def test_multiword_entities(tmp_path):
data = """
{
"rasa_nlu_data": {
"common_examples" : [
{
"text": "show me flights to New York City",
"intent": "unk",
"entities": [
{
"entity": "destination",
"start": 19,
"end": 32,
"value": "New York City"
}
]
}
]
}
}"""
f = tmp_path / "tmp_training_data.json"
f.write_text(data, rasa.shared.utils.io.DEFAULT_ENCODING)
td = load_data(str(f))
assert len(td.entity_examples) == 1
example = td.entity_examples[0]
entities = example.get("entities")
assert len(entities) == 1
tokens = WhitespaceTokenizer().tokenize(example, attribute=TEXT)
start, end = MitieEntityExtractor.find_entity(entities[0],
example.get(TEXT), tokens)
assert start == 4
assert end == 7
def test_repeated_entities(tmp_path):
data = """
{
"rasa_nlu_data": {
"common_examples" : [
{
"text": "book a table today from 3 to 6 for 3 people",
"intent": "unk",
"entities": [
{
"entity": "description",
"start": 35,
"end": 36,
"value": "3"
}
]
}
]
}
}"""
f = tmp_path / "tmp_training_data.json"
f.write_text(data, rasa.shared.utils.io.DEFAULT_ENCODING)
td = load_data(str(f))
assert len(td.entity_examples) == 1
example = td.entity_examples[0]
entities = example.get("entities")
assert len(entities) == 1
tokens = WhitespaceTokenizer().tokenize(example, attribute=TEXT)
start, end = MitieEntityExtractor.find_entity(entities[0],
example.get(TEXT), tokens)
assert start == 9
assert end == 10
def test_multiword_entities():
data = """
{
"rasa_nlu_data": {
"common_examples" : [
{
"text": "show me flights to New York City",
"intent": "unk",
"entities": [
{
"entity": "destination",
"start": 19,
"end": 32,
"value": "New York City"
}
]
}
]
}
}"""
with tempfile.NamedTemporaryFile(suffix="_tmp_training_data.json") as f:
f.write(data.encode("utf-8"))
f.flush()
td = training_data.load_data(f.name)
assert len(td.entity_examples) == 1
example = td.entity_examples[0]
entities = example.get("entities")
assert len(entities) == 1
tokens = WhitespaceTokenizer().tokenize(example.text)
start, end = MitieEntityExtractor.find_entity(entities[0],
example.text, tokens)
assert start == 4
assert end == 7
def test_repeated_entities():
data = """
{
"rasa_nlu_data": {
"common_examples" : [
{
"text": "book a table today from 3 to 6 for 3 people",
"intent": "unk",
"entities": [
{
"entity": "description",
"start": 35,
"end": 36,
"value": "3"
}
]
}
]
}
}"""
with tempfile.NamedTemporaryFile(suffix="_tmp_training_data.json") as f:
f.write(data.encode("utf-8"))
f.flush()
td = training_data.load_data(f.name)
assert len(td.entity_examples) == 1
example = td.entity_examples[0]
entities = example.get("entities")
assert len(entities) == 1
tokens = WhitespaceTokenizer().tokenize(example.text)
start, end = MitieEntityExtractor.find_entity(entities[0],
example.text, tokens)
assert start == 9
assert end == 10
def inner(config: Dict[Text, Any],
load: bool = False) -> MitieEntityExtractor:
if load:
constructor = MitieEntityExtractor.load
else:
constructor = MitieEntityExtractor.create
return constructor(
model_storage=default_model_storage,
execution_context=default_execution_context,
resource=Resource("MitieEntityExtractor"),
config={
**MitieEntityExtractor.get_default_config(),
**config
},
)