async def predict(
self, sources: SourcesContext
) -> AsyncIterator[Tuple[Record, Any, float]]:
if not self.is_trained:
raise ModelNotTrained("Train model before prediction.")
async for record in sources.records():
doc = self.parent.nlp(record.feature("sentence"))
prediction = [(ent.text, ent.label_) for ent in doc.ents]
record.predicted("Tag", prediction, "Nan")
yield record
async def predict(
self, sources: SourcesContext
) -> AsyncIterator[Tuple[Record, Any, float]]:
if not os.path.isdir(os.path.join(self.parent.config.output_dir,
"ner")):
raise ModelNotTrained("Train model before prediction.")
self.nlp = spacy.load(self.parent.config.output_dir)
async for record in sources.records():
doc = self.nlp(record.feature("sentence"))
prediction = [(ent.text, ent.label_) for ent in doc.ents]
record.predicted("Tag", prediction, "Nan")
yield record
async def score(self, mctx: ModelContext, sources: SourcesContext,
feature: Feature):
accuracy: int = 0
async for record in sources.records():
accuracy += int(record.key)
return accuracy
