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---
language:
- en
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- generated_from_trainer
datasets:
- tmnam20/VieGLUE
metrics:
- accuracy
model-index:
- name: bert-base-multilingual-cased-qnli-100
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tmnam20/VieGLUE/QNLI
type: tmnam20/VieGLUE
config: qnli
split: validation
args: qnli
metrics:
- name: Accuracy
type: accuracy
value: 0.8885227896760022
---
<!-- 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. -->
# bert-base-multilingual-cased-qnli-100
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co./bert-base-multilingual-cased) on the tmnam20/VieGLUE/QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3284
- Accuracy: 0.8885
## 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 100
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4041 | 0.15 | 500 | 0.3611 | 0.8488 |
| 0.3784 | 0.31 | 1000 | 0.3232 | 0.8603 |
| 0.364 | 0.46 | 1500 | 0.3128 | 0.8642 |
| 0.364 | 0.61 | 2000 | 0.3020 | 0.8702 |
| 0.3236 | 0.76 | 2500 | 0.2960 | 0.8768 |
| 0.3475 | 0.92 | 3000 | 0.2895 | 0.8816 |
| 0.252 | 1.07 | 3500 | 0.3019 | 0.8812 |
| 0.261 | 1.22 | 4000 | 0.2783 | 0.8893 |
| 0.2718 | 1.37 | 4500 | 0.2880 | 0.8832 |
| 0.2407 | 1.53 | 5000 | 0.3017 | 0.8812 |
| 0.254 | 1.68 | 5500 | 0.2775 | 0.8827 |
| 0.2611 | 1.83 | 6000 | 0.2837 | 0.8812 |
| 0.257 | 1.99 | 6500 | 0.2816 | 0.8852 |
| 0.1645 | 2.14 | 7000 | 0.3323 | 0.8845 |
| 0.1679 | 2.29 | 7500 | 0.3568 | 0.8825 |
| 0.1643 | 2.44 | 8000 | 0.3203 | 0.8889 |
| 0.1662 | 2.6 | 8500 | 0.3240 | 0.8878 |
| 0.1558 | 2.75 | 9000 | 0.3302 | 0.8856 |
| 0.1614 | 2.9 | 9500 | 0.3299 | 0.8872 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.2.0.dev20231203+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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