metadata
base_model: demdecuong/vihealthbert-base-word
tags:
- generated_from_trainer
datasets:
- tmnam20/ViNLI
metrics:
- accuracy
model-index:
- name: vihealthbert-w_dual-ViNLI
results:
- task:
name: Masked Language Modeling
type: fill-mask
dataset:
name: tmnam20/ViNLI
type: tmnam20/ViNLI
metrics:
- name: Accuracy
type: accuracy
value: 0.5919165580182529
vihealthbert-w_dual-ViNLI
This model is a fine-tuned version of demdecuong/vihealthbert-base-word on the tmnam20/ViNLI dataset. It achieves the following results on the evaluation set:
- Loss: 2.6042
- Accuracy: 0.5919
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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 30000
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 5.8126 | 15.625 | 1000 | 3.5461 | 0.4450 |
| 2.605 | 31.25 | 2000 | 2.7789 | 0.5404 |
| 1.5924 | 46.875 | 3000 | 2.5432 | 0.5809 |
| 1.2233 | 62.5 | 4000 | 2.6662 | 0.5567 |
| 0.9236 | 78.125 | 5000 | 2.4691 | 0.5927 |
| 0.7193 | 93.75 | 6000 | 2.4053 | 0.6027 |
| 0.6259 | 109.375 | 7000 | 2.5938 | 0.5782 |
| 0.5082 | 125.0 | 8000 | 2.4809 | 0.6137 |
| 0.4438 | 140.625 | 9000 | 2.7056 | 0.5819 |
| 0.4075 | 156.25 | 10000 | 2.6501 | 0.5946 |
| 0.3571 | 171.875 | 11000 | 2.5337 | 0.6082 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.0.1+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1