ckandemir / bert-base-uncased-amazon-reviews-sentiment-analysis

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Model's Last Updated: July 15 2024
text-classification

Introduction of bert-base-uncased-amazon-reviews-sentiment-analysis

Model Details of bert-base-uncased-amazon-reviews-sentiment-analysis

bert-base-uncased-amazon-reviews-sentiment-analysis

This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2344
  • Accuracy: 0.9198
  • F1: 0.9215
  • Precision: 0.9263
  • Recall: 0.9167
Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure
Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 16
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 3
Training results
Training Loss Epoch Step Validation Loss Accuracy F1 Precision Recall
0.6741 1.0 47 0.6232 0.7594 0.7097 0.9322 0.5729
0.4346 2.0 94 0.3871 0.8717 0.875 0.875 0.875
0.3593 3.0 141 0.2344 0.9198 0.9215 0.9263 0.9167
Framework versions
  • Transformers 4.41.2
  • Pytorch 2.3.0+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1

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