def train(self, training_data, mitie_file, num_threads):
# type: (TrainingData, Text, Optional[int]) -> None
import mitie
trainer = mitie.text_categorizer_trainer(mitie_file)
trainer.num_threads = num_threads
for example in training_data.intent_examples:
tokens = mitie.tokenize(example["text"])
trainer.add_labeled_text(tokens, example["intent"])
if training_data.intent_examples:
# we can not call train if there are no examples!
self.clf = trainer.train()
def train(self, training_data, config, **kwargs):
# type: (TrainingData, RasaNLUConfig, **Any) -> None
import mitie
trainer = mitie.text_categorizer_trainer(config["mitie_file"])
trainer.num_threads = config["num_threads"]
for example in training_data.intent_examples:
tokens = self._tokens_of_message(example)
trainer.add_labeled_text(tokens, example.get("intent"))
if training_data.intent_examples:
# we can not call train if there are no examples!
self.clf = trainer.train()
def train(self, training_data, cfg, **kwargs):
# type: (TrainingData, RasaNLUModelConfig, **Any) -> None
import mitie
model_file = kwargs.get("mitie_file")
if not model_file:
raise Exception("Can not run MITIE entity extractor without a "
"language model. Make sure this component is "
"preceeded by the 'nlp_mitie' component.")
trainer = mitie.text_categorizer_trainer(model_file)
trainer.num_threads = kwargs.get("num_threads", 1)
for example in training_data.intent_examples:
tokens = self._tokens_of_message(example)
trainer.add_labeled_text(tokens, example.get("intent"))
if training_data.intent_examples:
# we can not call train if there are no examples!
self.clf = trainer.train()
def train(self, training_data: TrainingData, cfg: RasaNLUModelConfig,
**kwargs: Any) -> None:
import mitie
model_file = kwargs.get("mitie_file")
if not model_file:
raise Exception("Can not run MITIE entity extractor without a "
"language model. Make sure this component is "
"preceeded by the 'MitieNLP' component.")
trainer = mitie.text_categorizer_trainer(model_file)
trainer.num_threads = kwargs.get("num_threads", 1)
for example in training_data.intent_examples:
tokens = self._tokens_of_message(example)
trainer.add_labeled_text(tokens, example.get("intent"))
if training_data.intent_examples:
# we can not call train if there are no examples!
self.clf = trainer.train()