examples/run_embedding.py

# run_embedding.py
# Get latent emotion vector from ROSA

import torch
from transformers import BertTokenizer
from emotion_model import Rosa

text = "The sky is golden and I feel serene."

# Define emotions and model
emotion_labels = [
    "admiration", "amusement", "anger", "annoyance", "approval", "caring",
    "confusion", "curiosity", "desire", "disappointment", "disapproval",
    "disgust", "embarrassment", "excitement", "fear", "gratitude", "grief",
    "joy", "love", "nervousness", "optimism", "pride", "realization", "relief",
    "remorse", "sadness", "surprise", "neutral"
]

tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
model = Rosa(num_emotions=29, latent_dim=64, return_vector=True, emotion_labels=emotion_labels)
model.load_state_dict(torch.load("rosa.pt", map_location=torch.device("cpu")))
model.eval()

inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
with torch.no_grad():
    result = model(**inputs)
    embedding = result["embedding"].squeeze().tolist()

print("ROSA Emotion Embedding Vector:")
print(embedding)