metadata
library_name: transformers
base_model: ProsusAI/finbert
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: macro-sentiment-finbert
results: []
macro-sentiment-finbert
This model is a fine-tuned version of ProsusAI/finbert on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3450
- Accuracy: 0.8486
- F1 Macro: 0.8325
- F1 Weighted: 0.8515
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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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: cosine
- lr_scheduler_warmup_steps: 31
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Weighted |
|---|---|---|---|---|---|---|
| 1.6807 | 1.0 | 314 | 0.3761 | 0.8246 | 0.8058 | 0.8300 |
| 1.1679 | 2.0 | 628 | 0.3450 | 0.8486 | 0.8325 | 0.8515 |
Framework versions
- Transformers 5.6.2
- Pytorch 2.11.0+cu130
- Datasets 4.8.4
- Tokenizers 0.22.2