tradepulse-finbert-yiyanghkust-finbert-tone-20250707_125257

This model is a fine-tuned version of yiyanghkust/finbert-tone on the None dataset. It achieves the following results on the evaluation set:

• Loss: 10.5009
• Accuracy: 0.1667
• Precision: 0.3333
• Recall: 0.1667
• F1: 0.2222

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: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
No log	1.0	2	10.5580	0.1667	0.3333	0.1667	0.2222
No log	2.0	4	10.5380	0.1667	0.3333	0.1667	0.2222
No log	3.0	6	10.5009	0.1667	0.3333	0.1667	0.2222

Framework versions

• Transformers 4.41.0
• Pytorch 2.7.1+cpu
• Datasets 2.19.1
• Tokenizers 0.19.1