def create_component(self, component_config: Dict[Text, Any],
cfg: RasaNLUModelConfig) -> Component:
"""Tries to retrieve a component from the cache,
calls `create` to create a new component."""
from rasa.nlu import registry
from rasa.nlu.model import Metadata
try:
component, cache_key = self.__get_cached_component(
component_config, Metadata(cfg.as_dict(), None))
if component is None:
component = registry.create_component_by_config(
component_config, cfg)
self.__add_to_cache(component, cache_key)
return component
except MissingArgumentError as e: # pragma: no cover
raise Exception("Failed to create component `{}`. "
"{}".format(component_config["name"], e))
def test_ltp(text):
message = Message(text)
component_meta = {
"name": "ltp",
"path": "/Users/zhangzhen/data/ltp_data_v3.4.0",
"lexicon": "lexicon",
"dimension": {
"Nh": "PER",
"Ni": "ORG",
"Ns": "LOC"
},
"class": "litemind.nlu.utils.ltp.LtpHelper"
}
model_dir = 'models/coref/model_20190515-150912'
ltp = ComponentBuilder().load_component(component_meta, model_dir,
Metadata({}, None))
ltp.process(message)
pprint.pprint(message.data)
def test_train_nlu(run_in_simple_project: Callable[..., RunResult]):
run_in_simple_project(
"train",
"nlu",
"-c",
"config.yml",
"--nlu",
"data/nlu.md",
"--out",
"train_models",
)
assert os.path.exists("train_models")
files = rasa.shared.utils.io.list_files("train_models")
assert len(files) == 1
assert os.path.basename(files[0]).startswith("nlu-")
model_dir = model.get_model("train_models")
assert model_dir is not None
metadata = Metadata.load(os.path.join(model_dir, "nlu"))
assert metadata.get("training_data") is None
assert not os.path.exists(
os.path.join(model_dir, "nlu",
training_data.DEFAULT_TRAINING_DATA_OUTPUT_PATH))
def test_train_nlu_persist_nlu_data(run_in_default_project):
run_in_default_project(
"train",
"nlu",
"-c",
"config.yml",
"--nlu",
"data/nlu.md",
"--out",
"train_models",
"--persist-nlu-data",
)
assert os.path.exists("train_models")
files = io_utils.list_files("train_models")
assert len(files) == 1
assert os.path.basename(files[0]).startswith("nlu-")
model_dir = model.get_model("train_models")
assert model_dir is not None
metadata = Metadata.load(os.path.join(model_dir, "nlu"))
assert metadata.get("training_data") is not None
assert os.path.exists(
os.path.join(model_dir, "nlu",
training_data.DEFAULT_TRAINING_DATA_OUTPUT_PATH))
def test_builder_load_unknown(component_builder):
with pytest.raises(Exception) as excinfo:
component_meta = {"name": "my_made_up_componment"}
component_builder.load_component(component_meta, "", Metadata({},
None))
assert "Cannot find class" in str(excinfo.value)
def interpreter_for_model(component_builder, model_dir):
metadata = Metadata.load(model_dir)
return Interpreter.create(metadata, component_builder)
def _fallback_model(self):
meta = Metadata({"pipeline": [{
"name": "KeywordIntentClassifier",
"class": utils.module_path_from_object(KeywordIntentClassifier())
}]}, "")
return Interpreter.create(meta, self._component_builder)
"被盗狼青狗特征：是一条1年半的狼青狗，高约60公分，长约80公分，现市场价值2000余元。" \
"涉案总价值2000余元。案件性质关键词：撬门压锁。, 出生地北京，现居住在新疆"
text = "小明，男，身高180cm，上个月去北京站坐G22到新疆，与他同行的有30岁的小黑，他们开着一辆白色法拉利逃跑。"
context = {}
time = datetime.datetime.now()
default_output_attributes = {
"intent": {
"name": None,
"confidence": 0.0
},
"entities": []
}
message = Message(text, data=default_output_attributes, time=time)
model_dir = './models/link/model_20190517-113416'
model_metadata = Metadata.load(model_dir)
jieba_meta = model_metadata.for_component(index=0)
jie = JiebaTokenizer.load(meta=jieba_meta,
model_dir=model_dir,
model_metadata=Metadata.load(model_dir))
pprint.pprint(message.data)
jie.process(message)
ltp_meta = model_metadata.for_component(index=5)
ltp = LtpHelper.load(meta=ltp_meta,
model_dir=model_dir,
model_metadata=Metadata.load(model_dir))
pprint.pprint(message.data)
