Hi,
I aim to develop a custom sentiment classifier based on the DIET system. You may think I should use just DIET as an intent and entity classifier, but that is not what I am looking for. Please take a look at the sentiment classifier below:
from rasa.nlu.components import Component
from rasa.nlu import utils
from rasa.nlu.model import Metadata
from nltk.sentiment.vader import SentimentIntensityAnalyzer
import os
import nltk
nltk.download('vader_lexicon')
class SentimentAnalyzer(Component):
"""A pre-trained sentiment component"""
name = "custom_component.SentimentAnalyzer"
provides = ["entities"]
requires = []
defaults = {}
language_list = ["en"]
def __init__(self, component_config=None):
super(SentimentAnalyzer, self).__init__(component_config)
def train(self, training_data, cfg, **kwargs):
"""Not needed, because the the model is pretrained"""
pass
def convert_to_rasa(self, value, confidence):
"""Convert model output into the Rasa NLU compatible output format."""
entity = {"value": value,
"confidence": confidence,
"entity": value,
"extractor": "sentiment_extractor"}
return entity
def process(self, message, **kwargs):
"""Retrieve the text message, pass it to the classifier
and append the prediction results to the message class."""
sid = SentimentIntensityAnalyzer()
data = ""
try:
data = message.data['text']
except KeyError:
pass
res = sid.polarity_scores(data)
key, value = max(res.items(), key=lambda x: x[1])
entity = self.convert_to_rasa(key, value)
message.set("entities", [entity], add_to_output=True)
def persist(self, file_name, model_dir):
"""Pass because a pre-trained model is already persisted"""
pass
As you can see, we can make a custom classifier based on NLTK which results in sentiment entities for all intents even including nlu_fallback.
entity = {"value": value,
"confidence": confidence,
"entity": value, // sentiment entity for all intents including nlu_fallback
"extractor": "sentiment_extractor"}
In this respect, I want to develop multinomial emotion classifiers, if possible, by using the DIET classifier. Then, the config would be like:
pipeline:
- name: WhitespaceTokenizer
- name: RegexFeaturizer
- name: CountVectorsFeaturizer
- name: CountVectorsFeaturizer
analyzer: char_wb
min_ngram: 1
max_ngram: 4
- name: custom_component.SentimentAnalyzer // based on DIET
- name: DIETClassifier
epochs: 100
constrain_similarities: true
- name: EntitySynonymMapper
constrain_similarities: true
- name: FallbackClassifier
threshold: 0.6
ambiguity_threshold: 0.1
Is there any way to use it for a custom component?