---
base_model: demdecuong/vihealthbert-base-word
tags:
- generated_from_trainer
datasets:
- tmnam20/pretrained-vn-med-nli
metrics:
- accuracy
model-index:
- name: vihealthbert-w_unsup-SynPD
  results:
  - task:
      name: Masked Language Modeling
      type: fill-mask
    dataset:
      name: tmnam20/pretrained-vn-med-nli all
      type: tmnam20/pretrained-vn-med-nli
      args: all
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6891028971951825
---

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

This model is a fine-tuned version of [demdecuong/vihealthbert-base-word](https://huggingface.co/demdecuong/vihealthbert-base-word) on the tmnam20/pretrained-vn-med-nli all dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5579
- Accuracy: 0.6891

## 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: 19144
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|
| 5.8543        | 0.3446 | 2000  | 3.8967          | 0.3950   |
| 3.4544        | 0.6893 | 4000  | 2.8119          | 0.5306   |
| 2.8312        | 1.0339 | 6000  | 2.4040          | 0.5771   |
| 2.5914        | 1.3786 | 8000  | 2.6482          | 0.5350   |
| 2.5649        | 1.7232 | 10000 | 2.1335          | 0.6087   |
| 2.2749        | 2.0679 | 12000 | 1.9895          | 0.6282   |
| 2.1572        | 2.4125 | 14000 | 1.9313          | 0.6353   |
| 2.1009        | 2.7572 | 16000 | 1.8646          | 0.6429   |
| 2.0609        | 3.1018 | 18000 | 1.8572          | 0.6450   |
| 2.0885        | 3.4465 | 20000 | 1.9489          | 0.6285   |
| 1.9891        | 3.7911 | 22000 | 1.7700          | 0.6583   |
| 1.9368        | 4.1358 | 24000 | 1.7398          | 0.6609   |
| 1.9003        | 4.4804 | 26000 | 1.7165          | 0.6664   |
| 1.9058        | 4.8251 | 28000 | 1.7032          | 0.6670   |
| 1.859         | 5.1697 | 30000 | 1.6771          | 0.6718   |
| 1.8401        | 5.5144 | 32000 | 1.6652          | 0.6710   |
| 1.8181        | 5.8590 | 34000 | 1.6417          | 0.6754   |
| 1.8133        | 6.2037 | 36000 | 1.6431          | 0.6748   |
| 1.7888        | 6.5483 | 38000 | 1.6363          | 0.6755   |
| 1.7811        | 6.8930 | 40000 | 1.6205          | 0.6793   |
| 1.7481        | 7.2376 | 42000 | 1.6190          | 0.6807   |
| 1.7509        | 7.5823 | 44000 | 1.6142          | 0.6794   |
| 1.7517        | 7.9269 | 46000 | 1.5949          | 0.6819   |
| 1.7358        | 8.2716 | 48000 | 1.5909          | 0.6843   |
| 1.7287        | 8.6162 | 50000 | 1.5757          | 0.6851   |
| 1.7132        | 8.9609 | 52000 | 1.5671          | 0.6885   |
| 1.7042        | 9.3055 | 54000 | 1.5685          | 0.6867   |
| 1.7051        | 9.6502 | 56000 | 1.5609          | 0.6876   |
| 1.7051        | 9.9948 | 58000 | 1.5576          | 0.6883   |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.2.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1