distilbert-base-uncased-lora-financial-sentiment-analysis

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

• Loss: 0.0777
• Accuracy: {'accuracy': 0.9779735682819384}

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: 0.001
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.3107	1.0	510	0.0777	{'accuracy': 0.9779735682819384}
0.117	2.0	1020	0.0853	{'accuracy': 0.9691629955947136}
0.0652	3.0	1530	0.0841	{'accuracy': 0.9779735682819384}

Framework versions

• PEFT 0.13.2
• Transformers 4.46.2
• Pytorch 2.5.1
• Datasets 3.1.0
• Tokenizers 0.20.3