Hi , I want to integrate the rasa nlu with custom NER like BERT, Can anyone suggest how to do it.there are no tutorial regarding custom pipeline, So far i seen little bit information in rasa nlu documentation.So please help me out.
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This is an interesting idea. You have to create the BERT model (or use anything existing) and make it part of the pipeline.
Does this link help? https://rasa.com/docs/nlu/0.13.8/customcomponents/
An interesting blog of someone adding a new model with Rasa is here https://scalableminds.com/blog/MachineLearning/2018/08/rasa-universal-sentence-encoder/
Hope this is helpful.
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Not that I know off…
None of the links you provided work or contain any relevant information on custom pipelines. Is Rasa python coding out of the window now? How can one simply create a small pipeline, train it on data from within python code (no shell) and test the model on a single input?
I’m trying the following code snippet from the old versions and it does not work… none of the libraries exist anymore:
from rasa_nlu.training_data import load_data
from rasa_nlu.config import RasaNLUModelConfig
from rasa_nlu.model import Trainer
from rasa_nlu import config
from rasa_nlu.model import Metadata, Interpreter
training_data = load_data('data/examples/rasa/demo-rasa.json')
trainer = Trainer(config.load("sample_configs/config_spacy.yml"))
trainer.train(training_data)
model_directory = trainer.persist('./projects/default/') # Returns the directory the model is stored in
# where `model_directory points to the folder the model is persisted in
interpreter = Interpreter.load(model_directory)
print(interpreter.parse(u"The text I want to understand"))