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Model Description

Bert is a Transformer Bidirectional Encoder based Architecture trained on MLM(Mask Language Modeling) objective

bert-base-uncased-emotion-fituned finetuned on the emotion dataset using HuggingFace Trainer with below training parameters

    num_train_epochs=8,              
    train_batch_size=32,  
    eval_batch_size=64,   
    warmup_steps=500,                
    weight_decay=0.01

Dataset

emotion

Model Performance Comparision on Emotion Dataset

Model Accuracy Recall F1 Score
Bert-base-uncased-emotion (SOTA) 92.6 87.9 88.2
Bert-base-uncased-emotion-fintuned 92.9 88 88.5

How to Use the Model:

from transformers import pipeline
classifier = pipeline("text-classification",model='sonia12138/bert-base-uncased-emotion-fituned', 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)

Model Sources

Eval Results

{
  'eval_accuracy': 0.929,
  'eval_f1': 0.9405920712282673,
  'eval_loss': 0.15769127011299133,
  'eval_loss': 0.37796708941459656,
  "eval_runtime': 8.0514,
  'eval_samples_per_second': 248.403,
  'eval_steps_per_second': 3.974,
 }

Compute Infrastructure

Hardware

NVIDIA GeForce RTX 4090

Software

22.04.1-Ubuntu

Model Card Authors

Xiaohan Wang, Kun Peng

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Dataset used to train sonia12138/bert-base-uncased-emotion-fituned