results_lora
This model is a fine-tuned version of albert/albert-base-v2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2590
- Accuracy: 0.9014
- F1: 0.9044
- Precision: 0.8925
- Recall: 0.9167
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: 3e-05
- train_batch_size: 16
- eval_batch_size: 64
- 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 | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.3298 | 1.0 | 4210 | 0.2669 | 0.8922 | 0.8934 | 0.8995 | 0.8874 |
| 0.236 | 2.0 | 8420 | 0.2702 | 0.9048 | 0.9089 | 0.8865 | 0.9324 |
| 0.1772 | 3.0 | 12630 | 0.2590 | 0.9014 | 0.9044 | 0.8925 | 0.9167 |
Framework versions
- PEFT 0.13.2
- Transformers 4.45.2
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
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Model tree for asm3515/alberta-sst2-lora
Base model
albert/albert-base-v2