You can deploy this model and start earning money today!

CURI-AI/
sentiment-analysis-en-v1

Name: sentiment-analysis-en-v1
Availability: InStock

multimodal_emotion_analyzer

Views

No views yet

Multimodal Emotion Analyzer for 26 emotions

Model Description

This is a multimodal emotion analysis model that processes both audio and text inputs to predict 26 different emotions with regression scores (0-1).

Architecture

Text Encoder: RoBERTa-base
Audio Encoder: CNN-based encoder with 3 convolutional layers
Fusion: MLP layers with ReLU activation and Dropout
Output: 26 emotion regression scores (0-1 range)

Emotion Labels

admiration, aesthetic appreciation, awe, anxiety, fear, horror, disgust, calmness, romantic love, sexual desire, nostalgia, interest, surprise, excitement, anger, pride, triumph, contempt, disappointment, empathic pain, sadness, guilt, envy, amusement, awkwardness, adoration

Model Files

best_model.pth: Trained model weights
config.json: Model configuration
training_history.png: Training progress visualization

Usage Example

python

import torch
from transformers import AutoTokenizer
from models.multimodal_emotion_all import MultimodalEmotionAnalyzerAll

# Load model
model = MultimodalEmotionAnalyzerAll(num_emotions=26)
checkpoint = torch.load('best_model.pth', map_location='cpu')
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("roberta-base")

# Process text
text = "I am feeling happy today"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)

# Get predictions
with torch.no_grad():
    # Note: This model expects both text and audio inputs
    # For text-only inference, you may need to modify the forward pass
    outputs = model(**inputs)
    emotion_scores = torch.sigmoid(outputs)

Training Details

Training Date: 2025-08-26
Number of Emotions: 26
Model Type: Multimodal (Text + Audio)
Base Model: RoBERTa-base
Loss Function: MSE Loss
Optimizer: AdamW

Performance

Model Size: 1432.7 MB
Parameters: 125,172,565

Citation

If you use this model in your research, please cite:

@misc{emotion_analyzer_2024, title={Multimodal Emotion Analyzer}, author={Your Name}, year={2024}, url={https://huggingface.co/CURI-AI/sentiment-analysis-en-v1} }