---
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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# vihealthbert-w_dual-ViNLI

This model is a fine-tuned version of [demdecuong/vihealthbert-base-word](https://huggingface.co/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