RoBERTa for Emotion Classification
Model Description
This model is a fine-tuned version of RoBERTaForSequenceClassification trained to classify text into six emotion categories: Sadness, Joy, Love, Anger, Fear, and Surprise.
- RoBERTa
- Special thanks to bhadresh-savani, whose notebook was the main guide for this work.
Intended Use
The model is intended for classifying emotions in text data. It can be used in applications involving sentiment analysis, chatbots, social media monitoring, diary entries.
Limitations
- The model is trained on a specific emotion dataset and may not generalize well to other datasets or domains.
- It might not perform well on text with mixed or ambiguous emotions.
How to use the model
from transformers import pipeline
classifier = pipeline(model="Dimi-G/roberta-base-emotion")
emotions=classifier("i feel very happy and excited since i learned so many things", top_k=None)
print(emotions)
"""
Output:
[{'label': 'Joy', 'score': 0.9991986155509949},
{'label': 'Love', 'score': 0.0003064649645239115},
{'label': 'Sadness', 'score': 0.0001680034474702552},
{'label': 'Anger', 'score': 0.00012623333896044642},
{'label': 'Surprise', 'score': 0.00011396403715480119},
{'label': 'Fear', 'score': 8.671794785186648e-05}]
"""
Training Details
The model was trained on a randomized subset of the dar-ai/emotion dataset from the Hugging Face datasets library. Here are the training parameters:
- Batch size: 64
- Number of epochs: 10
- Learning rate: 5e-5
- Warmup steps: 500
- Weight decay: 0.03
- Evaluation strategy: epoch
- Save strategy: epoch
- Metric for best model: F1 score
Evaluation
{'eval_loss': 0.18195335566997528,
'eval_accuracy': 0.94,
'eval_f1': 0.9396676959491667,
'eval_runtime': 1.1646,
'eval_samples_per_second': 858.685,
'eval_steps_per_second': 13.739,
'epoch': 10.0}
Model Resources
Link to the notebook with details on fine-tuning the model and our approach with other models for emotion classification:
- Repository: Beginners Guide to Emotion Classification
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
Citation
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