def read_from_json(self, js, **kwargs):
"""Loads training data stored in the rasa NLU data format."""
validate_rasa_nlu_data(js)
data = js['rasa_nlu_data']
common_examples = data.get("common_examples", [])
intent_examples = data.get("intent_examples", [])
entity_examples = data.get("entity_examples", [])
entity_synonyms = data.get("entity_synonyms", [])
regex_features = data.get("regex_features", [])
entity_synonyms = transform_entity_synonyms(entity_synonyms)
if intent_examples or entity_examples:
logger.warn("DEPRECATION warning: your rasa data "
"contains 'intent_examples' "
"or 'entity_examples' which will be "
"removed in the future. Consider "
"putting all your examples "
"into the 'common_examples' section.")
all_examples = common_examples + intent_examples + entity_examples
training_examples = []
for ex in all_examples:
msg = Message.build(ex['text'], ex.get("intent"),
ex.get("entities"))
training_examples.append(msg)
return TrainingData(training_examples, entity_synonyms, regex_features)
def read_from_json(self, js, **kwargs):
"""Loads training data stored in the rasa NLU data format."""
validate_rasa_nlu_data(js)
data = js['rasa_nlu_data']
common_examples = data.get("common_examples", [])
intent_examples = data.get("intent_examples", [])
entity_examples = data.get("entity_examples", [])
entity_synonyms = data.get("entity_synonyms", [])
regex_features = data.get("regex_features", [])
lookup_tables = data.get("lookup_tables", [])
entity_synonyms = transform_entity_synonyms(entity_synonyms)
if intent_examples or entity_examples:
logger.warning("DEPRECATION warning: your rasa data "
"contains 'intent_examples' "
"or 'entity_examples' which will be "
"removed in the future. Consider "
"putting all your examples "
"into the 'common_examples' section.")
all_examples = common_examples + intent_examples + entity_examples
training_examples = []
for ex in all_examples:
msg = Message.build(ex['text'], ex.get("intent"),
ex.get("entities"))
training_examples.append(msg)
return TrainingData(training_examples, entity_synonyms, regex_features,
lookup_tables)
def create_message(text: str, intent: str, entities: [], training: bool,
corpus: tp.Corpus) -> Message:
""" Helper function to create a message: Message used by Rasa including whether train or test sentence. """
message = Message.build(text, intent, entities)
message.data['training'] = training
message.data['corpus'] = corpus
return message
def _read_intent(self, intent_js, examples_js):
"""Reads the intent and examples from respective jsons."""
intent = intent_js.get("name")
training_examples = []
for ex in examples_js:
text, entities = self._join_text_chunks(ex['data'])
training_examples.append(Message.build(text, intent, entities))
return TrainingData(training_examples)
def _read_intent(self, intent_js, examples_js):
"""Reads the intent and examples from respective jsons."""
intent = intent_js.get("name")
training_examples = []
for ex in examples_js:
text, entities = self._join_text_chunks(ex['data'])
training_examples.append(Message.build(text, intent, entities))
return TrainingData(training_examples)
def convert_line_message(line: List[str]) -> Message:
"""Return message without entities. Good enough for now."""
if len(line) < 2:
raise AssertionError(
'Line should be a list containing at least [sentence, intent], got {}'
.format(line))
message = Message.build(text=convert_annotated_text(line[0]),
intent=line[1],
entities=[])
if len(line) == 3:
message.data['training'] = line[2] == 'True'
return message
def train_update(update, by):
update.start_training(by)
examples = [
Message.build(
text=example.get_text(update.language),
intent=example.intent,
entities=[
example_entity.rasa_nlu_data
for example_entity in example.get_entities(update.language)
]) for example in update.examples
]
label_examples_query = update.examples \
.filter(entities__entity__label__isnull=False) \
.annotate(entities_count=models.Count('entities')) \
.filter(entities_count__gt=0)
label_examples = [
Message.build(
text=example.get_text(update.language),
entities=[
example_entity.get_rasa_nlu_data(label_as_entity=True)
for example_entity in filter(
lambda ee: ee.entity.label,
example.get_entities(update.language))
]) for example in label_examples_query
]
rasa_nlu_config = get_rasa_nlu_config_from_update(update)
trainer = Trainer(rasa_nlu_config, ComponentBuilder(use_cache=False))
training_data = BothubTrainingData(label_training_examples=label_examples,
training_examples=examples)
trainer.train(training_data)
persistor = BothubPersistor(update)
trainer.persist(mkdtemp(),
persistor=persistor,
project_name=str(update.repository.uuid),
fixed_model_name=str(update.id))
