Hi, im using rasa 3 and i’m trying to implement a custom graph component for sentiment analysis. I’m using a pretrained model with vadersentiment. My sentiment.py code : from __future__ import annotations from rasa.engine.graph import GraphComponent, ExecutionContext from rasa.shared.nlu.training_data.training_data import TrainingData from rasa.engine.recipes.default_recipe import DefaultV1Recipe from typing import List, Type, Dict, Text, Any, Optional from rasa.engine.graph import ExecutionContext from rasa.engine.storage.resource import Resource from rasa.engine.storage.storage import ModelStorage from rasa.shared.nlu.training_data.message import Message from rasa.shared.nlu.constants import TEXT from rasa.nlu.extractors.extractor import EntityExtractorMixin from nltk.sentiment.vader import SentimentIntensityAnalyzer @DefaultV1Recipe.register( DefaultV1Recipe.ComponentType.ENTITY_EXTRACTOR, is_trainable=False ) class SentimentAnalyzer(GraphComponent, EntityExtractorMixin): """A pre-trained sentiment component""" name = "sentiment" provides = ["entities"] requires = [] defaults = {} language_list = ["en"] def __init__(self, component_config: Dict[Text, Any]) -> None: self.component_config = component_config @classmethod def create( cls, config: Dict[Text, Any], model_storage: ModelStorage, resource: Resource, execution_context: ExecutionContext, ) -> GraphComponent: return cls(config) def train(self, training_data: TrainingData) -> Resource: pass def convert_to_rasa(self, value, confidence): """Convert model output into the Rasa NLU compatible output format.""" entity = {"value": value, "confidence": confidence, "entity": "sentiment", "extractor": "sentiment_extractor"} return entity def process(self, messages: List[Message]) -> List[Message]: """Retrieve the text message, pass it to the classifier and append the prediction results to the message class.""" sid = SentimentIntensityAnalyzer() for message in messages: res = sid.polarity_scores(message.get(TEXT)) 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) return messages def persist(self, file_name, model_dir): """Pass because a pre-trained model is already persisted""" pass I have this errors : 2022-01-14 15:05:34 ERROR rasa.server - An unexpected error occurred. Error: Error running graph component for node run_sentiment.SentimentAnalyzer9. 2022-01-14 15:05:34 ERROR rasa.core.training.interactive - An exception occurred while recording messages. Traceback (most recent call last): File "c:\users\hp\pycharmprojects\newpr\venv\li…

Custom graph component for sentiment analysis

Fares (Ben Ayed) January 14, 2022, 9:17pm 7

I’d recommend to use rasa 2.8 and try this Pretrained sentiment component nltk and if you have any error please let me know

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