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metadata
language:
  - en
thumbnail: >-
  https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
tags:
  - text-classification
  - emotion
  - pytorch
license: apache-2.0
datasets:
  - emotion
metrics:
  - Accuracy, F1 Score

Albert-base-v2-emotion

Model description:

Albert is A Lite BERT architecture that has significantly fewer parameters than a traditional BERT architecture.

Albert-base-v2 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/albert-base-v2-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': 0.0006792712374590337}, 
{'label': 'joy', 'score': 0.9959300756454468}, 
{'label': 'love', 'score': 0.0009452480007894337}, 
{'label': 'anger', 'score': 0.0018055217806249857}, 
{'label': 'fear', 'score': 0.00041110432357527316}, 
{'label': 'surprise', 'score': 0.0002288572577526793}
]]
"""

Dataset:

Twitter-Sentiment-Analysis.

Training procedure

Colab Notebook

Eval results

{
'test_accuracy': 0.936,
 'test_f1': 0.9365658988006296,
 'test_loss': 0.15278364717960358,
 'test_runtime': 10.9413,
 'test_samples_per_second': 182.794,
 'test_steps_per_second': 2.925
 }

Reference: