| | --- |
| | library_name: transformers |
| | base_model: ProsusAI/finbert |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | - f1 |
| | model-index: |
| | - name: finance-sentiment-classifier |
| | results: [] |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # finance-sentiment-classifier |
| |
|
| | This model is a fine-tuned version of [ProsusAI/finbert](https://huggingface.co/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 |
| |
|
