bert-base-uncased-finetuned-emotion

This model is a fine-tuned version of bert-base-uncased on the emotion dataset. It achieves the following results on the evaluation set:

• Loss: 0.1651
• Accuracy: {'accuracy': 0.9355}
• F1: {'f1': 0.935388774713548}

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: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.2519	1.0	1000	0.1878	{'accuracy': 0.9325}	{'f1': 0.9323540471733189}
0.1434	2.0	2000	0.1799	{'accuracy': 0.9335}	{'f1': 0.9341179573678701}
0.0907	3.0	3000	0.1651	{'accuracy': 0.9355}	{'f1': 0.935388774713548}

Framework versions

• Transformers 4.33.1
• Pytorch 2.0.1+cu118
• Datasets 2.13.1
• Tokenizers 0.13.3