|
--- |
|
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 |
|
|
