Roberta Base Emotion

🚀 robert-base-emotion

This model is based on RoBERTa and fine - tuned on the emotion dataset. It can effectively perform text classification tasks, especially for emotion classification, providing high - accuracy results.

🚀 Quick Start

To use the robert - base - emotion model, you can follow the example below:

from transformers import pipeline
classifier = pipeline("text - classification", model='bhadresh - savani/roberta - base - emotion', return_all_scores=True)
prediction = classifier("I love using transformers. The best part is wide range of support and its easy to use", )
print(prediction)

"""
Output:
[[
{'label': 'sadness', 'score': 0.002281982684507966}, 
{'label': 'joy', 'score': 0.9726489186286926}, 
{'label': 'love', 'score': 0.021365027874708176}, 
{'label': 'anger', 'score': 0.0026395076420158148}, 
{'label': 'fear', 'score': 0.0007162453257478774}, 
{'label': 'surprise', 'score': 0.0003483477921690792}
]]
"""

✨ Features

• Based on RoBERTa: roberta is an improved version of BERT with better hyperparameter choices, robustly optimized during pretraining.
• Fine - tuned on Emotion Dataset: The model is fine - tuned on the emotion dataset using HuggingFace Trainer with specific hyperparameters, enabling it to perform well in emotion classification tasks.

📦 Installation

The original README does not provide specific installation steps, so this section is skipped.

💻 Usage Examples

Basic Usage

from transformers import pipeline
classifier = pipeline("text - classification", model='bhadresh - savani/roberta - base - emotion', return_all_scores=True)
prediction = classifier("I love using transformers. The best part is wide range of support and its easy to use", )
print(prediction)

"""
Output:
[[
{'label': 'sadness', 'score': 0.002281982684507966}, 
{'label': 'joy', 'score': 0.9726489186286926}, 
{'label': 'love', 'score': 0.021365027874708176}, 
{'label': 'anger', 'score': 0.0026395076420158148}, 
{'label': 'fear', 'score': 0.0007162453257478774}, 
{'label': 'surprise', 'score': 0.0003483477921690792}
]]
"""

Advanced Usage

There is no advanced usage example in the original README, so this part is not provided.

📚 Documentation

Model description

roberta is Bert with better hyperparameter choices, so it's said to be Robustly optimized Bert during pretraining. [roberta - base](https://huggingface.co/roberta - base) is finetuned on the emotion dataset using HuggingFace Trainer with the following hyperparameters:

learning rate 2e - 5, 
batch size 64,
num_train_epochs = 8,

Model Performance Comparision on Emotion Dataset from Twitter

Dataset

The model is trained on the Twitter - Sentiment - Analysis dataset.

Training procedure

You can follow the Colab Notebook and change the model name to roberta for training.

Eval results

{
 'test_accuracy': 0.9395,
 'test_f1': 0.9397328860104454,
 'test_loss': 0.14367154240608215,
 'test_runtime': 10.2229,
 'test_samples_per_second': 195.639,
 'test_steps_per_second': 3.13
 }

Reference

• [Natural Language Processing with Transformer By Lewis Tunstall, Leandro von Werra, Thomas Wolf](https://learning.oreilly.com/library/view/natural - language - processing/9781098103231/)

🔧 Technical Details

The original README does not provide detailed technical implementation details, so this section is skipped.

📄 License

The model is licensed under the Apache - 2.0 license.