metadata
library_name: transformers
license: mit
base_model: vinai/phobert-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: phobert-large_nli
results: []
phobert-large_nli
This model is a fine-tuned version of vinai/phobert-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.3062
- Accuracy: 0.8102
- Precision Macro: 0.8106
- Recall Macro: 0.8103
- F1 Macro: 0.8103
- F1 Weighted: 0.8103
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-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Macro | Recall Macro | F1 Macro | F1 Weighted |
|---|---|---|---|---|---|---|---|---|
| 1.0976 | 1.0 | 72 | 1.0257 | 0.5237 | 0.5529 | 0.5264 | 0.5082 | 0.5072 |
| 0.9271 | 2.0 | 144 | 0.6649 | 0.7592 | 0.7887 | 0.7579 | 0.7590 | 0.7590 |
| 0.4037 | 3.0 | 216 | 0.5864 | 0.7894 | 0.7930 | 0.7895 | 0.7895 | 0.7895 |
| 0.2866 | 4.0 | 288 | 0.6385 | 0.8120 | 0.8142 | 0.8125 | 0.8118 | 0.8118 |
| 0.1197 | 5.0 | 360 | 0.6949 | 0.8115 | 0.8117 | 0.8115 | 0.8115 | 0.8115 |
| 0.0939 | 6.0 | 432 | 0.7485 | 0.8058 | 0.8084 | 0.8060 | 0.8058 | 0.8059 |
| 0.0647 | 7.0 | 504 | 0.9244 | 0.7920 | 0.7977 | 0.7921 | 0.7919 | 0.7918 |
| 0.0457 | 8.0 | 576 | 0.8464 | 0.8106 | 0.8107 | 0.8107 | 0.8106 | 0.8106 |
| 0.046 | 9.0 | 648 | 0.9886 | 0.8062 | 0.8121 | 0.8066 | 0.8064 | 0.8063 |
| 0.026 | 10.0 | 720 | 0.9887 | 0.8120 | 0.8126 | 0.8121 | 0.8120 | 0.8121 |
| 0.0244 | 11.0 | 792 | 1.0642 | 0.8124 | 0.8130 | 0.8126 | 0.8125 | 0.8125 |
| 0.0211 | 12.0 | 864 | 1.0197 | 0.8075 | 0.8097 | 0.8078 | 0.8077 | 0.8077 |
| 0.0146 | 13.0 | 936 | 1.1487 | 0.8151 | 0.8171 | 0.8155 | 0.8151 | 0.8151 |
| 0.0085 | 14.0 | 1008 | 1.1846 | 0.8053 | 0.8056 | 0.8053 | 0.8053 | 0.8053 |
| 0.0051 | 15.0 | 1080 | 1.2905 | 0.8084 | 0.8095 | 0.8085 | 0.8084 | 0.8084 |
| 0.0036 | 16.0 | 1152 | 1.3259 | 0.8102 | 0.8121 | 0.8104 | 0.8104 | 0.8104 |
| 0.0027 | 17.0 | 1224 | 1.3187 | 0.8115 | 0.8121 | 0.8115 | 0.8116 | 0.8116 |
| 0.0023 | 18.0 | 1296 | 1.3024 | 0.8115 | 0.8120 | 0.8117 | 0.8116 | 0.8116 |
| 0.0025 | 19.0 | 1368 | 1.3049 | 0.8111 | 0.8115 | 0.8112 | 0.8111 | 0.8111 |
| 0.0037 | 20.0 | 1440 | 1.3062 | 0.8102 | 0.8106 | 0.8103 | 0.8103 | 0.8103 |
Framework versions
- Transformers 4.55.0
- Pytorch 2.7.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4