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electra-base-emotion

Text Classification
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electra
emotion
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electra-base-emotion / README.md
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metadata
language:
  - en
license: apache-2.0
tags:
  - text-classification
  - emotion
  - pytorch
datasets:
  - emotion
metrics:
  - Accuracy, F1 Score
thumbnail: >-
  https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
model-index:
  - name: bhadresh-savani/electra-base-emotion
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: emotion
          type: emotion
          config: default
          split: test
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Electra-base-emotion

Model description:

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
Electra-base-emotion 91.95 91.90 472.72

How to Use the model: 

from transformers import pipeline
classifier = pipeline("text-classification",model='bhadresh-savani/electra-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': 0.0006792712374590337}, 
{'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 

{
 'epoch': 8.0,
 'eval_accuracy': 0.9195,
 'eval_f1': 0.918975455617076,
 'eval_loss': 0.3486028015613556,
 'eval_runtime': 4.2308,
 'eval_samples_per_second': 472.726,
 'eval_steps_per_second': 7.564
 }

Reference: