metadata
license: mit
base_model: microsoft/phi-2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: phi_2_ledgar
results: []
phi_2_ledgar
This model is a fine-tuned version of microsoft/phi-2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6120
- Accuracy: 0.826
- F1 Macro: 0.7355
- F1 Micro: 0.826
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: 5e-06
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Micro |
---|---|---|---|---|---|---|
3.6034 | 0.11 | 100 | 3.2114 | 0.337 | 0.1236 | 0.337 |
2.2678 | 0.21 | 200 | 1.9837 | 0.5623 | 0.3331 | 0.5623 |
1.4927 | 0.32 | 300 | 1.3369 | 0.6712 | 0.4884 | 0.6712 |
1.1518 | 0.43 | 400 | 1.0526 | 0.7243 | 0.5613 | 0.7243 |
1.1041 | 0.53 | 500 | 0.9305 | 0.7521 | 0.6206 | 0.7521 |
1.0144 | 0.64 | 600 | 0.9068 | 0.7574 | 0.6294 | 0.7574 |
0.9892 | 0.75 | 700 | 0.8712 | 0.7669 | 0.6430 | 0.7669 |
0.9972 | 0.85 | 800 | 0.8591 | 0.7675 | 0.6369 | 0.7675 |
0.8439 | 0.96 | 900 | 0.7895 | 0.7848 | 0.6835 | 0.7848 |
0.7409 | 1.07 | 1000 | 0.7614 | 0.7944 | 0.6809 | 0.7944 |
0.7627 | 1.17 | 1100 | 0.7539 | 0.7946 | 0.6810 | 0.7946 |
0.8065 | 1.28 | 1200 | 0.7289 | 0.8008 | 0.6945 | 0.8008 |
0.7359 | 1.39 | 1300 | 0.7254 | 0.8034 | 0.6976 | 0.8034 |
0.6525 | 1.49 | 1400 | 0.7073 | 0.8065 | 0.7050 | 0.8065 |
0.7359 | 1.6 | 1500 | 0.7206 | 0.8033 | 0.6949 | 0.8033 |
0.7291 | 1.71 | 1600 | 0.6924 | 0.8089 | 0.7066 | 0.8089 |
0.7072 | 1.81 | 1700 | 0.6764 | 0.8102 | 0.7070 | 0.8102 |
0.6688 | 1.92 | 1800 | 0.6546 | 0.814 | 0.7128 | 0.814 |
0.6253 | 2.03 | 1900 | 0.6506 | 0.8158 | 0.7059 | 0.8158 |
0.6044 | 2.13 | 2000 | 0.6603 | 0.8155 | 0.7165 | 0.8155 |
0.6414 | 2.24 | 2100 | 0.6435 | 0.8138 | 0.7185 | 0.8138 |
0.6115 | 2.35 | 2200 | 0.6368 | 0.8216 | 0.7280 | 0.8216 |
0.6331 | 2.45 | 2300 | 0.6273 | 0.8208 | 0.7251 | 0.8208 |
0.608 | 2.56 | 2400 | 0.6252 | 0.8232 | 0.7286 | 0.8232 |
0.5879 | 2.67 | 2500 | 0.6172 | 0.8241 | 0.7308 | 0.8241 |
0.6056 | 2.77 | 2600 | 0.6157 | 0.8257 | 0.7346 | 0.8257 |
0.5711 | 2.88 | 2700 | 0.6129 | 0.8253 | 0.7341 | 0.8253 |
0.5802 | 2.99 | 2800 | 0.6120 | 0.826 | 0.7355 | 0.826 |
Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2