distilbert-base-uncased-finetuned-sentiment

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

• Loss: 0.2871
• Accuracy: 0.8784
• F1: 0.8784
• Precision: 0.8756
• Recall: 0.8874

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: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.4369	1.0	109	0.2982	0.8716	0.8715	0.8609	0.8919
0.2502	2.0	218	0.2871	0.8784	0.8784	0.8756	0.8874

Framework versions

• Transformers 4.16.2
• Pytorch 2.1.0+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2