README.md

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

bert-base-uncased-emotion

Model description:

bert-base-uncased finetuned on the emotion dataset using HuggingFace Trainer.

 learning rate 2e-5, 
 batch size 64,
 num_train_epochs=8,

How to Use the model:

from transformers import pipeline
classifier = pipeline("sentiment-analysis",model='bhadresh-savani/bert-base-uncased-emotion')
prediction = classifier("I love using transformers. The best part is wide range of support and its easy to use")

Dataset:

Twitter-Sentiment-Analysis.

Training procedure

Colab Notebook

Eval results

{
 'test_accuracy': 0.9355,
 'test_f1': 0.9354074792391709,
 'test_loss': 0.18557891249656677,
 'test_runtime': 11.0092,
 'test_samples_per_second': 181.666,
 'test_steps_per_second': 2.907
 }