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timi-domain-classification-undersampling-focal-loss-v3
This model is a fine-tuned version of vinai/phobert-base-v2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0489
- Precision: 0.8987
- F1: 0.8793
- Accuracy: 0.9400
- Recall: 0.8608
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: 64
- 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | F1 | Accuracy | Recall |
|---|---|---|---|---|---|---|---|
| 0.0255 | 1.0 | 460 | 0.0303 | 0.8140 | 0.8225 | 0.9089 | 0.8312 |
| 0.0109 | 2.0 | 920 | 0.0262 | 0.8327 | 0.8566 | 0.9250 | 0.8819 |
| 0.0047 | 3.0 | 1380 | 0.0584 | 0.9460 | 0.8528 | 0.9319 | 0.7764 |
| 0.002 | 4.0 | 1840 | 0.0385 | 0.842 | 0.8645 | 0.9293 | 0.8882 |
| 0.0009 | 5.0 | 2300 | 0.0489 | 0.8987 | 0.8793 | 0.9400 | 0.8608 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
