metadata
library_name: peft
base_model: ProsusAI/finbert
tags:
- base_model:adapter:ProsusAI/finbert
- lora
- transformers
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: finbert_lora
results: []
finbert_lora
This model is a fine-tuned version of ProsusAI/finbert on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3124
- Accuracy: 0.8630
- Precision: 0.8480
- Recall: 0.8562
- F1: 0.8521
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: 0.0002
- train_batch_size: 32
- eval_batch_size: 32
- 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: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.3374 | 1.0 | 5013 | 0.3304 | 0.8518 | 0.8172 | 0.8739 | 0.8446 |
| 0.3287 | 2.0 | 10026 | 0.3101 | 0.8633 | 0.8458 | 0.8603 | 0.8530 |
Framework versions
- PEFT 0.18.0
- Transformers 4.57.3
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.2