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import torch |
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from transformers import BertTokenizer |
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from emotion_model import Rosa |
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text = "The sky is golden and I feel serene." |
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emotion_labels = [ |
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"admiration", "amusement", "anger", "annoyance", "approval", "caring", |
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"confusion", "curiosity", "desire", "disappointment", "disapproval", |
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"disgust", "embarrassment", "excitement", "fear", "gratitude", "grief", |
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"joy", "love", "nervousness", "optimism", "pride", "realization", "relief", |
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"remorse", "sadness", "surprise", "neutral" |
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] |
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tokenizer = BertTokenizer.from_pretrained("bert-base-uncased") |
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model = Rosa(num_emotions=29, latent_dim=64, return_vector=True, emotion_labels=emotion_labels) |
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model.load_state_dict(torch.load("rosa.pt", map_location=torch.device("cpu"))) |
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model.eval() |
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inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True) |
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with torch.no_grad(): |
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result = model(**inputs) |
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embedding = result["embedding"].squeeze().tolist() |
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print("ROSA Emotion Embedding Vector:") |
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print(embedding) |
