roberta-large-goemotions

🧠 RoBERTa-Large GoEmotions (Optimized)

Multi-label Emotion Classification with Focal Loss + Per-label Threshold Optimization

🏷️ Overview

This model fine-tunes RoBERTa-Large on the GoEmotions dataset for multi-label emotion classification, detecting 28 distinct emotions (plus neutral) in text.
Unlike standard models that use fixed thresholds or BCE loss, this version applies focal loss, per-label optimization, and targeted augmentation, leading to a balanced and generalizable model across all emotions.

Each input sentence can evoke multiple emotions simultaneously — for example:

"I can't believe this happened!" → surprise, disappointment

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📚 Dataset

GoEmotions (Google Research, 2021)
• ~58k Reddit comments
• 28 emotion labels + neutral
• Multi-label: each text can have multiple active emotions
• Highly imbalanced: e.g., gratitude has >1,000 examples, while grief or relief have <20

This model addresses imbalance through loss design, augmentation, and threshold tuning.

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🚀 Quick Start

Installation

pip install torch transformers huggingface_hub numpy

Basic Usage (3 lines!)

import torch
import torch.nn as nn
from transformers import RobertaTokenizer, RobertaModel
from huggingface_hub import hf_hub_download
import json
import numpy as np
# Step 1: Define the model architecture
class RobertaForMultiLabelClassification(nn.Module):
    def __init__(self, model_name, num_labels, dropout_rate=0.3, use_mean_pooling=True):
        super().__init__()
        self.roberta = RobertaModel.from_pretrained(model_name)
        self.use_mean_pooling = use_mean_pooling
        hidden_size = self.roberta.config.hidden_size
        self.dropout1 = nn.Dropout(dropout_rate)
        self.fc1 = nn.Linear(hidden_size, hidden_size // 2)
        self.relu = nn.ReLU()
        self.dropout2 = nn.Dropout(dropout_rate)
        self.fc2 = nn.Linear(hidden_size // 2, num_labels)
    def mean_pooling(self, token_embeddings, attention_mask):
        input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
        sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1)
        sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
        return sum_embeddings / sum_mask
    def forward(self, input_ids, attention_mask):
        outputs = self.roberta(input_ids, attention_mask=attention_mask)
        if self.use_mean_pooling:
            pooled_output = self.mean_pooling(outputs.last_hidden_state, attention_mask)
        else:
            pooled_output = outputs.pooler_output
        x = self.dropout1(pooled_output)
        x = self.fc1(x)
        x = self.relu(x)
        x = self.dropout2(x)
        logits = self.fc2(x)
        return logits
# Step 2: Load model
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model_name = "Lakssssshya/roberta-large-goemotions"
tokenizer = RobertaTokenizer.from_pretrained(model_name)
# Load config
config_path = hf_hub_download(repo_id=model_name, filename="config.json")
with open(config_path, 'r') as f:
    config = json.load(f)
model = RobertaForMultiLabelClassification(
    model_name='roberta-large',
    num_labels=config['num_labels'],
    dropout_rate=config.get('dropout_rate', 0.3),
    use_mean_pooling=config.get('use_mean_pooling', True)
)
# Load weights
weights_path = hf_hub_download(repo_id=model_name, filename="pytorch_model.bin")
state_dict = torch.load(weights_path, map_location=device)
model.load_state_dict(state_dict)
model.to(device)
model.eval()
# Load thresholds
thresholds_path = hf_hub_download(repo_id=model_name, filename="optimal_thresholds.json")
with open(thresholds_path, 'r') as f:
    thresholds = np.array(json.load(f))
# Emotion labels
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'
]
# Step 3: Predict
def predict_emotions(text):
    inputs = tokenizer(text, return_tensors='pt', truncation=True, max_length=128, padding=True)
    inputs = {k: v.to(device) for k, v in inputs.items()}
    
    with torch.no_grad():
        logits = model(input_ids=inputs['input_ids'], attention_mask=inputs['attention_mask'])
        probs = torch.sigmoid(logits).cpu().numpy()[0]
    
    # Apply optimized thresholds
    predictions = (probs > thresholds).astype(int)
    
    # Get predicted emotions
    predicted_emotions = [emotion_labels[i] for i in range(len(predictions)) if predictions[i] == 1]
    
    # Get top emotions with scores
    top_indices = np.argsort(probs)[::-1][:5]
    top_emotions = [(emotion_labels[idx], float(probs[idx])) for idx in top_indices]
    
    return {
        'predicted_emotions': predicted_emotions,
        'top_emotions': top_emotions
    }
# Example usage
text = "I'm so proud and excited about this achievement!"
result = predict_emotions(text)
print(f"Text: {text}")
print(f"Predicted emotions: {result['predicted_emotions']}")
print(f"Top 5 emotions: {result['top_emotions']}")

Easy-to-Use Wrapper Class

For convenience, use this wrapper class:

class EmotionPredictor:
    def __init__(self, model_name="Lakssssshya/roberta-large-goemotions"):
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        self.tokenizer = RobertaTokenizer.from_pretrained(model_name)
        
        # Load config and model
        config_path = hf_hub_download(repo_id=model_name, filename="config.json")
        with open(config_path, 'r') as f:
            config = json.load(f)
        
        self.model = RobertaForMultiLabelClassification(
            model_name='roberta-large',
            num_labels=config['num_labels'],
            dropout_rate=config.get('dropout_rate', 0.3),
            use_mean_pooling=config.get('use_mean_pooling', True)
        )
        
        weights_path = hf_hub_download(repo_id=model_name, filename="pytorch_model.bin")
        state_dict = torch.load(weights_path, map_location=self.device)
        self.model.load_state_dict(state_dict)
        self.model.to(self.device)
        self.model.eval()
        
        # Load thresholds
        thresholds_path = hf_hub_download(repo_id=model_name, filename="optimal_thresholds.json")
        with open(thresholds_path, 'r') as f:
            self.thresholds = np.array(json.load(f))
        
        self.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'
        ]
    
    def predict(self, text, top_k=5):
        inputs = self.tokenizer(text, return_tensors='pt', truncation=True, max_length=128, padding=True)
        inputs = {k: v.to(self.device) for k, v in inputs.items()}
        
        with torch.no_grad():
            logits = self.model(input_ids=inputs['input_ids'], attention_mask=inputs['attention_mask'])
            probs = torch.sigmoid(logits).cpu().numpy()[0]
        
        predictions = (probs > self.thresholds).astype(int)
        predicted_emotions = [self.emotion_labels[i] for i in range(len(predictions)) if predictions[i] == 1]
        
        top_indices = np.argsort(probs)[::-1][:top_k]
        top_emotions = [
            {'emotion': self.emotion_labels[idx], 'score': float(probs[idx])}
            for idx in top_indices
        ]
        
        return {'text': text, 'emotions': predicted_emotions, 'top_emotions': top_emotions}
# Simple usage
predictor = EmotionPredictor()
result = predictor.predict("I'm so happy and excited!")
print(result)

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📝 Example Predictions

Example 1: Pride and Achievement

text = "I'm so proud and excited about this achievement!"
result = predictor.predict(text)
# Output: {'emotions': ['pride', 'excitement', 'joy'], 'top_emotions': [{'emotion': 'pride', 'score': 0.867}, {'emotion': 'excitement', 'score': 0.712}, ...]}

Example 2: Regret

text = "I really regret saying that earlier."
result = predictor.predict(text)
# Output: {'emotions': ['remorse', 'sadness'], 'top_emotions': [{'emotion': 'remorse', 'score': 0.758}, ...]}

Example 3: Mixed Emotions

text = "I feel anxious but hopeful about the future."
result = predictor.predict(text)
# Output: {'emotions': ['nervousness', 'optimism'], 'top_emotions': [{'emotion': 'nervousness', 'score': 0.710}, {'emotion': 'optimism', 'score': 0.645}, ...]}

Example 4: Surprise

text = "I can't believe this happened!"
result = predictor.predict(text)
# Output: {'emotions': ['surprise', 'realization'], 'top_emotions': [{'emotion': 'surprise', 'score': 0.747}, ...]}

Example 5: Gratitude

text = "Thank you so much for all your help and support!"
result = predictor.predict(text)
# Output: {'emotions': ['gratitude', 'admiration'], 'top_emotions': [{'emotion': 'gratitude', 'score': 0.923}, ...]}

Example 6: Multiple Strong Emotions

text = "This is absolutely disgusting and infuriating!"
result = predictor.predict(text)
# Output: {'emotions': ['disgust', 'anger', 'annoyance'], 'top_emotions': [{'emotion': 'disgust', 'score': 0.834}, {'emotion': 'anger', 'score': 0.789}, ...]}

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⚙️ Training Details

Thresholds are optimized individually to maximize per-label F1 and saved in optimal_thresholds.json.

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📊 Evaluation Metrics (Test Split)

This model achieves a balanced macro-F1 above 0.52, maintaining strong recall even for underrepresented emotions.

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🔍 Per-Label Performance (Best performance on Val Split)

🔍 Per-Label Performance (Test Split)

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🥇 Why This Model Outperforms Other GoEmotions Models

Most GoEmotions models on Hugging Face use BCE loss and a fixed 0.5 threshold.
While effective for frequent emotions, they perform poorly on rare ones.

This model overcomes those limits via:

1️⃣ Adaptive per-label thresholds — Each label has a unique optimized decision boundary, maximizing per-label F1 and balancing recall/precision.
2️⃣ Focal Loss — Down-weights easy examples and boosts hard ones, enhancing minority class generalization.
3️⃣ Mean Pooling — Captures richer emotional nuance than CLS-based pooling.
4️⃣ Targeted Augmentation — Paraphrasing and synonym replacement to strengthen rare emotion classes.
5️⃣ Gradual Unfreezing — Stabilizes fine-tuning by freezing encoder early epochs.
6️⃣ Balanced Macro Metrics — Consistent fairness across all emotion classes.

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🧾 Model Details

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🎯 Intended Use

• Emotion detection in conversational AI systems
• Social media sentiment analysis
• Affective computing research
• Psychological and HCI studies

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⚠️ Limitations

• Trained on Reddit; may not generalize to formal or non-English domains.
• Rare emotions (e.g. grief, relief) have low support.
• Detects linguistic cues, not true emotional state.

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📎 Citation

@misc{lakshya2025robertalargegoemotions,
  title={RoBERTa-Large GoEmotions (Optimized Thresholds and Focal Loss)},
  author={Lakshya Kumar},
  year={2025},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/Lakssssshya/roberta-large-goemotions}}
}

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🙏 Acknowledgments

• Google Research for the GoEmotions dataset
• Hugging Face for the transformers library
• Meta AI for RoBERTa architecture

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📧 Contact

For questions or collaborations:

• Hugging Face: @Lakssssshya
• Model Repository: roberta-large-goemotions

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