def get_emotion_embeddings(input_file):
model = EmotionPredictor(classification='plutchik',
setting='mc',
use_unison_model=True)
f = open(input_file)
tweets = f.read().splitlines()
embeddings = model.embedd(tweets)
return embeddings
import argparse
from emotion_predictor import EmotionPredictor
parser = argparse.ArgumentParser()
parser.add_argument('-tweets')
args = parser.parse_args()
tweets = args.tweets
model = EmotionPredictor(classification='ekman', setting='mc', use_unison_model=True)
predictions = model.predict_classes(tweets)
print(predictions, '\n')
probabilities = model.predict_probabilities(tweets)
print(probabilities, '\n')
embeddings = model.embedd(tweets)
print(embeddings, '\n')