NLI-Lora-Fine-Tuning-10K-ALBERTA
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: 0.8439
- Accuracy: 0.6063
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
No log | 1.0 | 312 | 1.0562 | 0.4551 |
1.0762 | 2.0 | 624 | 1.0236 | 0.4995 |
1.0762 | 3.0 | 936 | 0.9603 | 0.5361 |
1.0075 | 4.0 | 1248 | 0.9053 | 0.5671 |
0.9178 | 5.0 | 1560 | 0.8796 | 0.5823 |
0.9178 | 6.0 | 1872 | 0.8649 | 0.5934 |
0.8859 | 7.0 | 2184 | 0.8551 | 0.5977 |
0.8859 | 8.0 | 2496 | 0.8488 | 0.6033 |
0.8632 | 9.0 | 2808 | 0.8450 | 0.6057 |
0.8543 | 10.0 | 3120 | 0.8439 | 0.6063 |
Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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