VanshajR/roberta-emotion-7class

RoBERTa Emotion Classifier (7-class)

Fine-tuned RoBERTa model for emotion classification on 7 emotions: happy, sad, angry, fear, disgust, surprise, neutral.

Model Details

• Developed by: VanshajR
• Base Model: roberta-base (125M parameters)
• Task: Multi-class emotion classification
• Dataset: GoEmotions (27 emotions mapped to 7)
• Training Samples: ~58,000
• Language: English
• License: MIT

Performance

Evaluated on GoEmotions test set:

Metric	Score
Accuracy	57.77%
Macro F1	0.4787
Precision	0.5289
Recall	0.4958

Per-Class Performance

Emotion	Precision	Recall	F1-Score	Support
Happy	0.62	0.67	0.64	2,362
Sad	0.54	0.51	0.52	1,210
Angry	0.58	0.43	0.49	1,145
Fear	0.42	0.31	0.36	428
Disgust	0.48	0.26	0.34	361
Surprise	0.43	0.43	0.43	623
Neutral	0.64	0.86	0.73	8,711

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("VanshajR/roberta-emotion-7class")
model = AutoModelForSequenceClassification.from_pretrained("VanshajR/roberta-emotion-7class")

# Classify emotion
text = "I'm so excited about this project!"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)

with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predicted_class = torch.argmax(predictions, dim=-1).item()

# Emotion labels
emotions = ["happy", "sad", "angry", "fear", "disgust", "surprise", "neutral"]
print(f"Predicted emotion: {emotions[predicted_class]}")
print(f"Confidence: {predictions[0][predicted_class].item():.2%}")

Training Details

Training Data

• Dataset: GoEmotions (Google Research)
• Emotion Mapping: 27 fine-grained emotions → 7 basic emotions
• Training Samples: ~58,000 Reddit comments
• Preprocessing: Truncation to 128 tokens, lowercase normalization

Training Procedure

• Optimizer: AdamW (lr=2e-5, weight_decay=0.01)
• Batch Size: 16 (train), 32 (eval)
• Epochs: 3
• Max Length: 128 tokens
• Training Regime: fp32

Compute Infrastructure

• Hardware: NVIDIA RTX 3070 (8GB VRAM)
• Training Time: ~2 hours
• Framework: PyTorch 2.1.0, Transformers 4.35.0

Limitations and Bias

• Language: English only
• Domain: Primarily trained on Reddit comments (may not generalize to formal text)
• Class Imbalance: Better performance on frequent emotions (happy, neutral) vs rare emotions (fear, disgust)
• Subjective Task: Human annotators often disagree on emotions (~25-30% disagreement rate)

Intended Use

✅ Recommended:

• Emotion detection in conversational text
• Evaluating emotion-controlled text generation
• Research on emotion understanding in dialogue
• Sentiment analysis applications

❌ Not Recommended:

• Clinical diagnosis or mental health assessment
• High-stakes decision making
• Non-English languages

Citation

@misc{vanshajr2024roberta,
  author = {Vanshaj R},
  title = {RoBERTa Emotion Classifier for 7-Class Emotion Detection},
  year = {2024},
  publisher = {HuggingFace},
  url = {https://huggingface.co/VanshajR/roberta-emotion-7class}
}

Related Work

Part of the Emotion-Controlled Response Generation project:

• 🔗 GitHub Repository
• 🔗 GPT-2 Emotion-Conditioned Model
• 📄 Full Project Report