bert-base-uncased-emotion

Model description:

Bert is a Transformer Bidirectional Encoder based Architecture trained on MLM(Mask Language Modeling) objective

bert-base-uncased finetuned on the emotion dataset using HuggingFace Trainer with below training parameters

 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/bert-base-uncased-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': 0.0005138228880241513}, 
{'label': 'joy', 'score': 0.9972520470619202}, 
{'label': 'love', 'score': 0.0007443308713845909}, 
{'label': 'anger', 'score': 0.0007404946954920888}, 
{'label': 'fear', 'score': 0.00032938539516180754}, 
{'label': 'surprise', 'score': 0.0004197491507511586}
]]
"""

Dataset:

Twitter-Sentiment-Analysis.

Training procedure

Colab Notebook follow the above notebook by changing the model name from distilbert to bert

Eval results

{
 'test_accuracy': 0.9405,
 'test_f1': 0.9405920712282673,
 'test_loss': 0.15769127011299133,
 'test_runtime': 10.5179,
 'test_samples_per_second': 190.152,
 'test_steps_per_second': 3.042
 }

Reference:

• Natural Language Processing with Transformer By Lewis Tunstall, Leandro von Werra, Thomas Wolf