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:
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
}