Rasa version : 0.1.1
Python version : 3.7
Operating system (windows, osx, …): osx
I am making an application that can pick entities from a grocery list. For the first version I am trying only with a couple of products/brands.
My training data has been generated using chatito and these are the query shapes:
%[inventory_count]('training': '0.99', 'testing': '') order @[count] @[units?] of @[brand?] @[product] @[count] @[units?] @[product] by @[brand] @[brand] @[product] is @[count] @[units?] add @[brand] @[product] @[count] @[units?] @[brand] @[product] @[count] @[units?] on floor
The product list is something like this:
spinach 5 oz spinach 5 ounces spinach 16 oz spinach 16 ounces spinach spinach sixteen ounces spinach five ounces gala apples gala apple apples apple
The trained model is able to detect all the products in the list above but it is also detecting arbitrary products like this (there can be potential errors in the text and I wanted to test for the negative case):
spinach 50 oz call apples
Is this overfitting? What are the possible solutions?
Content of configuration file (config.yml) :
language: "en" pipeline: - name: "tokenizer_whitespace" - name: "ner_crf" - name: "intent_featurizer_count_vectors" - name: "intent_classifier_tensorflow_embedding" droprate: 0.5 epochs: 300 C2: 0.02