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---
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
should probably proofread and complete it, then remove this comment. -->
# 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
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