finance-sentiment-classifier

This model is a fine-tuned version of ProsusAI/finbert on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.3186
• Accuracy: 0.9203
• F1: 0.9202

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: 32
• eval_batch_size: 64
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 3
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.6005	0.1999	500	0.5220	0.7970	0.7954
0.4518	0.3998	1000	0.3991	0.8546	0.8540
0.3773	0.5998	1500	0.3231	0.8813	0.8808
0.3375	0.7997	2000	0.3025	0.8878	0.8881
0.2982	0.9996	2500	0.2735	0.8977	0.8971
0.2057	1.1995	3000	0.3028	0.9035	0.9026
0.2001	1.3994	3500	0.2624	0.9123	0.9119
0.2034	1.5994	4000	0.2450	0.9171	0.9170
0.1631	1.7993	4500	0.2547	0.9180	0.9179
0.1691	1.9992	5000	0.2379	0.9189	0.9189
0.1077	2.1991	5500	0.2982	0.9190	0.9186
0.1288	2.3990	6000	0.2772	0.9201	0.9199
0.1114	2.5990	6500	0.2847	0.9220	0.9220
0.1128	2.7989	7000	0.2879	0.9234	0.9233
0.0969	2.9988	7500	0.2920	0.9236	0.9234

Framework versions

• Transformers 4.57.6
• Pytorch 2.9.1+cu128
• Datasets 4.5.0
• Tokenizers 0.22.2