library_name: peft
license: mit
base_model: microsoft/phi-2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: phi-2-heart
results: []
phi-2-heart
This model is a fine-tuned version of microsoft/phi-2 on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.6017
Accuracy: 0.6667
Report: precision recall f1-score support
absence 0.63 0.98 0.77 45 presence 0.91 0.28 0.43 36
accuracy 0.67 81 macro avg 0.77 0.63 0.60 81
weighted avg 0.75 0.67 0.61 81
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Report |
|---|---|---|---|---|---|
| No log | 1.0 | 48 | 0.7215 | 0.5556 | precision recall f1-score support |
absence 0.56 1.00 0.71 45
presence 0.00 0.00 0.00 36
accuracy 0.56 81
macro avg 0.28 0.50 0.36 81 weighted avg 0.31 0.56 0.40 81 | | No log | 2.0 | 96 | 0.6567 | 0.5802 | precision recall f1-score support
absence 0.57 1.00 0.73 45
presence 1.00 0.06 0.11 36
accuracy 0.58 81
macro avg 0.78 0.53 0.42 81 weighted avg 0.76 0.58 0.45 81 | | No log | 3.0 | 144 | 0.6312 | 0.6667 | precision recall f1-score support
absence 0.63 0.96 0.76 45
presence 0.85 0.31 0.45 36
accuracy 0.67 81
macro avg 0.74 0.63 0.61 81 weighted avg 0.73 0.67 0.62 81 | | No log | 4.0 | 192 | 0.6253 | 0.5926 | precision recall f1-score support
absence 0.58 1.00 0.73 45
presence 1.00 0.08 0.15 36
accuracy 0.59 81
macro avg 0.79 0.54 0.44 81 weighted avg 0.76 0.59 0.47 81 | | No log | 5.0 | 240 | 0.6017 | 0.6667 | precision recall f1-score support
absence 0.63 0.98 0.77 45
presence 0.91 0.28 0.43 36
accuracy 0.67 81
macro avg 0.77 0.63 0.60 81 weighted avg 0.75 0.67 0.61 81 |
Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1