This is a RoBERTa-based model fine-tuned on the Empathetic Dialogues dataset for conversational emotion classification. The model leverages the powerful RoBERTa architecture to understand and classify emotional contexts in conversational text.

Emotion Classes

The model is trained to classify conversations into the following emotional categories:

Surprised
Angry
Sad
Joyful
Anxious
Hopeful
Confident
Disappointed

Model Details

Base Model: roberta-base
Task: Emotion Classification in Conversations
Dataset: Empathetic Dialogues
Training Approach: Full Fine-Tuning
Number of Emotion Classes: 8

Model Performance

Metric	Score
Test Loss	0.8107
Test Accuracy	73.01%
Test F1 Score	72.96%
Runtime	10.99 seconds
Samples per Second	61.68
Steps per Second	1.001

Usage

Hugging Face Transformers Pipeline

python

from transformers import pipeline

# Initialize the emotion classification pipeline
classifier = pipeline(
    "text-classification", 
    model="Sidharthan/roberta-base-conv-emotion"
)

# Classify emotion in a conversation
text = "I'm feeling really frustrated with work lately."
result = classifier(text)
print(result)

Direct Model Loading

python

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

# Load the model and tokenizer
model_name = "Sidharthan/roberta-base-conv-emotion"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare input
text = "I'm feeling really frustrated with work lately."
inputs = tokenizer(text, return_tensors="pt")

# Predict
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.softmax(outputs.logits, dim=1)
    predicted_class = torch.argmax(predictions, dim=1)

Limitations

Performance may vary with out-of-domain conversational contexts
Emotion classification limited to the 8 specified emotional categories
Relies on the specific emotional nuances in the Empathetic Dialogues dataset
Requires careful interpretation in real-world applications

Ethical Considerations

Emotion classification can be subjective
Potential for bias based on training data
Should not be used for making critical decisions about individuals

License

Apache 2.0

Citations

bibtex

@misc{roberta-base-conv-emotion,
  title={RoBERTa Fine-Tuned on Empathetic Dialogues},
  author={Sidharthan},
  year={2024},
  publisher={Hugging Face}
}

Contact

For more information, please contact the model's author.