finetuning-sentiment-model-unbalanced-dataset

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

• Loss: 0.2679
• Accuracy: 0.9543
• Weighted f1: 0.9541
• Micro f1: 0.9543
• Macro f1: 0.9398
• Weighted recall: 0.9543
• Micro recall: 0.9543
• Macro recall: 0.9369
• Weighted precision: 0.9541
• Micro precision: 0.9543
• Macro precision: 0.9428

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: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5

Training results

Framework versions

• Transformers 4.37.2
• Pytorch 2.1.0+cu121
• Datasets 2.17.1
• Tokenizers 0.15.2