robert-base-emotion
Model description:
roberta is Bert with better hyperparameter choices so they said it's Robustly optimized Bert during pretraining.
roberta-base finetuned on the emotion dataset using HuggingFace Trainer with below Hyperparameters
learning rate 2e-5,
batch size 64,
num_train_epochs=8,
Model Performance Comparision on Emotion Dataset from Twitter:
Model | Accuracy | F1 Score | Test Sample per Second |
---|---|---|---|
Distilbert-base-uncased-emotion | 93.8 | 93.79 | 398.69 |
Bert-base-uncased-emotion | 94.05 | 94.06 | 190.152 |
Roberta-base-emotion | 93.95 | 93.97 | 195.639 |
Albert-base-v2-emotion | 93.6 | 93.65 | 182.794 |
How to Use the model:
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}
]]
"""
Dataset:
Training procedure
Colab Notebook follow the above notebook by changing the model name to roberta
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:
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Dataset used to train bhadresh-savani/roberta-base-emotion
Space using bhadresh-savani/roberta-base-emotion 1
Evaluation results
- Accuracy on emotiontest set verified0.931
- Precision Macro on emotiontest set verified0.917
- Precision Micro on emotiontest set verified0.931
- Precision Weighted on emotiontest set verified0.936
- Recall Macro on emotiontest set verified0.874
- Recall Micro on emotiontest set verified0.931
- Recall Weighted on emotiontest set verified0.931
- F1 Macro on emotiontest set verified0.882
- F1 Micro on emotiontest set verified0.931
- F1 Weighted on emotiontest set verified0.930