Edit model card

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:

Downloads last month
440
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for bhadresh-savani/electra-base-emotion

Finetunes
15 models

Dataset used to train bhadresh-savani/electra-base-emotion

Evaluation results