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: 0.0637
• 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: 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
0.3143	1.0	510	0.0637	{'accuracy': 0.9779735682819384}
0.1253	2.0	1020	0.0722	{'accuracy': 0.973568281938326}
0.0574	3.0	1530	0.0806	{'accuracy': 0.973568281938326}

Framework versions

• PEFT 0.11.1
• Transformers 4.42.1
• Pytorch 2.0.1+cpu
• Datasets 2.20.0
• Tokenizers 0.19.1