiguration(use_spacy_tokens=False))
tokenized = tokenizer.tokenize_text("This is a text")
assert len(tokenized) == 1
assert len(tokenized[0]) == 4
assert all(map(lambda t: isinstance(t, AllennlpToken), tokenized[0]))